Mapping biomedical hydrogel microspheres through scientometric and topic modeling analysis of emerging nano-enabled design strategies

Siyang Cao; Deren Lan; Yihao Wei; Qiuyu He; Ruijie Chen; Guoqing Li; Mengjie Dong; Yongsheng Zhao; Hua Zhang

doi:10.1186/s11671-026-04979-8

. 2026 Oct 12;21(1):503. doi: 10.1186/s11671-026-04979-8

Mapping biomedical hydrogel microspheres through scientometric and topic modeling analysis of emerging nano-enabled design strategies

Siyang Cao ^1,^#, Deren Lan ^2,^#, Yihao Wei ^3,^4,^5,^#, Qiuyu He ¹, Ruijie Chen ⁶, Guoqing Li ^7,^✉, Mengjie Dong ^2,^✉, Yongsheng Zhao ^2,^✉, Hua Zhang ^1,^✉

PMCID: PMC13658721 PMID: 42862440

Abstract

Hydrogel microspheres (HMs) are injectable microscale hydrogel platforms formed from crosslinked polymeric networks, with tunable porosity, stiffness, degradation behavior, surface chemistry, and cargo-loading capacity. Recent advances have expanded HMs beyond conventional controlled-release carriers into multifunctional biomedical platforms incorporating micro/nano-architectural designs, nanocomposite components, and biofunctional interfaces. However, the field-level evolution of biomedical HMs, including emerging nano-enabled design strategies, application landscapes, and validation requirements, remains insufficiently characterized. Here, we analyzed 4,399 English-language publications on biomedical HMs from the Web of Science Core Collection and PubMed between 2000 and 2025. Bibliometric mapping, co-citation analysis, keyword evolution analysis, disease/application-landscape mapping, and latent Dirichlet allocation topic modeling were integrated to identify publication growth, collaboration structures, intellectual bases, latent topics, and emerging material trends. The results show that controlled release and drug delivery remain foundational themes, whereas recent attention has increasingly focused on engineered HM platforms incorporating micro/nano-architectural features for wound healing, tissue engineering/cell delivery, bone regeneration, osteoarthritis/cartilage repair, and intervertebral disc degeneration. This study provides a data-driven map linking HM fabrication strategies, emerging micro/nano-architectural design approaches, material properties, biomedical functions, and translational validation requirements.

Clinical Trial Number: Not applicable.

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Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s11671-026-04979-8.

Keywords: Hydrogel microspheres, Bibliometrics, Topic modeling, Drug delivery, Regenerative medicine

Introduction

Hydrogels are three-dimensional hydrophilic polymer networks capable of absorbing and retaining large amounts of water while maintaining soft, tissue-like properties [1]. Owing to their biocompatibility, tunable physicochemical properties, and ability to mimic aspects of the extracellular matrix (ECM), hydrogels have been widely investigated for drug delivery, cell culture, tissue engineering, wound repair, and regenerative medicine [2, 3]. However, conventional bulk hydrogels also have limitations. Their macroscopic dimensions and sometimes irregular geometry may restrict minimally invasive administration, hinder uniform distribution within irregular defects, and impair nutrient diffusion or spatial cell organization [4]. To address these limitations, hydrogel microspheres (HMs) have emerged as microengineered hydrogel systems with improved injectability, spatial distribution, and functional adaptability [5].

HMs are generally defined as micron-scale spherical hydrogel particles formed through physical or chemical crosslinking of hydrophilic or amphiphilic polymer networks [6]. Compared with bulk hydrogels, they offer several structural and functional advantages, including a high surface-area-to-volume ratio, tunable porosity, adjustable mechanical properties, controllable degradation, and capacity for multifunctional modification [7]. These features enable HMs to act not only as drug carriers, but also as cell delivery vehicles, tissue-engineering scaffolds, bioactive microenvironments, and modular building blocks for complex biomedical systems [8]. Their compositions may include natural, synthetic, or composite polymers, while fabrication strategies such as emulsion, microfluidics, electrospray, lithography, and three-dimensional (3D) printing allow regulation of size, morphology, and function [9, 10]. Their biomedical performance is closely linked to polymer composition, crosslinking chemistry, network architecture, particle size, porosity, stiffness, degradation behavior, surface functionality, and stimuli responsiveness [11]. Recent advances have expanded HMs from passive delivery carriers to responsive and biofunctional polymeric microgel platforms [12]. Porous, core–shell, multicompartmental, cell-laden, nanoparticle-integrated, and stimuli-responsive microspheres have been developed to support controlled release, immune modulation, tissue regeneration, lubrication, and disease modeling [13]. Accordingly, HMs have been explored in diverse biomedical settings, including drug delivery, cell therapy, regenerative medicine, wound healing, cancer-related therapy, organ-on-a-chip systems, and musculoskeletal repair [7, 14, 15]. Beyond their roles as polymeric biomaterial platforms, HMs are also relevant to pharmaceutical development because their performance depends on reliable control and characterization of particle size, morphology, polymer composition, loading and encapsulation efficiency, release kinetics, degradation behavior, mechanical properties, storage stability, and batch-to-batch consistency. The pharmaceutical relevance of these parameters is also evident in drug-loaded hydrogel systems, in which formulation characteristics such as drug-loading capacity, release behavior, rheological properties, and storage stability are closely evaluated alongside therapeutic performance [16]. A conceptual overview of representative HM fabrication strategies and pharmaceutical/biomedical application contexts is provided in Fig. 1. From a nanotechnology perspective, some HM systems can be considered hierarchical micro/nano-structured platforms, where microscale particle geometry is combined with nanoscale features such as polymer-network mesh, nanoporous architecture, nanoengineered surfaces, nanocomposite integration, or nanobiointerface interactions [17]. In this context, the term “nano-enabled” does not indicate that all retrieved HM studies were explicitly designed as nanotechnology-based systems. Rather, it refers to the integration of nanoscale structural features, nanomaterials, nano-biointerfaces, or nanoscale functional mechanisms that enhance the biomedical performance of HM platforms. Therefore, in the present study, nano-enabled HM represents an emerging design direction within the broader biomedical HM research landscape rather than a strict retrieval criterion defined by the occurrence of the keyword “nano”.

Fig. 1 — Conceptual Background of HM Fabrication Strategies and Pharmaceutical/Biomedical Applications. This schematic provides a conceptual overview of representative fabrication strategies and pharmaceutical/biomedical application contexts of HMs. Common fabrication approaches include emulsification, microfluidics, electrohydrodynamic spraying, lithography, and three-dimensional printing. As background to the subsequent bibliometric and topic modeling analyses, the figure summarizes major application contexts in which HMs have been investigated, including controlled release, localized drug delivery, long-acting delivery, thermosensitive delivery, bioactive-factor delivery, tissue engineering, wound repair, regenerative applications, and tumor-related therapy. HMs, hydrogel microspheres. Created with Figdraw (https://www.figdraw.com/static/index.html#/)

A growing number of qualitative syntheses have summarized HMs from different perspectives, including material design, fabrication methods, biological functions, smart responsiveness, tissue regeneration, osteoarthritis (OA) therapy, organ-on-a-chip integration, and broad biomedical applications [7, 12, 15, 18–24]. These prior syntheses have provided valuable mechanistic and application-oriented insights. Nevertheless, most are qualitative syntheses focused on specific materials, diseases, or application scenarios. A comprehensive understanding of the field’s global development, collaboration patterns, disciplinary structure, intellectual base, emerging research fronts, disease landscape, and latent thematic evolution remains limited. Moreover, the terminology in this area is heterogeneous, with overlapping concepts such as HMs, hydrogel microparticles, microgels, hydrogel beads, injectable microspheres, and responsive hydrogel carriers [25]. This complexity makes it difficult to capture the overall knowledge structure of the field using qualitative synthesis alone.

Bibliometric analysis provides a useful framework for addressing this gap by quantitatively mapping publication growth, collaboration networks, citation relationships, co-citation structures, keyword evolution, and emerging hotspots [26–30]. Previous bibliometric studies have already examined hydrogel research at broader or disease-specific levels. For example, Sun et al. systematically mapped biomedical applications of hydrogels using CiteSpace and characterized publication patterns, collaboration structures, and evolving research hotspots, whereas Han et al. focused specifically on hydrogel applications in spinal cord injury using multiple bibliometric tools to delineate the knowledge structure and emerging directions of this disease-specific field [31, 32]. However, these studies examined either hydrogels as a broad material category or their application within a specific disease context, rather than HMs as a distinct microscale biomaterial platform across biomedical applications.

Conventional bibliometric methods also rely primarily on explicit metadata and may not fully capture latent semantic themes embedded in article titles, abstracts, and author keywords. Latent Dirichlet allocation (LDA) topic modeling can complement bibliometric mapping by identifying probabilistic topic structures and tracing their temporal evolution across large-scale text corpora [33]. To our knowledge, no previous study has systematically mapped biomedical HMs as a distinct research domain while integrating conventional scientometric analysis with LDA-based topic modeling. Accordingly, the present study combines dual-database retrieval, bibliometric and knowledge-network analysis, disease/application-landscape mapping, and LDA topic modeling to characterize the publication trajectory, intellectual structure, latent themes, and evolving design and application patterns of biomedical HMs from 2000 to 2025. Within this broader HM landscape, emerging nano-enabled strategies are further interpreted as an evolving design direction rather than as a defining feature of all HMs.

Methods

This study was designed as a scientometric and topic modeling analysis based on bibliographic records retrieved from the Web of Science Core Collection (WoSCC) and PubMed.

Data sources and retrieval strategy

The data analyzed in this study were retrieved from two major databases, namely the WoSCC and PubMed, to improve coverage of the literature on HMs in biomedical applications. The WoSCC search was conducted in the Science Citation Index Expanded, and the PubMed search was performed using Medical Subject Headings (MeSH) and title/abstract terms. In WoSCC, the Topic field (TS) was used for retrieval, which searches titles, abstracts, author keywords, and Keywords Plus. In PubMed, controlled vocabulary terms from MeSH and free-text terms in titles and abstracts were combined to improve retrieval sensitivity. Publication-type filtering differed between the two databases at the retrieval stage. The WoSCC search did not exclude review articles, whereas the PubMed search excluded records indexed as reviews using the NOT (“Review“[Publication Type]) filter. The publication time span was restricted to January 1, 2000, to December 31, 2025. The searches were performed on December 31, 2025. The search strategy was designed to capture publications related to HMs and their biomedical applications, including but not limited to drug delivery, tissue engineering, diagnosis, imaging, cancer therapy, antimicrobial treatment, regeneration, and biosensing. Because the objective of this study was to characterize the overall evolution of biomedical HM research rather than exclusively identify studies containing the term “nano”, nano-enabled characteristics were not used as a mandatory retrieval criterion. Instead, nano-enabled HM strategies were interpreted as emerging design features within the broader biomedical HM literature. These features include the incorporation of nanoscale structural components, nanomaterials, nano-biointerfaces, or nanoscale functional mechanisms that enhance the biomedical performance of HM platforms. This approach was adopted because nano-enabled characteristics are not always explicitly described using the keyword “nano” in titles or abstracts. The search strategy also included terms related to diagnosis, bioimaging, theranostics, biosensing, bioassay, and biomarkers to capture diagnostic, therapeutic, and application-oriented biomedical uses of HMs. Records retrieved from WoSCC and PubMed were exported in plain-text or comma-separated values (CSV) formats and merged before deduplication. Duplicate records were identified primarily by digital object identifier and PubMed identifier, and records lacking these identifiers were further checked using title, author information, and publication year, followed by manual verification. The remaining records were screened according to the predefined inclusion and exclusion criteria based on titles, abstracts, and full texts when necessary. Uncertain records were resolved through discussion among the investigators.

To enhance search reproducibility, the complete database-specific search strategies, including field tags, Boolean operators, and search terms, are provided in Supplementary Tables S1 and S2. All searches were performed on December 31, 2025, and the retrieval date was recorded because database indexing and record availability may change over time. The retrieved records were exported directly from WoSCC and PubMed in machine-readable formats (plain text or CSV) and retained before subsequent processing. The final dataset used for analysis was generated after duplicate removal and eligibility screening according to predefined inclusion and exclusion criteria. The dataset construction and quality-control procedures are summarized in Supplementary Table S3.

To broaden literature coverage, the search was performed using two databases (WoSCC and PubMed) with complementary indexing systems. Database-specific controlled vocabulary and free-text search terms were combined to improve retrieval across heterogeneous terminology. However, the publication-type filtering differed between the two databases, as described above, and this database-specific difference was considered when interpreting the integrated dataset.

Inclusion and exclusion criteria

Publication types eligible for the final integrated dataset included English-language original articles and review articles focusing on HMs and their biomedical applications, provided that sufficient bibliographic and textual information was available for bibliometric and topic modeling analyses. However, at the database-retrieval stage, PubMed records indexed as reviews were excluded by the predefined PubMed search filter, whereas the WoSCC search did not apply an equivalent review exclusion. Studies were excluded if they: (a) addressed non-biomedical applications of HMs, such as industrial or environmental uses; (b) were non-peer-reviewed publications, including dissertations, letters, commentaries, editorials, meeting abstracts, conference abstracts, or proceedings papers; (c) lacked sufficient bibliographic metadata; or (d) lacked abstracts required for LDA topic modeling. Study eligibility was determined according to titles, abstracts, and full texts when necessary.

Data analysis

Bibliometric and visual analyses were conducted using multiple software tools. After deduplication, the merged WoSCC–PubMed dataset was used for descriptive analyses, publication trend analysis, journal distribution analysis, keyword mapping, disease-term analysis, and LDA topic modeling. Citation-based analyses, including reference co-citation networks and citation burst detection, were primarily performed using WoSCC records because WoSCC provides more complete cited-reference metadata. Network analyses of countries/regions, institutions, authors, journals, keywords, and diseases were performed according to the availability and completeness of the corresponding metadata in the cleaned dataset.

For descriptive characterization of cumulative publication growth, a quadratic regression model was fitted to the annual cumulative publication counts from 2000 to 2025. A standard logistic growth model was also explored as an alternative because logistic curves are commonly used to describe maturation and saturation processes. However, the observed publication series showed no clear deceleration or plateau within the study period, and the logistic fit did not yield a stable estimate of the saturation level. The quadratic model was therefore retained as a descriptive representation of the observed growth trajectory and was not used for long-term forecasting.

Publication types were extracted from the bibliographic records and considered during dataset characterization. Both original articles and review articles present in the final merged dataset were retained because the primary objective was to characterize the overall landscape and thematic evolution of biomedical HM research. However, because PubMed records indexed as reviews were excluded at the retrieval stage, the representation of review articles was not fully symmetric between the two database sources. Publication-type composition was therefore considered when interpreting the bibliometric findings, as original articles and review articles may differ in citation behavior and thematic representation.

Chord diagrams of international collaboration and country co-occurrence networks were generated using the chorddiag package in R, together with VOSviewer 1.6.18 (Centre for Science and Technology Studies, Leiden University, Leiden, the Netherlands). VOSviewer 1.6.18 and Pajek (version 5.16; University of Ljubljana, Ljubljana, Slovenia) were further used to analyze and visualize the co-occurrence patterns of institutions, journals, research fields, keywords, and diseases. Before keyword co-occurrence, temporal, and burst analyses, keyword terms were normalized using a conservative manual procedure. Terms were merged only when they represented the same concept and differed solely by capitalization, singular/plural form, hyphenation or spacing, established abbreviation versus full form, or unequivocal spelling or word-order variants. Semantically related but non-identical terms were retained separately to minimize subjective over-merging. Detailed normalization and synonym-merging rules are provided in Supplementary Note S1. The normalized keyword set was used for both VOSviewer co-occurrence mapping and CiteSpace keyword analyses. In these network maps, nodes represented individual items, node size reflected the frequency of occurrence, and links between nodes indicated co-occurrence relationships, with thicker lines representing stronger associations. In journal density maps, the area and intensity of red regions were positively associated with publication activity, whereas in disease density maps, the area and intensity of yellow regions reflected the frequency of disease occurrence.

CiteSpace (version 6.3.R1; Chaomei Chen) was used to perform visual analyses of countries, institutions, authors, journals, co-cited references, and keywords, and to generate the corresponding knowledge maps. Citation burst detection was performed to identify the top 10 burst items for countries, institutions, authors, keywords, and references. For the author collaboration network, CiteSpace was set as follows: time slicing = 2000–2025, years per slice = 1, and selection criteria = k = 30. For the co-cited reference network, the parameters were time slicing = 2000–2025, years per slice = 1, and selection criteria = k = 8. For the keyword timeline clustering analysis, the parameters were time slicing = 2000–2025, years per slice = 1, and selection criteria = k = 7.

In CiteSpace visualizations, nodes represented the analyzed entities, node size reflected frequency or citation counts, and links indicated collaborative or co-citation relationships. In the author collaboration map, nodes appearing within the same time zone represented the same time period, and the position of each node reflected the year of first publication of the corresponding author. In the co-cited reference map, node size was proportional to the citation frequency of each reference, and the colored rings within nodes represented citation counts across different time periods. Data on disease associations were obtained from the Citexs Data Analysis Platform (https://www.citexs.com/) and used for disease-related visualization analysis.

Topic modeling analysis

To identify latent thematic structures in the bibliographic corpus, we applied LDA, a probabilistic topic modeling method widely used for discovering underlying topics in large-scale text data [34]. For each record, the article title, abstract, and author keywords were concatenated into a single document string. Text preprocessing included lowercasing, tokenization, lemmatization using the NLTK WordNet Lemmatizer, removal of standard and domain-specific stopwords, and detection of meaningful bigram and trigram collocations using Gensim’s Phrases model. Records yielding empty text after preprocessing were excluded from topic modeling. The resulting token sequences were transformed into a term-frequency bag-of-words matrix using scikit-learn CountVectorizer.

LDA models were fitted for topic numbers ranging from 1 to 30 using scikit-learn LatentDirichletAllocation with batch variational Bayes. To improve reproducibility, model training was performed with a fixed random seed, 50 expectation–maximization iterations, and symmetric Dirichlet priors. Detailed preprocessing and model parameters, including vocabulary filtering thresholds, phrase-detection settings, random_state, max_iter, alpha, and eta, are provided in Supplementary Table S4.

Candidate LDA models were evaluated using both perplexity and C_v coherence. To further support the reliability and interpretability of the selected topic solution, topic interpretability was evaluated by examining the consistency of high-probability terms, representative documents, and thematic coherence across candidate models. The final topic number was selected by balancing statistical performance with interpretability and parsimony. The topic coherence curve showed that the nine-topic model achieved the highest coherence score, indicating strong semantic consistency and interpretability among topic terms (Supplementary Fig. S1). The perplexity curve decreased rapidly when the number of topics increased from 1 to 10, declined more slowly between 10 and 20 topics, and reached a plateau after approximately 20 topics (Supplementary Fig. S2). Based on coherence performance, perplexity behavior, topic parsimony, and interpretability, the nine-topic model was selected as the final solution.

For the final model, the top 10 terms for each topic were extracted from the topic–word distribution matrix and visualized using word clouds. The dominant topic of each document was defined as the topic with the highest posterior probability. Dominant-topic document counts were calculated by assigning each document to its most probable topic, whereas mean topic proportion was calculated by averaging document-level topic probabilities across all included records. Therefore, dominant-topic counts and mean topic proportions represent related but non-identical measures of topic prevalence.

To further enhance the interpretability of the LDA-derived topics, documents were ranked within each topic according to their topic-specific probabilities. For each topic, three representative high-probability articles were selected from among the high-probability records after manual assessment of their semantic consistency with the assigned topic label. The titles, publication years, and topic probabilities of these representative articles are provided in Supplementary Table S5.

Annual topic prevalence was calculated by averaging document-level topic probabilities within each publication year and fitting ordinary least squares regression models using Seaborn and Statsmodels. Regression coefficients, P values, and R² values were obtained, and a two-tailed threshold of α = 0.01 was used to identify increasing, decreasing, or stable trends. Stacked area plots were generated to visualize temporal changes in normalized topic composition. To further explore semantic relationships among topics, principal component analysis (PCA) was applied to the document–topic distribution matrix for two-dimensional projection.

Results

The nano-enabled interpretation in this study was based on the identification of emerging micro/nano-architectural strategies, nanoparticle integration, and multifunctional platform design reported within the HM literature, rather than a separate retrieval filter based on the keyword “nano”.

Publication growth trends

Fig. 2A outlines the dual-database search, screening, and bibliometric workflow used to construct the final dataset. After duplicate removal and title, abstract, and full-text screening, 4,399 English-language publications from 2000 to 2025 were included in the analysis. The included publications consisted primarily of original research articles, with review articles representing a smaller proportion of the dataset. Both publication types were retained because they collectively reflect knowledge generation and synthesis within the biomedical HM research landscape. The dataset construction and quality-control procedures are summarized in Supplementary Table S3. The final dataset contained publications retrieved independently from both WoSCC and PubMed, and the inclusion of multiple synonymous terms for HMs (e.g., microspheres, microparticles, microgels, and beads) improved the coverage of heterogeneous terminology used in this field. As shown in Fig. 2B, annual publication output increased from 31 publications in 2000 to 513 in 2025, suggesting a sustained increase in research activity related to HMs in biomedicine. The overall trajectory was not strictly linear. Publication output remained relatively low and fluctuated during the early stage, followed by steady expansion and a more pronounced increase after 2020. Accordingly, the cumulative number of publications reached 4,399 by 2025, reflecting the long-term accumulation of literature in this field.

The highest year-on-year growth rate occurred in 2003, reaching 118.75%, when the number of publications more than doubled compared with the previous year. However, this increase should be interpreted cautiously because it was calculated from a small baseline. In the most recent decade, the largest annual increase was observed in 2024, with a growth rate of 31.12%, followed by continued growth in 2025. Based on the cumulative publication data from 2000 to 2025, the observed growth trajectory was fitted by a quadratic function: y = 7.352x² − 34.609x + 114.18, with R² = 0.9979. In this model, x represents the chronological year sequence and y represents the cumulative number of publications. A standard logistic model was also explored because such models can capture decelerating growth toward a saturation level. However, the cumulative publication series showed no clear plateau within the observation period, and the logistic fit did not provide a stable estimate of the saturation ceiling. The quadratic function was therefore retained as a descriptive fit to the observed 2000–2025 trajectory. Importantly, it should not be interpreted as implying indefinite accelerating growth or as a model for long-term prediction.

Knowledge production and collaboration landscape

Country/region distribution and collaboration

As shown in the country/region collaboration network (Fig. 3A and B), research on biomedical HMs involved 94 countries/regions, indicating broad international participation in this field. In Fig. 3A, each node represents a country or region, with node size corresponding to publication output and link thickness reflecting the strength of international collaboration [35]. China was the most productive country, contributing 1,946 publications, which accounted for 44.24% of the total output and was more than twice the output of the United States, the second-ranked country with 744 publications. India, Japan, and South Korea followed, with 296, 212, and 207 publications, respectively. Despite China’s leading contribution in publication volume, citation-based indicators revealed a more nuanced pattern of academic influence. Among the top 20 most productive countries, the Netherlands had the highest average number of citations per paper (58.12), followed by Canada (54.81), suggesting that some countries with relatively smaller publication volumes produced highly cited research outputs. This finding highlights a distinction between productivity-driven growth and citation-intensive scholarship in this field.

The international collaboration structure was further visualized using a chord diagram generated with the chorddiag R package (Fig. 3B). Each peripheral arc represents a country or region, and the width of the connecting chords is proportional to the strength of bilateral collaboration [36]. The strongest collaboration was observed between China and the United States, with a link strength of 100, indicating the strongest bilateral collaboration in this field. The next strongest links were observed between the United States and South Korea (link strength = 32) and between China and the United Kingdom (link strength = 21). These results suggest that the global collaboration network is not uniformly distributed but is instead organized around several major research hubs, particularly China and the United States.

Citation burst analysis using CiteSpace further revealed temporal changes in country/region-level citation attention from 2000 to 2025 (Fig. 3C). Japan showed the strongest citation burst, with a burst strength of 34.70 from 2000 to 2012, suggesting sustained early citation attention during the formative stage of the field. The Netherlands ranked second, with a burst strength of 15.52 from 2000 to 2009, followed by India, which showed a burst strength of 12.96 from 2007 to 2014. Among the top 10 countries with the strongest citation bursts, Japan, the Netherlands, and Belgium had the earliest burst onset, whereas Türkiye showed the latest burst beginning in 2023 and continuing through 2025. These temporal patterns suggest that country-level citation attention in research on biomedical HMs has gradually shifted from early contributions by several established countries toward broader and more geographically diverse participation. Publication output reflects absolute research productivity, whereas citation burst strength captures temporally concentrated increases in citation attention. Therefore, a country with high cumulative publication output does not necessarily exhibit one of the strongest citation bursts. Because this analysis was based on WoSCC and PubMed-indexed English-language publications, contributions published in non-English journals or local databases may be underrepresented.

Institutional distribution and collaboration network

The institutional collaboration map (Fig. 4A and B) reveals a multi-clustered structure with a strong concentration of Chinese institutions in research on biomedical HMs. A total of 3,036 institutions contributed to the 4,399 included publications. Shanghai Jiao Tong University was the most productive institution, with 179 publications and a recent average publication year of 2022.70, suggesting its recent and increasing activity in this field. Sichuan University and the Chinese Academy of Sciences followed, with 131 and 120 publications, respectively. Most of the top 10 productive institutions had average publication years between 2021 and 2023, indicating that institutional research activity has intensified in recent years. Citation performance varied across institutions. Among the top 10 most productive institutions, the Chinese Academy of Sciences showed the highest average number of citations per paper (54.67), suggesting heterogeneity between productivity and citation impact.

The institutional collaboration network further showed that Shanghai Jiao Tong University had the highest total link strength (115), indicating its central position in inter-institutional cooperation. Its strongest bilateral collaboration was with Chongqing Medical University (link strength = 18), followed by Tongji University (link strength = 15) and Soochow University (link strength = 13). These findings suggest that the institutional network is not uniformly distributed but is organized around several highly connected research hubs.

Citation burst analysis (Fig. 4C) highlights temporal shifts in institutional prominence. Kyoto University showed the strongest and longest early citation burst, with a burst strength of 27.35 from 2000 to 2012, suggesting sustained early influence during the formative stage of the field. Shanghai Jiao Tong University ranked second, with a burst strength of 23.75 from 2022 to 2025, reflecting its recent rise in this research area. Utrecht University also exhibited an early burst from 2000 to 2008. Notably, the citation bursts of Shanghai Jiao Tong University, Soochow University, and Chongqing Medical University extended into 2023–2025, indicating the recent emergence of Chinese institutions as major contributors to the field. Together, these patterns suggest a progressive transition from early citation attention concentrated in established international institutions toward intensified recent activity within high-output Chinese research clusters.

Author distribution and citation bursts

The author time-zone map (Fig. 5A) shows the temporal evolution of collaborative authorship in research on biomedical HMs. Each node represents an individual author, and links indicate co-authorship relationships [37]. Node size corresponds to CiteSpace publication frequency within the mapped network, while node color reflects the publication timeline, with purple indicating earlier activity and yellow indicating more recent publications [38]. Multiple colors within a node form an annual-ring pattern, suggesting sustained publication activity over time. Because this map only includes authors with collaborative links, authors without identifiable co-authorship connections are not displayed in Fig. 5A. Cui Wenguo showed the highest CiteSpace publication frequency, with 80 occurrences, followed by Tabata Yasuhiko, with 72 occurrences. The time-zone distribution further showed that Cui Wenguo first appeared in the field in 2020 within the retrieval period, whereas Xu Yong and Yang Huilin first appeared in 2024. These findings suggest that new contributors have continued to emerge during the recent expansion of this field.

Author citation burst analysis (Fig. 5B) identified authors whose work received rapidly increasing attention during specific periods. Cui Wenguo had the strongest citation burst, with a burst strength of 24.42 from 2021 to 2025. Tabata Yasuhiko ranked second, with a burst strength of 19.18 from 2003 to 2012, reflecting earlier sustained citation attention. Deng Lianfu ranked third, with a burst strength of 8.13 from 2020 to 2025. Notably, several authors, including Cui Wenguo, Deng Lianfu, Cai Zhengwei, Chen Liang, Wang Juan, Zhao Yuanjin, and Huang Wei, showed bursts extending into 2023–2025. This pattern suggests that recent author-level influence has become increasingly concentrated among a group of active researchers, reflecting the ongoing growth and renewal of research on biomedical HMs.

However, author-level findings should be interpreted with caution because CiteSpace-based author analysis relies mainly on name strings rather than persistent researcher identifiers. This limitation is particularly relevant for Chinese names, where common surnames, pinyin transliteration, name-order variation, and abbreviated given names may lead to residual author conflation or splitting [39]. Such ambiguity may affect estimates of author frequency, collaboration links, and burst strength [40, 41]. Future studies incorporating Open Researcher and Contributor Identifier (ORCID), Scopus Author ID, ResearcherID, or other persistent identifiers may improve author disambiguation and enhance the reliability of author-level analyses.

Journals, disciplinary fields, and co-cited references

Journals and citation analysis

The journal distribution analysis showed that 763 journals contributed to the 4,399 publications on biomedical HMs. As shown in the journal density map (Fig. 6A), color intensity reflects publication concentration, with warmer regions indicating journals with higher publication output [42]. After setting the minimum publication threshold to 15, International Journal of Biological Macromolecules emerged as the most productive journal, with 216 publications and an average publication year of 2021.44. This suggests that the journal has become an important outlet for recent research in this field. Biomaterials ranked second, with 130 publications and an earlier average publication year of 2012.44, followed by Carbohydrate Polymers, with 117 publications and an average publication year of 2017.07. Among the top five most productive journals, International Journal of Biological Macromolecules showed both the highest publication output and the latest average publication year, indicating its increasing relevance to biomedical HM research in recent years.

Fig. 6B further presents the temporal distribution of journals contributing to this field. Each node consists of a circular marker and its journal label, with node size proportional to publication volume. Link thickness reflects the strength of journal-level associations, while node color represents the average publication year according to the gradient scale shown in the lower-right legend [43]. Blue or purple tones indicate earlier average publication years, whereas yellow tones indicate more recent publication activity. Among the top 20 most productive journals, Journal of Controlled Release, Biomaterials, and International Journal of Pharmaceutics showed relatively early average publication years, before 2015. In contrast, Materials Today Bio, Bioactive Materials, and Chemical Engineering Journal published articles in this field relatively later, suggesting that biomedical HM research has recently become more visible in journals emphasizing bioactive materials, translational biomaterials, and chemical engineering.

The dual-map overlay analysis provides an integrated view of the citation relationships between citing and cited journals across major disciplinary domains (Fig. 6C). In this map, citing journals are located on the left, while cited journals are located on the right. Each point represents a journal, and the curved lines between the two sides indicate citation pathways, thereby illustrating the interdisciplinary knowledge flow within the field. The length of the vertical axis of each ellipse reflects the number of published papers, whereas the horizontal axis reflects the number of contributing authors [44]. As shown in Fig. 6C, publications on biomedical HMs were mainly concentrated in journals belonging to the Physics, Materials, and Chemistry domains. Their cited references were primarily distributed in Molecular Biology, Genetics, Chemistry, Materials, Physics, and Environmental Toxicology and Nutrition. These citation trajectories suggest that biomedical HM research is rooted in materials science and chemical engineering, while its biological validation and application-oriented development increasingly draw on biomedical and environmental science knowledge bases.

The WoSCC subject-category analysis further supported the interdisciplinary nature of this field (Fig. 6D). The included publications were grouped into five major disciplinary clusters using VOSviewer, with different colors representing distinct research domains. Research on biomedical HMs was mainly concentrated in the Biology and Medicine cluster and the Chemistry and Physics cluster. In particular, subject categories such as Materials Science, Biomaterials; Polymer Science; Engineering, Biomedical; Materials Science, Multidisciplinary; and Chemistry, Multidisciplinary accounted for a relatively high proportion of publications. These findings indicate a dual-core disciplinary structure: one core is based on polymer science, biomaterials, and materials chemistry, while the other is oriented toward biomedical engineering, pharmacology, and translational biomedical applications. However, because WoSCC subject categories are assigned at the journal level, some heterogeneous studies may be grouped under broad disciplinary labels, and finer distinctions between specific biomedical applications may be partially obscured.

Intellectual base and knowledge evolution: co-citation structure

Before interpreting the co-citation and burst networks, we first summarized the top 10 most-cited references based on WoSCC citation counts to provide an overview of highly visible publications in this field (Table 1). The most-cited reference was the review by Andrew C. Daly et al. (2020) [45], followed by studies focusing on injectable HMs, microfluidic fabrication, cell/drug delivery, OA treatment, and regenerative biomaterials. Total citation counts reflect cumulative scholarly visibility, whereas co-citation frequency and citation burst strength capture the position of references within the co-citation network and time-sensitive increases in scholarly attention, respectively.

Table 1.

Top 10 most-cited publications in biomedical HM research based on WoSCC citation counts

Rank	Title	Type	Author (Year)	Journal	Citations	DOI
1	Hydrogel microparticles for biomedical applications	Review	Andrew C Daly et al. (2020)	Nature Reviews Materials	1,077	https://doi.org/10.1038/s41578-019-0148-6
2	Injectable Stem Cell-Laden Photocrosslinkable Microspheres Fabricated Using Microfluidics for Rapid Generation of Osteogenic Tissue Constructs	Article	Xin Zhao et al. (2016)	Advanced Functional Materials	380	https://doi.org/10.1002/adfm.201504943
3	Injectable Microfluidic Hydrogel Microspheres for Cell and Drug Delivery	Review	Zhenyu Zhao et al. (2021)	Advanced Functional Materials	313	https://doi.org/10.1002/adfm.202103339
4	Biomimetic injectable hydrogel microspheres with enhanced lubrication and controllable drug release for the treatment of osteoarthritis	Article	Ying Han et al. (2021)	Bioactive Materials	300	https://doi.org/10.1016/j.bioactmat.2021.03.022
5	Injectable hydrogel microspheres with self-renewable hydration layers alleviate osteoarthritis	Article	Yiting Lei et al. (2022)	Science Advances	291	https://doi.org/10.1126/sciadv.abl6449
6	Modulation of Local Overactive Inflammation via Injectable Hydrogel Microspheres	Article	Jiang Bian et al. (2021)	Nano Letters	202	https://doi.org/10.1021/acs.nanolett.0c04713
7	Ball-Bearing-Inspired Polyampholyte-Modified Microspheres as Bio-Lubricants Attenuate Osteoarthritis	Article	Jielai Yang et al. (2020)	Small	159	https://doi.org/10.1002/smll.202004519
8	Charge-Guided Micro/Nano-Hydrogel Microsphere for Penetrating Cartilage Matrix	Article	Feng Lin et al. (2021)	Advanced Functional Materials	139	https://doi.org/10.1002/adfm.202107678
9	Stem Cell-Recruiting Injectable Microgels for Repairing Osteoarthritis	Article	Yiting Lei et al. (2021)	Advanced Functional Materials	137	https://doi.org/10.1002/adfm.202105084
10	Living and Injectable Porous Hydrogel Microsphere with Paracrine Activity for Cartilage Regeneration	Article	Xingchen Li et al. (2023)	Small	130	https://doi.org/10.1002/smll.202207211

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Note: Citation counts were extracted from WoSCC on December 31, 2025

Based on WoSCC records with available cited-reference metadata, Fig. 7A illustrates the reference co-citation network of research on biomedical HMs from January 1, 2000 to December 31, 2025. The visualization was generated using CiteSpace to identify key co-cited references and their temporal citation patterns. In this network, node size is proportional to co-citation frequency within the mapped dataset, and links between nodes represent co-citation relationships among references. Node color reflects the citation timeline, with purple or blue tones indicating earlier citation activity and yellow tones indicating more recent citations [40]. Multiple colors within a node form an annual-ring pattern, suggesting that the reference was cited across multiple years. The three references with the highest co-citation frequencies were Andrew C. Daly et al. (2020) [45], Zhenyu Zhao et al. (2021) [6], and Ying Han et al. (2021) [46], with co-citation frequencies of 94, 62, and 45, respectively. These highly co-cited studies represent important intellectual anchors in the field and reflect major research interests in hydrogel-based biomaterials, biomedical engineering, and functional microsphere-related applications. Among the top 10 co-cited references, Kuen Yong Lee et al. (2012) and Andrew C. Daly et al. (2020) showed relatively high betweenness centrality values of 0.82 and 0.30, respectively, suggesting that they may serve as bridging references within the co-citation network [45, 47]. Co-citation frequency reflects how often a reference is co-cited within the dataset, whereas betweenness centrality reflects its bridging position between different parts of the network. Thus, a highly central reference does not necessarily have the highest co-citation frequency. These findings indicate that the knowledge base of research on biomedical HMs is structured around both foundational hydrogel materials studies and more recent application-oriented biomaterials research.

Using the same WoSCC cited-reference dataset, the citation-burst map of the top 10 burst references was further generated using CiteSpace to identify publications that experienced a rapid increase in citation attention during specific periods (Fig. 7B). In this map, red segments indicate the time intervals during which citation bursts occurred [41]. Andrew C. Daly et al. (2020) [45] showed the strongest citation burst, with a burst strength of 20.67 from 2021 to 2025, indicating sustained recent attention. Xin Zhao et al. (2016) [48] ranked second, with a burst strength of 18.99 from 2018 to 2021, followed by Zhenyu Zhao et al. (2021) [6], with a burst strength of 18.22 from 2022 to 2025. Narayan Bhattarai et al. (2010) [49] showed one of the longest burst durations, extending from 2010 to 2015, suggesting prolonged citation attention during an earlier developmental stage of the field.

Several of the top 10 burst references showed citation bursts extending into 2023–2025, including recent studies on advanced hydrogel biomaterials, microsphere-based delivery systems, tissue engineering, and regenerative biomedical applications [6, 45, 46, 50–54]. This pattern suggests that recent intellectual attention has increasingly shifted toward emerging HM platforms with enhanced functional integration and application-oriented design. However, citation burst strength reflects a rapid increase in scholarly attention within a defined time window rather than absolute citation impact. Therefore, these burst results should be interpreted as time-sensitive indicators of emerging influence rather than definitive measures of long-term academic importance.

Keyword co-occurrence, burst detection, and research frontiers

The keyword co-occurrence and temporal evolution map provides an overview of the thematic structure and emerging directions in research on biomedical HMs (Fig. 8A). After conservative keyword normalization and merging, 8,293 keyword terms were retained from the original 8,342 entries, corresponding to a reduction of 49 entries. The limited reduction reflects the deliberately restrictive merging criteria: only unequivocally equivalent lexical variants were combined, whereas related but non-identical terms were retained separately to minimize semantic over-merging. A minimum occurrence threshold of 20 was subsequently applied, and 91 keywords were included in the VOSviewer visualization. In this map, each node consists of a circular marker and its keyword label. Node size is proportional to keyword occurrence frequency, while links between nodes represent keyword co-occurrence relationships. Node color indicates the average publication year according to the gradient scale, with blue tones representing earlier average publication years and yellow tones representing more recent average publication years. The temporal keyword map suggests a gradual shift in research emphasis [38]. Earlier terms, such as “controlled release” and “biodegradable”, had average publication years before 2015, reflecting the foundational importance of release kinetics and degradable biomaterials in the early development of this field. Keywords such as “hydrogels”, “tissue engineering”, and “mesenchymal stem cells” had average publication years between 2016 and 2020, indicating growing integration of HMs with regenerative medicine and cell-based therapeutic strategies. More recent terms, such as “hydrogel microspheres”, “osteoarthritis”, and “wound healing”, had average publication years around 2022–2023, suggesting that recent research has increasingly shifted toward disease-oriented and application-driven biomedical contexts.

Keyword burst analysis using CiteSpace further identified terms that experienced rapid increases in scholarly attention during specific periods (Fig. 8B). “Hydrogel microspheres” showed the strongest burst, with a burst strength of 39.30 from 2023 to 2025, indicating the recent consolidation of this term as a distinct research focus. “Drug delivery” ranked second, with a burst strength of 15.02 from 2008 to 2013, reflecting its central role in earlier biomedical HM research. “Wound healing” ranked third, with a burst strength of 11.17 from 2022 to 2025, suggesting increasing attention to tissue repair and regenerative applications. Among the top 10 burst keywords, “basic fibroblast growth factor” showed an early and long-lasting burst from 2000 to 2013, whereas “hydrogel microspheres”, “wound healing”, “intervertebral disc degeneration”, “reactive oxygen species”, and “cartilage regeneration” showed bursts extending into 2023–2025. These patterns indicate a shift in research attention from earlier interests in growth factors, controlled release, and drug delivery toward increasing attention to regenerative medicine, oxidative stress, musculoskeletal disease, and tissue repair-related topics.

Fig. 8C displays the timeline visualization of keyword clusters, providing a longitudinal view of how major thematic domains have evolved over time. In this map, keywords within the same cluster are arranged along the same horizontal line, and node size is proportional to keyword frequency. Links between nodes indicate co-occurrence relationships, while node color reflects temporal evolution from earlier to more recent appearances [37]. The analysis identified ten major clusters: #0 wound healing, #1 drug delivery, #2 tissue engineering, #3 basic fibroblast growth factor, #4 bone tissue engineering, #5 mesenchymal stem cells, #6 bone regeneration, #7 sustained release, #8 controlled release, and #9 drug release. These clusters indicate that biomedical HM research has been structured around several persistent themes, including controlled release, drug delivery, tissue engineering, and bone regeneration. At the same time, the recent prominence of wound healing, intervertebral disc degeneration (IDD), cartilage regeneration, and reactive oxygen species (ROS) suggests a growing translational shift toward disease-specific repair, inflammation-related injury, and musculoskeletal regenerative applications.

Together, these keyword analyses demonstrate temporal changes in the thematic composition of biomedical HM research, with early terms mainly associated with delivery-related concepts and more recent terms showing broader associations with regenerative and disease-related applications.

Biomedical application landscape and emerging nano-enabled design trends of HMs

Mapping disease associations helps clarify the biomedical application landscape of HMs and identify disease contexts in which these systems have been most frequently investigated. Using the Citexs Data Platform, 652 disease terms were extracted from the 4,399 included publications. After setting the minimum occurrence threshold to eight, disease terms meeting this criterion were visualized using VOSviewer. As shown in the disease density map (Fig. 9A), the most frequently occurring disease-related term was “cartilage diseases”, followed by “osteoarthritis”, “fractures”, “hepatocellular carcinoma”, “myocardial infarction”, and “intervertebral disc degeneration”. The high density around cartilage diseases and OA suggests that musculoskeletal and cartilage-related disorders represent major biomedical application contexts for HMs. The disease co-occurrence network further indicates that research on HMs is not restricted to a single therapeutic area. Instead, it spans multiple disease categories, including musculoskeletal disorders, cancer, cardiovascular diseases, wound-related conditions, and inflammatory diseases. Among these, the prominence of “cartilage diseases”, “osteoarthritis”, “fractures”, and “intervertebral disc degeneration” is consistent with the functional advantages of HMs in localized delivery, injectable implantation, controlled release, and tissue regeneration. These features may explain their frequent use in studies related to cartilage repair, bone regeneration, and musculoskeletal tissue engineering.

The temporal disease map (Fig. 9B) provides additional insight into changes in disease-related research attention over time. In this visualization, node size corresponds to disease-term occurrence frequency, while node color indicates the average publication year, with blue representing earlier occurrence and red indicating more recent appearance [41]. Earlier disease-related terms, such as “myocardial infarction”, showed average publication years before 2018. Terms such as “cartilage diseases”, “fractures”, and “soft tissue injuries” appeared mainly between 2018 and 2021, reflecting the growing application of HMs in tissue repair and regenerative medicine. More recent terms, including “osteoarthritis”, “wound infection”, and “inflammatory bowel diseases”, showed average publication years between 2021 and 2023, suggesting a recent shift toward inflammation-associated, degenerative, and clinically relevant repair contexts.

Overall, the disease landscape analysis identified the major disease contexts associated with biomedical HM research and demonstrated increasing representation of musculoskeletal and inflammatory conditions in recent publications. In particular, the increasing prominence of OA, cartilage diseases, IDD, and wound infection indicates that HMs are being increasingly explored in musculoskeletal degeneration, localized inflammation, and regenerative repair. However, because disease terms were extracted through automated text mining and co-occurrence analysis, these results should be interpreted as indicators of research attention rather than direct evidence of therapeutic efficacy or clinical translation. In addition, the extracted terms may include both broad disease categories, such as “cartilage diseases”, and more specific diseases or clinical/pathological contexts, such as “osteoarthritis”, “wound infection”, and “myocardial infarction”. These terms may also be partially overlapping and are not necessarily mutually exclusive. Therefore, the disease landscape should be interpreted as a text-mined application map rather than a formal disease taxonomy.

LDA-based topic modeling of latent research themes

To complement keyword co-occurrence analysis and further uncover latent semantic structures in research on biomedical HMs, LDA-based topic modeling was applied to the bibliographic text corpus. Unlike keyword analysis, which captures explicit term co-occurrence, LDA identifies underlying thematic patterns based on probabilistic topic distributions across documents [34, 55]. After evaluating candidate LDA models with different topic numbers, the nine-topic solution was selected because it achieved the highest coherence score and provided a balanced combination of semantic consistency, interpretability, and topic parsimony (Supplementary Figs. S1 and S2). After manual interpretation of high-weight terms and representative records, nine interpretable topics were identified; their top-weighted keywords, manually assigned labels, dominant-topic document counts, and mean topic proportions are summarized in Table 2. To further support the interpretation and labeling of these topics, three representative high-probability articles for each topic are provided in Supplementary Table S5. These documents were selected from among the high-probability records and illustrate the semantic content underlying each LDA-derived theme. Based on mean topic proportion, Topic 3: Controlled Drug Release was the most prevalent topic (proportion = 0.23). Topic 6: Tissue Engineering and Cell Delivery and Topic 9: Thermosensitive Hydrogel Delivery Systems followed, each with a topic proportion of 0.12. When documents were classified by dominant topic assignment, Topic 3 also contained the largest number of records (n = 950), followed by Topic 6 (n = 609) and Topic 8: Bone Regeneration and Osteogenic Repair (n = 570). Collectively, the nine-topic solution provides an interpretable representation of major thematic domains within the biomedical HM literature, including controlled release, drug delivery platforms, hydrogel formulation design, tissue engineering, and regenerative biomedical applications.

Table 2.

Topic labels, top-weighted keywords, and proportions of the nine LDA-derived topics

Topic	Top Keywords	Label	N	Proportion
1	wound_healing; wound_dressing; composite_hydrogel; antibacterial_activity; diabetic_wound; antibacterial_property; bacterial_infection; drug_release; injectable_hydrogel; chronic_wound	Wound Healing and Antibacterial Hydrogels	387	0.07
2	hydrogel_microspheres; delivery_system; drug_delivery; basic_fibroblast_growth_factor; oral_administration; therapeutic_effect; drug_release; hydrogel_microsphere; drug_delivery_system; magnetic_microspheres	Drug Delivery Systems and Bioactive Factor Release	322	0.08
3	drug_release; controlled_release; drug_delivery; drug_delivery_system; vitro_release; sustained_release; hydrogel_bead; drug_loaded; release_rate; release_profile	Controlled Drug Release	950	0.23
4	hydrogel_microspheres; sodium_alginate; hydrogel_bead; composite_hydrogel; adsorption_capacity; hydrogel_microsphere; cancer_cell; embolic_agent; methylene_blue; microfluidic_device	Alginate Composite and Embolic Microspheres	475	0.09
5	alginate_hydrogel; alginate_microspheres; drug_delivery; hydrogel_microparticles; biomedical_application; hydrogel_microspheres; tissue_engineering; silk_fibroin; alginate_bead; poly_ethylene_glycol	Alginate Microspheres for Biomedical Applications	383	0.09
6	tissue_engineering; stem_cell; tissue_regeneration; cell_viability; gelatin_microspheres; extracellular_matrix; cell_laden; growth_factor; cell_culture; cell_therapy	Tissue Engineering and Cell Delivery	609	0.12
7	plga_microspheres; sustained_release; drug_delivery_system; drug_delivery; chitosan_microspheres; poly_lactic_glycolic_acid; myocardial_infarction; long_term; blood_vessel; spinal_cord_injury	Long-Acting Polymeric Microspheres	357	0.10
8	bone_regeneration; hydrogel_microspheres; growth_factor; bone_defect; composite_scaffold; composite_hydrogel; bone_tissue_engineering; osteogenic_differentiation; sustained_release; bone_repair	Bone Regeneration and Osteogenic Repair	570	0.10
9	thermosensitive_hydrogel; drug_delivery; poly_isopropylacrylamide; composite_hydrogel; aqueous_solution; drug_carrier; chitosan_hydrogel; lower_critical_solution_temperature; phase_transition; sustained_release	Thermosensitive Hydrogel Delivery Systems	346	0.12

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Note: N indicates the number of records assigned to each topic based on the highest posterior topic probability. Proportion indicates the mean document-level topic probability across all included records. Therefore, N and proportion represent related but non-identical measures of topic prevalence

Temporal regression analysis revealed divergent developmental trajectories among the nine topics (Fig. 10A). Topic 1: Wound Healing and Antibacterial Hydrogels, Topic 4: Alginate Composite and Embolic Microspheres, Topic 6: Tissue Engineering and Cell Delivery, and Topic 8: Bone Regeneration and Osteogenic Repair showed significant positive temporal trends, indicating their increasing prominence over time. In contrast, Topic 3: Controlled Drug Release, Topic 7: Long-Acting Polymeric Microspheres, and Topic 9: Thermosensitive Hydrogel Delivery Systems showed significant negative trends, suggesting a gradual decline in their relative proportions across the study period. Topic 2: Drug Delivery Systems and Bioactive Factor Release and Topic 5: Alginate Microspheres for Biomedical Applications remained relatively stable, although their wider confidence intervals suggest greater year-to-year variability and warrant cautious interpretation. Consistently, the stacked area plot showed that Topic 3 remained a long-standing dominant theme but gradually decreased, whereas Topic 1, Topic 6, and Topic 8 expanded in recent years (Fig. 10B).

The PCA-based topic map further illustrated the semantic relationships among LDA-derived topics (Fig. 10C). The first two principal components explained 25.6% and 14.6% of the variance, respectively, together accounting for 40.2% of the structural variation in the corpus. In this map, topic vectors with similar directions indicate closer semantic relationships, whereas opposite directions suggest thematic divergence. Topic 5: Alginate Microspheres for Biomedical Applications showed the largest contribution to the displayed two-dimensional topic space and was oriented in an opposite direction to Topic 6: Tissue Engineering and Cell Delivery, suggesting a semantic distinction between alginate-based biomedical microsphere platforms and tissue engineering/cell-delivery themes. Other closely aligned topics, such as Topic 1: Wound Healing and Antibacterial Hydrogels and Topic 9: Thermosensitive Hydrogel Delivery Systems, indicate partial thematic overlap between hydrogel delivery systems and wound-repair applications.

The topic word clouds provided additional support for the interpretability of the nine topic labels (Fig. 10D). Disease- and repair-oriented themes were represented by Topic 1 and Topic 8, whereas Topic 6 was characterized by terms related to stem cells, tissue engineering, ECM, and cell therapy. Delivery- and formulation-oriented themes were mainly represented by Topic 2, Topic 3, Topic 7, and Topic 9, with repeated terms such as “drug_delivery”, “drug_release”, “controlled_release”, and “sustained_release”. Together, the LDA results demonstrate heterogeneous thematic structures within biomedical HM research, including delivery-related, formulation-related, and regenerative application-related topics.

Cluster-level topic evolution of hm delivery, analysis, and biomedical applications

To further examine the temporal redistribution of LDA-derived themes, annual trend lines were first plotted for all nine topics (Fig. 11A). Based on the topic clustering results, these topics were then grouped into three thematic clusters for cluster-level temporal analysis (Fig. 11B and D). Overall, Topic 3: Controlled Drug Release dominated the early stage of research on biomedical HMs and peaked around 2004, but its relative prominence gradually declined thereafter. In contrast, Topic 8: Bone Regeneration and Osteogenic Repair showed a late-rising pattern and became one of the most prominent topics by 2025. Topic 2: Drug Delivery Systems and Bioactive Factor Release remained relatively stable, whereas Topic 4: Alginate Composite and Embolic Microspheres and Topic 6: Tissue Engineering and Cell Delivery showed sustained activity with gradual increases. These patterns demonstrate distinct temporal trajectories among LDA-derived thematic clusters, with some formulation-related topics showing stable or declining trends and several application-oriented topics showing increased representation.

The drug delivery–regenerative engineering cluster included Topic 2: Drug Delivery Systems and Bioactive Factor Release, Topic 3: Controlled Drug Release, Topic 6: Tissue Engineering and Cell Delivery, and Topic 8: Bone Regeneration and Osteogenic Repair (Fig. 11B). Within this cluster, Topic 3 showed early dominance but entered a long-term declining trajectory after its early peak. By contrast, Topic 6 and Topic 8 increased gradually, with Topic 8 showing an accelerated rise after 2020. Topic 2 showed fluctuations but recovered after 2020. These trajectories suggest that this cluster evolved from a controlled-release-centered pattern toward a more balanced structure integrating targeted delivery, tissue engineering, and regenerative applications.

The other two clusters showed distinct temporal patterns. The alginate/polymeric microsphere formulation cluster, including Topic 5: Alginate Microspheres for Biomedical Applications and Topic 7: Long-Acting Polymeric Microspheres, was characterized by an early peak and subsequent decline of Topic 7, whereas Topic 5 remained relatively stable over time (Fig. 11C). This suggests that long-acting polymeric microspheres represented an earlier hotspot, while alginate-based microspheres have remained a durable material platform. The wound healing–alginate hydrogel delivery cluster, including Topic 1: Wound Healing and Antibacterial Hydrogels, Topic 4: Alginate Composite and Embolic Microspheres, and Topic 9: Thermosensitive Hydrogel Delivery Systems, showed a transition from earlier thermosensitive delivery systems toward more recent wound-healing and alginate composite hydrogel applications (Fig. 11D). Notably, Topic 1 and Topic 4 increased in parallel after approximately 2013, supporting the emergence of wound-repair-oriented HMs as an important recent direction.

Taken together, Fig. 11 provides a cluster-level perspective on the temporal redistribution of LDA-derived themes, highlighting the coexistence of established formulation-related topics and emerging application-oriented research directions.

Discussion

Principal scientometric and topic modeling findings in biomedical HM research

This integrated scientometric and topic modeling framework provides complementary perspectives on the evolution of biomedical HM research. Across publication trends, knowledge networks, keyword evolution, disease mapping, and LDA-derived topics, a common pattern emerged: the field has expanded from a predominantly delivery-oriented research base toward increasingly diverse regenerative, disease-contextualized, and multifunctional applications. Importantly, this evolution is better characterized by functional layering than by simple topic replacement. Controlled-release principles remain embedded within newer tissue-engineering, regenerative, and disease-specific platforms, indicating a broader shift from optimizing individual delivery functions toward integrating material architecture, biological regulation, and application-specific requirements.

This interpretation is further supported by the convergence and divergence observed across LDA, keyword, and co-citation analyses. Controlled release and drug delivery consistently emerged as foundational themes, appearing prominently in keyword clusters, major LDA topics, and co-cited literature on microsphere-based delivery systems. Tissue engineering and regenerative medicine showed a similar cross-method pattern, appearing in recent keyword trends, LDA topics such as Tissue Engineering and Cell Delivery and Bone Regeneration and Osteogenic Repair, and prominent co-cited literature on regenerative biomaterials. At the same time, the three approaches captured different analytical layers. Co-citation analysis primarily reflected the historical intellectual base and influential publications, whereas keyword analysis was more sensitive to explicit and recently emerging terms such as wound healing, osteoarthritis, intervertebral disc degeneration, and cartilage regeneration. LDA, in contrast, identified broader latent semantic structures and differentiated formulation-oriented themes such as alginate-based, long-acting polymeric, and thermosensitive microsphere systems. Thus, the approaches converged at the level of broad research themes but differed in thematic granularity, temporal sensitivity, and the type of information captured. These differences represent complementary analytical perspectives rather than substantively conflicting interpretations of the field.

These findings should nevertheless be interpreted as indicators of research attention, knowledge evolution, and conceptual positioning rather than direct measures of technological superiority, clinical readiness, or therapeutic effectiveness. Accordingly, the results can be considered at three levels: (1) descriptive signals, including publication growth, keyword emergence, and topic prevalence; (2) thematic interpretation, reflecting changes in research interests and knowledge organization; and (3) translational implications, which require independent support from experimental, preclinical, and clinical evidence.

The observed diversification may be partly enabled by the intrinsic material versatility of HMs. Their injectable format, high surface-area-to-volume ratio, tunable porosity, cargo-loading capacity, and ability to conform to irregular defects allow the same general platform concept to be adapted across different biomedical contexts. Previous qualitative syntheses have similarly emphasized the use of HMs for controlled release, cell delivery, tissue regeneration, and cross-system repair, while also noting persistent challenges in manufacturing, standardization, safety, and regulation [7, 12, 15, 18–24]. Thus, the expansion of regenerative and disease-contextualized themes is consistent with increasing functional diversification of HM platforms, but should not be interpreted as evidence of established clinical efficacy.

From controlled release to disease-contextualized biomedical HM platforms

Building on the complementary evidence provided by keyword evolution, disease mapping, and LDA topic modeling, this section interprets how the functional positioning of HMs has evolved beyond conventional delivery applications, while recognizing that earlier formulation-centered strategies remain fundamental components of the field (Figs. 8, 9, 10 and 11). Controlled release and drug delivery constituted the early technical foundation of this field [56]. This is biologically and materially reasonable because HMs combine the water-rich polymer network of hydrogels with microscale geometry, allowing local encapsulation and sustained release of drugs, proteins, growth factors, cells, and other bioactive agents [57, 58]. Their high surface-area-to-volume ratio, tunable porosity, tunable degradation behavior, and flexible polymer composition make them particularly suitable for localized and minimally invasive delivery. Therefore, the declining relative proportion of “Controlled Drug Release” in the LDA analysis should not be interpreted as a loss of importance (Figs. 10 and 11). Rather, controlled release appears to have become a foundational design principle that is increasingly embedded within more complex tissue-repair and regenerative systems. Importantly, the emergence of nano-enabled strategies within HM research should be interpreted as an evolution of material design approaches rather than a replacement of conventional microsphere systems. The bibliometric dataset captures the broader biomedical HM research landscape, within which nano-enabled approaches represent an emerging design direction involving nanoscale functional integration, including nanoparticle incorporation, nanoengineered interfaces, hierarchical micro/nano-architectures, and other nanoscale strategies for modulating biological interactions. Similar concepts have also been explored in other hydrogel-based systems, where nanoscale functional components are incorporated to improve drug loading, responsiveness, or biological interactions [59].

This conceptual expansion is closely related to the intrinsic material advantages of HMs. Compared with bulk hydrogels, HMs can be delivered as discrete injectable units, distribute more readily within irregular defects or confined anatomical spaces, and provide interstitial spaces that facilitate nutrient transport, cell migration, and tissue ingrowth [60]. Their size, stiffness, porosity, degradation behavior, surface chemistry, and bioactivity can be tuned through material selection, crosslinking strategy, fabrication method, and post-fabrication modification [61]. These features allow HMs to function not only as passive depots for payload release, but also as microenvironment-modulating units that can support cell viability, growth factor retention, inflammatory regulation, angiogenesis, ECM remodeling, and tissue regeneration [62–64]. More broadly, regenerative biomaterials are increasingly designed to modulate macrophage behavior and rebalance local immune microenvironments, thereby linking material engineering with endogenous tissue-repair processes [65]. The emergence of porous, core–shell, multicompartmental, cell-laden, nanoparticle-integrated, and stimuli-responsive HMs further demonstrates the increasing structural and functional diversity of HM platforms [12, 66–69].

The disease-associated applications identified in this study highlight biological contexts in which the intrinsic properties of HMs may provide functional advantages, particularly where local retention, injectable administration, controlled release, and microenvironment regulation are required. In wound repair, HMs can provide local delivery of antibacterial, anti-inflammatory, pro-angiogenic, or regenerative agents while supporting cell migration and matrix reconstruction [70, 71]. In musculoskeletal regeneration, their injectable format and tunable mechanical and biochemical properties are well suited to bone, cartilage, tendon, nerve, and intervertebral disc-related repair [7, 72–74]. OA represents a typical disease-contextualized application [75]. Conventional intra-articular delivery is limited by rapid clearance, uneven distribution, and short residence time in the joint cavity [76, 77]. By contrast, HMs have been proposed to offer potential advantages in joint-cavity distribution, lubrication, sustained local release, and responsiveness to pathological microenvironmental cues such as inflammation, oxidative stress, enzymatic degradation, and impaired matrix homeostasis [18, 24].

Taken together, controlled release remains a foundational principle of HM design but is increasingly integrated with broader strategies involving tissue repair, disease-specific intervention, and multifunctional biomaterial engineering. Within this broader evolution, nano-enabled strategies should be considered as one emerging avenue for enhancing HM functionality rather than a defining feature shared by all biomedical HM systems. Nevertheless, these trends reflect research attention and platform development strategies rather than established clinical efficacy, and most disease-contextualized HM systems still require standardized manufacturing, long-term safety evaluation, and disease-specific validation [78].

Disciplinary convergence between polymer science, biomaterials, and biomedical engineering

Beyond thematic evolution, the collaboration and disciplinary analyses provide additional insight into how the HM field has developed as an interdisciplinary research ecosystem. The country/region and institutional analyses indicate that biomedical HM research has developed within a hub-centered yet increasingly international ecosystem, with China contributing the largest publication volume and the United States occupying an important collaborative position (Figs. 3 and 4). Several high-output institutions, including Shanghai Jiao Tong University, demonstrated strong productivity and collaboration activity, while recent citation bursts highlighted the continued emergence of active research centers. These patterns suggest that the rapid expansion of the field has been driven not only by increasing publication volume, but also by the emergence of concentrated research centers with strong activity in biomaterials, tissue engineering, and regenerative medicine.

However, productivity and scholarly visibility should be interpreted as related but distinct dimensions. Although China and several Chinese institutions showed high publication output and recent activity, countries with smaller publication volumes, such as the Netherlands and Canada, exhibited higher average citations per paper among the top productive countries. Similarly, earlier citation bursts from institutions such as Kyoto University and Utrecht University indicate that the intellectual development of the field was initially shaped by a broader international knowledge base before recent activity became more concentrated within high-output Chinese research clusters. Therefore, the current collaboration landscape should not be viewed simply as a ranking of productivity, but as a dynamic structure in which early methodological and conceptual contributions, recent research intensity, and international cooperation jointly shape the field.

This collaboration pattern is closely linked to the inherently interdisciplinary nature of HMs. Their design requires knowledge from polymer chemistry, colloidal science, crosslinking chemistry, interfacial engineering, microfluidics, electrospray, lithography, three-dimensional printing, and mechanical fragmentation. Each fabrication route involves different trade-offs in particle size control, monodispersity, structural complexity, cell compatibility, encapsulation efficiency, throughput, and scalability. For example, microfluidic approaches offer high controllability and narrow size distribution, whereas batch-emulsion and electrohydrodynamic spraying methods may provide higher production efficiency or encapsulation advantages but often face limitations in size uniformity or structural precision [9, 15, 19, 79]. These technical trade-offs explain why HM research is strongly anchored in materials science and engineering while simultaneously requiring biological validation and application-oriented design.

The dual-map overlay and subject-category clustering provide additional evidence for this interdisciplinary structure (Fig. 6). Biomedical HM research is rooted in materials science, polymer science, chemistry, and biomedical engineering, but its cited knowledge base extends toward molecular biology, pharmacology, regenerative medicine, disease modeling, and clinical application fields. This dual-core disciplinary structure reflects the dual identity of HMs: they are engineered microscale materials, but their biomedical value depends on how effectively they interact with cells, tissues, pathological microenvironments, and therapeutic cargos. Applications such as musculoskeletal regeneration, OA therapy, wound repair, and organ-on-a-chip systems further illustrate this convergence. In organ-on-a-chip research, for instance, HMs are being explored as ECM-mimetic and tunable components that help reconstruct cellular niche microenvironments, deliver cells or therapeutic agents, and support intercellular or inter-organ crosstalk, highlighting the extension of HM research from therapeutic delivery toward advanced disease modeling and translational platforms [21, 80].

Co-citation and citation burst analyses further indicate that the intellectual foundation of biomedical HM research has been shaped by interactions among hydrogel biomaterials, controlled release strategies, microfluidic fabrication, cell/drug delivery, and regenerative applications (Fig. 7). This suggests that the knowledge base of the field has developed through the interaction between foundational hydrogel science and emerging application-oriented biomaterials research. Importantly, co-citation frequency and citation burst strength reflect scholarly visibility and temporal attention rather than direct evidence of methodological quality or clinical readiness. Thus, the knowledge structure identified in this study should be interpreted as a map of influential research directions rather than a definitive hierarchy of scientific or translational value.

Overall, the collaboration and disciplinary patterns indicate that the maturation of biomedical HM research will require deeper integration across materials design, fabrication engineering, biological mechanism studies, disease-specific models, and translational evaluation. High-output institutions and international collaboration hubs may acceerate this process, but further progress will depend on whether the field can move beyond parallel disciplinary contributions toward coordinated, problem-driven research programs. Such integration is particularly important for addressing the translational bottlenecks of HMs, including scalable manufacturing, batch-to-batch consistency, standardized characterization, long-term biosafety, and disease-specific efficacy validation.

Application-oriented requirements for biomedical HM platforms

From a polymer materials perspective, the application-oriented trends identified in this study should be interpreted through structure–property–application relationships. Based on the disease-landscape and LDA findings, biomedical HMs are not evolving along a single technical route, but are being adapted to distinct biological and application contexts through polymer composition, crosslinking chemistry, network architecture, particle size, porosity, stiffness, degradation behavior, surface chemistry, cargo-release behavior, and stimuli responsiveness (Figs. 9–Fig. 11). These parameters collectively determine HM performance in controlled release, cell delivery, lubrication, tissue integration, microenvironment modulation, and disease-contextualized biomedical applications. Therefore, the value of HMs should be evaluated not only by whether they can encapsulate and release therapeutic agents, but also by whether their structural and functional attributes match the needs of specific tissues, diseases, and delivery environments (Table 3). Alginate-based hydrogel systems provide a representative example of this structure–function relationship, as composite engineering can improve material properties while extending hydrogel applications toward localized drug delivery and three-dimensional biological model construction [81]. Recent hydrogel systems integrating nanoscale components or bioactive modules further demonstrate how hydrogel architectures can be engineered beyond simple cargo depots toward multifunctional therapeutic platforms [82].

Table 3.

Representative biomedical application scenarios, micro/nano-scale design requirements, and validation considerations for HM-based platforms synthesized from the present analysis and relevant literature

Application scenario	Biological/clinical challenge	Required micro/nano-scale properties	Representative nano-enabled design strategies	Characterization and translational bottlenecks
Wound healing	Infection, inflammation, impaired angiogenesis	Injectable, antibacterial, moisture-retaining, pro-angiogenic	Antibacterial agents, ROS-responsive release, composite hydrogels	Sterility, reproducibility, wound heterogeneity
OA/cartilage repair	Rapid joint clearance, inflammation, poor cartilage regeneration	Lubrication, retention, anti-inflammatory release, cartilage penetration	HA-based HMs, charge-guided HMs, self-lubricating microspheres	Long-term safety, joint residence, mechanical durability
Bone regeneration	Irregular defects, osteogenic induction, vascularization	Osteoinductivity, porosity, sustained growth-factor delivery	Growth-factor-loaded HMs, cell-laden HMs, composite scaffolds	Scale-up, mechanical matching, ectopic ossification risk
IDD	Avascular tissue, acidic/inflammatory microenvironment	Injectable, mechanically adaptive, anti-inflammatory, matrix-supportive	Cell/growth factor delivery, stimuli-responsive microspheres	Delivery accuracy, disc mechanics, long-term retention
Cell delivery/tissue engineering/granular biofabrication and 3D bioprinting	Cell survival, spatial organization, nutrient diffusion, printability, construct fidelity	Cell-compatible, interparticle porous, ECM-mimetic, tunable rheology, printable, degradable	Cell-laden microgels/HMs, granular hydrogel assembly, modular bioinks, microgel-based supporting matrices	Cell viability, rheology–mechanics balance, structural fidelity, reproducibility, GMP production
Cancer/embolization/immunotherapy and vaccine delivery	Local tumor control, embolic stability, immune activation, spatiotemporal cargo delivery	Local retention, embolic capability when required, imageability, controlled/staged release, immune compatibility	Drug-eluting or embolic HMs, immunomodulatory cargo-loaded HMs, microsphere vaccines, stimuli-responsive release	Off-target embolization, immune-related safety, biodistribution, release reproducibility, regulatory pathway
Organ-on-a-chip/disease modeling	Reconstructing tissue microenvironments	ECM-mimetic, tunable stiffness, modularity	Cell-laden microgels, multicompartmental HMs	Standardization, reproducibility, model validation
Controlled-release and long-acting delivery	Burst release, short exposure, unstable release profile	High loading, controlled diffusion, predictable degradation, formulation stability	PLGA/alginate/chitosan HMs, thermosensitive HMs, core–shell HMs	Release reproducibility, IVIVC, scale-up, storage stability
Bioimaging and biosensing	Signal stability, tissue penetration, biological background interference	Stable labeling, responsive interfaces, reproducible particle size, nanocomposite integration	Fluorescent/magnetic HMs, nanoparticle-integrated HMs, stimuli-responsive microgels	Sensitivity, specificity, calibration, long-term signal stability, in vivo tracking

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Note: This table provides an interpretive synthesis based on the bibliometric, disease-landscape, and LDA topic-modeling results of the present study, supplemented by representative literature on established and emerging HM research fronts. It summarizes application-oriented platform requirements and translational considerations rather than providing a quantitative ranking of applications or evidence of established clinical efficacy

Abbreviations: ECM, extracellular matrix; GMP, Good Manufacturing Practice; HA, hyaluronic acid; HM, hydrogel microsphere; IDD, intervertebral disc degeneration; IVIVC, in vitro–in vivo correlation; OA, osteoarthritis; PEG, polyethylene glycol; PLGA, poly(lactic-co-glycolic acid); ROS, reactive oxygen species

Although HMs represent an important class of hydrogel-based delivery platforms, they should be considered within the broader landscape of hydrogel systems, including bulk hydrogels, injectable hydrogels, nanogels, and hydrogel nanoparticles. Compared with conventional bulk hydrogels, HMs provide discrete microscale units with improved injectability, spatial distribution, surface-area-to-volume ratio, and modularity, which may facilitate localized cargo delivery and tissue-specific applications. However, bulk or injectable hydrogels may offer advantages in forming continuous three-dimensional matrices, maintaining structural integrity, and supporting cell encapsulation or tissue reconstruction [83]. In contrast, nanoscale hydrogel systems, such as nanogels and hydrogel nanoparticles, generally provide smaller dimensions, higher surface-area exposure, and enhanced intracellular or tissue penetration potential, although challenges related to rapid clearance, limited cargo capacity, and manufacturing complexity may remain.

For wound repair, HM design should prioritize local retention, moisture regulation, antibacterial activity, inflammation control, and support for angiogenesis and ECM reconstruction [84]. Similar hydrogel-based strategies have been developed for infection-associated applications, where antibacterial functionality and controlled release are integrated to address complex wound microenvironments [85]. In this setting, microspheres are often expected to provide sustained release of antimicrobial, anti-inflammatory, pro-angiogenic, or regenerative cargos while maintaining a permissive microenvironment for cell migration and tissue remodeling [8, 62, 86, 87]. More broadly, hydrogel matrices can also serve as local delivery platforms for bioactive extracellular vesicle-based cargos, as exemplified by GelMA-encapsulated small extracellular vesicles for regenerative wound therapy [88, 89]. Porous or composite HMs may be particularly useful because they can enhance fluid exchange, increase loading capacity, and provide additional bioactive or mechanical cues [19]. More broadly, ordered micro-/nano-structured biomaterials can actively regulate cell behavior, the immune microenvironment, and antibacterial responses, highlighting the importance of structural architecture in wound-repair biomaterial design [90]. However, wound environments are highly heterogeneous, especially in chronic or diabetic wounds, where infection, ischemia, oxidative stress, and delayed matrix remodeling may coexist [91]. Recent microenvironment-responsive hydrogel systems further demonstrate that dynamically coupling material behavior to evolving pathological cues can enable sequential antibacterial intervention, oxidative and inflammatory regulation, angiogenic support, and tissue regeneration [92]. Thus, application-oriented HM design for wound repair should move beyond simple drug release and instead integrate antibacterial protection, inflammatory regulation, vascular support, and matrix reconstruction within a locally adaptable system.

For musculoskeletal and joint-related applications, including OA, cartilage-related diseases, IDD, and bone/cartilage repair, the design requirements are more closely linked to mechanical compatibility, local residence, lubrication, and responsiveness to degenerative microenvironments [5, 15]. In OA and cartilage repair, HMs may need to withstand synovial-fluid turnover, distribute within the joint cavity, reduce friction, and release anti-inflammatory or chondroprotective agents in a sustained manner [57]. Surface charge, particle size, hydration layer stability, and matrix-penetrating capacity may influence whether HMs remain in the joint space, interact with cartilage-related barriers, or contribute to lubrication [22]. In IDD, HMs design should additionally account for the avascular and mechanically loaded disc microenvironment, where injectability, retention, anti-inflammatory function, matrix-supportive activity, and mechanical adaptation are all important [93–95]. These examples indicate that musculoskeletal HM systems require tissue- and disease-specific engineering rather than generic regenerative labeling.

For bone regeneration and osteogenic repair, HMs are expected to support osteogenic differentiation, vascularization, defect filling, and spatially controlled release of growth factors, ions, cells, or other osteoinductive cues [96, 97]. Compared with compact bulk scaffolds, injectable or modular HMs may better conform to irregular bone defects and can be incorporated into composite scaffolds or granular hydrogel systems [72]. In this context, porosity, degradation rate, mechanical reinforcement, osteoconductivity, and sustained bioactive factor release become particularly important [79]. However, the design goal is not only to accelerate bone formation, but also to coordinate osteogenesis, angiogenesis, material degradation, and new tissue ingrowth. Therefore, HM-based bone repair platforms should be evaluated using parameters that reflect both material performance and biological integration, including defect filling, mineralization, vascular ingrowth, mechanical recovery, and safety of degradation products.

For cell delivery, tissue engineering, and organ-on-a-chip or disease-modeling applications, the central requirement shifts from payload release to microenvironment construction [12, 15, 80]. HMs used in these contexts should provide cell-compatible fabrication conditions, high cell viability, appropriate stiffness, nutrient diffusion, ECM-mimetic cues, and controllable degradation [98]. Cell-laden, gelatin-based, alginate-based, polyethylene glycol-based, or hybrid HMs can serve as modular microtissues, injectable cell carriers, or building blocks for three-dimensional tissue assembly. In organ-on-a-chip and in vitro biological models, HMs may further contribute to the reconstruction of tissue-specific niches and support cell–cell or cell–matrix interactions. These applications require careful control of microsphere uniformity, cell distribution, microgel mechanics, and biological reproducibility, because small changes in material composition or fabrication conditions may substantially affect cellular behavior and model reliability.

Two additional research fronts warrant specific consideration within this broader landscape. First, granular hydrogel assembly and microgel-assisted 3D bioprinting exploit HMs as modular building blocks, bioinks, or supporting elements for constructing cell-laden and spatially organized tissue architectures. Recent studies have highlighted the importance of interparticle porosity, rheological behavior, printability, cell compatibility, and structural fidelity in these systems [99, 100]. Second, HMs are also being explored for cancer immunotherapy and vaccine delivery, where localized and temporally controlled release of immunomodulatory cargos may be used to regulate the tumor immune microenvironment and amplify antitumor responses [101]. Notably, neither of these fronts emerged as a standalone LDA topic or a top keyword burst in the present analysis. This absence should not be interpreted as indicating that these areas are unimportant or absent from the HM literature. Rather, LDA resolves a limited number of dominant corpus-level semantic themes, whereas keyword burst analysis emphasizes terms showing sufficiently strong and temporally concentrated increases in attention. More specialized, recent, or lexically heterogeneous research fronts may therefore remain embedded within broader tissue-engineering, drug-delivery, or immunomodulatory themes without forming independent bibliometric categories.

Drug delivery, embolization, and cancer-related applications impose another set of design requirements. In these settings, HMs should combine predictable size distribution, embolic stability when required, imageability or traceability, controlled drug loading, and reliable release kinetics [102, 103]. For drug-eluting or embolic microspheres, particle size and deformability may affect vascular distribution, embolic behavior, local drug exposure, and off-target risk. For cancer-related local therapy, HMs may also need to integrate sustained release with responsiveness to tumor-associated cues or compatibility with imaging and interventional procedures [14]. These requirements differ from those of regenerative systems, where cell interaction and tissue integration may be more important. Thus, application-oriented design should distinguish between HMs used primarily as delivery or embolic systems and those intended to function as regenerative microenvironments.

Taken together, the application landscape identified in this study indicates that the future development of HMs should be guided by a “scenario-specific design” framework. Rather than evaluating HMs only by general properties such as injectability, biocompatibility, and sustained release, future studies should define the biological problem first and then tailor material composition, fabrication strategy, structural architecture, release behavior, and functional modules accordingly. Table 3 summarizes representative application scenarios, biological or clinical challenges, required HM properties, design strategies, and translational considerations. Importantly, the table is intended as an interpretive synthesis of the bibliometric, disease-landscape, and LDA findings, supplemented by relevant literature, rather than as a quantitative ranking of application areas or evidence of established clinical efficacy. This framework may help translate the bibliometric and topic modeling findings into practical guidance for structure-informed HM-based polymeric biomaterial design, while also emphasizing that application-specific validation is essential before these systems can be considered mature for biomedical use.

Table 4.

(continued). Representative biomedical application scenarios, micro/nano-scale design requirements, and validation considerations for HM-based platforms synthesized from the present analysis and relevant literature

Application Scenario	Biological/Clinical Challenge	Required Micro/Nano-Scale Properties	Representative Nano-Enabled Design Strategies	Characterization and Translational Bottlenecks
Organ-on-a-chip/disease modeling	Reconstructing tissue microenvironments	ECM-mimetic, tunable stiffness, modularity	Cell-laden microgels, multicompartmental HMs	Standardization, reproducibility, model validation
Controlled-release and long-acting delivery	Burst release, short exposure, unstable release profile	High loading, controlled diffusion, predictable degradation, formulation stability	PLGA/alginate/chitosan HMs, thermosensitive HMs, core–shell HMs	Release reproducibility, IVIVC, scale-up, storage stability
Bioimaging and biosensing	Signal stability, tissue penetration, biological background interference	Stable labeling, responsive interfaces, reproducible particle size, nanocomposite integration	Fluorescent/magnetic HMs, nanoparticle-integrated HMs, stimuli-responsive microgels	Sensitivity, specificity, calibration, long-term signal stability, in vivo tracking

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Manufacturing, standardization, and validation bottlenecks for polymeric HM platforms

The thematic and disciplinary transitions identified in this study point to several translational opportunities for biomedical HM research. Their injectable format, modular architecture, tunable physicochemical properties, and capacity for localized cargo delivery make HMs attractive platforms for minimally invasive and disease-specific interventions [15, 22]. In particular, the increasing prominence of wound healing, OA, cartilage-related diseases, IDD, and bone/cartilage regeneration suggests that HMs are being increasingly positioned in clinical scenarios where local retention, controlled release, tissue conformability, and microenvironment modulation are central requirements [96, 104, 105]. In these settings, HMs may provide advantages over conventional systemic administration or bulk hydrogel implantation by improving local delivery efficiency, reducing treatment invasiveness, and enabling the integration of multiple therapeutic functions within a single material platform.

One important translational opportunity lies in the development of HMs as modular systems for localized combination therapy [102]. Because microspheres with different compositions, sizes, structures, and payloads can be designed separately and then combined, HMs provide a flexible strategy for constructing heterogeneous therapeutic systems [106, 107]. This feature is particularly relevant for complex tissue repair, where inflammation control, antimicrobial activity, angiogenesis, cell recruitment, matrix remodeling, and mechanical support may need to be coordinated over time. In addition, porous, core–shell, multicompartmental, nanoparticle-integrated, and stimuli-responsive HMs offer opportunities to spatially separate incompatible cargos, reduce burst release, and match release behavior to pathological cues. Such modular design may help shift HM systems from single-function carriers toward programmable therapeutic platforms for regenerative medicine and disease-contextualized repair [79, 80].

Another emerging opportunity is the integration of HMs with organoids, organ-on-a-chip systems, and other new approach methodologies (NAMs) [21, 108, 109]. Organoid models are also increasingly being explored in rheumatic and immune-mediated diseases as disease-relevant in vitro systems for investigating pathological processes and therapeutic responses [110, 111]. These platforms may help bridge the gap between simplified in vitro assays and animal models by providing human-relevant microphysiological environments for disease modeling and therapeutic evaluation [112]. In HM research, such systems may further provide physiologically relevant platforms for evaluating HM behavior, drug release, cell–matrix interactions, and tissue-specific responses. HMs are particularly suitable for such systems because their size, stiffness, degradation, surface chemistry, and cell-loading capacity can be tuned to reconstruct ECM-like niches and modular three-dimensional microtissues. Recent regulatory initiatives have encouraged the use of human-relevant NAMs, including organ-on-a-chip systems, computational modeling, advanced in vitro assays, artificial intelligence (AI)-based toxicity models, and organoid-based testing, to reduce or replace some animal testing in preclinical safety assessment [113, 114]. In this context, HM-integrated organoid or organ-on-a-chip systems may become useful tools for preclinical screening, disease modeling, and translational validation, although standardization, reproducibility, functional benchmarking, and correlation with in vivo and clinical outcomes remain essential.

The increasing complexity of these human-relevant model systems also creates opportunities for data-driven design and analysis. AI- and machine learning-assisted design may further support the optimization of HM-based biomaterial platforms [115–118]. Data-driven models could be used to relate polymer composition, crosslinking conditions, particle size, porosity, stiffness, degradation behavior, and release profiles to biological outcomes, thereby supporting high-throughput screening, release-kinetics prediction, mechanical-property optimization, and image-based analysis in complex in vitro systems [119]. However, these tools should be regarded as enabling methods rather than evidence of translational readiness; their value will depend on high-quality datasets, transparent model reporting, experimental validation, and integration with disease-relevant biological models.

Despite these opportunities, manufacturing remains one of the major bottlenecks for clinical translation. Current fabrication strategies, including batch emulsion, microfluidics, electrohydrodynamic spraying, lithography, three-dimensional printing, and mechanical fragmentation, each involve trade-offs between precision, productivity, cell compatibility, structural complexity, and scalability [20, 21]. Microfluidic methods can generate highly uniform and controllable microspheres, but they often require specialized equipment, have relatively low throughput, and may suffer from channel blockage during continuous production [9]. Batch emulsification and electrohydrodynamic spraying methods may offer higher productivity or better encapsulation efficiency, but they often produce broader size distributions and may require careful removal of residual oil, surfactants, initiators, or crosslinking agents [45]. Lithography enables high-resolution control but is limited by material compatibility and production efficiency, whereas mechanical fragmentation is simple and fast but often yields irregular and polydisperse particles [120–122]. These fabrication constraints indicate that a central challenge for HM translation is to reconcile laboratory-level design precision with industrial-scale reproducibility and cost-effective production.

Standardized characterization is another essential requirement. For clinical use, HMs cannot be evaluated only by morphology or proof-of-concept biological effects. Critical quality attributes should include particle-size distribution, shape uniformity, swelling behavior, porosity, stiffness, degradation rate, injectability, cargo-loading efficiency, release kinetics, sterilization compatibility, storage stability, and batch-to-batch consistency. These parameters are not merely technical details; they directly affect tissue distribution, residence time, drug-release behavior, cellular compatibility, and therapeutic reproducibility. For example, particle size can influence injectability, early burst release, sustained release behavior, and suitability for cell encapsulation. In intra-articular applications, particle size and surface properties may also affect joint-cavity distribution, lubrication, and penetration into cartilage-related barriers [123–125]. Therefore, future studies should move toward application-specific characterization standards rather than relying on generic material descriptions.

Safety and regulatory complexity also require more attention. As HMs evolve from simple carriers to multifunctional systems containing drugs, proteins, cells, extracellular vesicles, nanoparticles, or stimuli-responsive components, their safety evaluation becomes increasingly complex [126]. Potential concerns include residual reagents from fabrication, cytotoxicity of crosslinking systems, immunogenicity of natural polymers or biological cargos, inflammatory responses, degradation-product toxicity, uncontrolled long-term retention, and off-target distribution [127]. These issues are particularly important for injectable and intra-articular applications, where materials may remain in confined anatomical spaces and interact with synovium, cartilage, immune cells, or local ECM over extended periods [128]. Moreover, multifunctional HMs may fall into regulatory categories that overlap materials, devices, drugs, and biological products, making translational pathways more complicated than those for conventional small-molecule formulations or simple biomaterials.

Disease-specific validation represents a further bottleneck. The rise of OA, wound healing, cartilage regeneration, bone regeneration, and IDD in this study indicates strong research interest, but most disease-contextualized HM systems remain in preclinical or early translational stages. For OA, pathological heterogeneity, disease stage, joint mechanics, synovial-fluid turnover, cartilage permeability, oxidative stress, inflammatory mediators, and enzymatic degradation can all affect HM behavior and therapeutic performance [18, 22, 24]. Responsive HMs designed to react to pH, ROS, enzymes, temperature, ultrasound, or other stimuli may perform differently across disease stages or tissue microenvironments [12]. Similarly, wound healing, bone repair, and IDD involve distinct mechanical, vascular, inflammatory, and cellular contexts [62, 97, 129, 130]. Therefore, future studies should avoid treating “regeneration” as a single generic outcome and should instead establish disease-specific evaluation systems, including relevant animal models, long-term efficacy assessment, biodistribution studies, degradation tracking, safety evaluation, and clinically meaningful functional endpoints.

Taken together, the translational future of biomedical HMs will depend on whether material innovation can be integrated with manufacturability, standardized characterization, long-term safety, disease-specific validation, and regulatory feasibility. Although the present bibliometric and LDA results suggest a shift toward application-oriented and regenerative themes, this shift now needs to be matched by rigorous translational frameworks. Future progress will require scalable manufacturing platforms, standardized reporting of key material parameters, quality-control criteria for complex HM systems, clinically relevant disease models, and closer collaboration among materials scientists, engineers, clinicians, regulatory experts, and industry partners. These design requirements, application scenarios, and translational bottlenecks are integrated into a data-driven roadmap for HM-based regenerative biomedical platforms (Fig. 12).

Fig. 12 — Data-Driven Roadmap for HM-Based Pharmaceutical Delivery and Formulation Platforms. This roadmap was derived from the scientometric and topic modeling analyses of biomedical HMs. Representative fabrication strategies include emulsion, microfluidics, electrospray, three-dimensional printing, and lithography, which enable porous, core–shell, cell-laden, nanoparticle-integrated, and stimuli-responsive HM architectures. These platforms support controlled release, localized delivery, long-acting administration, thermosensitive release, bioactive-factor delivery, lubrication, antibacterial activity, immune modulation, and tissue-regenerative functions. Key application scenarios include wound repair, OA, IDD, bone/cartilage regeneration, tumor embolization, and organ-on-a-chip modeling. Major development challenges include formulation reproducibility, release-profile control, scalable manufacturing, storage stability, quality evaluation, safety, regulatory evaluation, and disease-specific validation. Generated using Figdraw (https://www.figdraw.com/static/index.html#/)

Strengths and limitations

This study has several strengths. First, previous bibliometric studies have examined hydrogels either as a broad biomedical material category or within specific disease settings, such as spinal cord injury. In contrast, the present study focuses specifically on HMs as a distinct microscale biomaterial platform across biomedical applications. This defined scope enables a more focused characterization of how HM research has evolved at the interface of polymer science, drug delivery, tissue engineering, regenerative medicine, and disease-contextualized applications. Second, by integrating records from both the WoSCC and PubMed, this study provides broader literature coverage than single-database analyses and captures both citation-rich bibliographic metadata and biomedical indexing information. Third, the study combines conventional bibliometric mapping with LDA-based topic modeling. This integrated framework enables the analysis to move beyond publication counts, collaboration networks, and keyword co-occurrence, and to further reveal latent thematic structures and their temporal evolution. In this way, the study provides not only a descriptive map of the field, but also an analytical perspective on changing thematic patterns from early formulation- and release-centered studies toward increasingly investigated application-oriented themes such as wound healing, tissue engineering, OA, cartilage repair, and bone/cartilage regeneration. Finally, the inclusion of disease-associated mapping helps connect bibliometric trends with potential translational contexts, thereby offering a useful reference for researchers seeking to identify emerging biomedical application areas for HMs.

Several limitations should also be acknowledged. First, although this study used both WoSCC and PubMed, it was restricted to English-language articles and reviews. Relevant studies indexed in other databases, such as Scopus, Embase, China National Knowledge Infrastructure, SciFinder, or regional databases, as well as non-English publications and gray literature, may have been underrepresented. Although the dataset construction was strengthened through database triangulation and systematic retrieval procedures, residual selection bias may remain due to database coverage limitations, indexing differences, and terminology heterogeneity. In addition, publication-type filtering was not fully symmetric across the two databases because the PubMed search excluded records indexed as reviews, whereas the WoSCC search did not apply an equivalent review exclusion. Consequently, PubMed-only review articles may have been underrepresented in the merged dataset, representing a potential source of database-specific coverage bias. Furthermore, because database indexing may lag behind actual publication dates, the 2025 publication count should be interpreted as the indexed output available at the search date rather than a definitive census of all publications from 2025. Therefore, the findings should be interpreted as reflecting the English-language research landscape indexed in WoSCC and PubMed rather than a complete census of global biomedical HM research. Second, the search strategy was designed to balance sensitivity and specificity, and it included related particle terms such as microspheres, microparticles, microgels, beads, and microbeads. Nevertheless, terminology variation remains unavoidable. Studies using less common or less standardized expressions, such as injectable hydrogel carriers, responsive hydrogel depots, or disease-specific hydrogel delivery systems without explicit particle-related terms, may still have been incompletely captured or may have introduced conceptual heterogeneity. Third, bibliometric indicators, including publication counts, citation frequency, co-citation strength, total link strength, and citation bursts, reflect scholarly visibility and research attention rather than methodological quality, therapeutic efficacy, or clinical validity. In addition, original articles and review articles present in the final merged dataset were analyzed within the same bibliometric framework to characterize the overall development landscape of biomedical HMs. Although publication types were considered during dataset characterization, differences in citation patterns, knowledge dissemination, and thematic emphasis between these publication categories may introduce potential heterogeneity. Future studies may further investigate publication-type-specific research trends. Citation-based results should therefore be interpreted with contextual caution. Fourth, the results of network visualization, clustering, and burst detection are partly dependent on software selection, parameter settings, threshold definitions, and time-slicing strategies. Different analytical settings may produce slightly different network structures or cluster boundaries. Although keyword normalization was performed conservatively, residual lexical variants or unmerged synonyms may remain and could modestly influence keyword frequencies, cluster boundaries, or burst detection. Fifth, author- and institution-level analyses may be affected by residual name ambiguity. This limitation is particularly relevant for Chinese names, where common surnames, pinyin transliteration, abbreviated given names, and name-order variation may lead to author conflation or splitting. Future studies incorporating ORCID, ResearcherID, Scopus Author ID, or other persistent identifiers may improve disambiguation accuracy. Sixth, disease-associated analyses were based on automated text mining and co-occurrence mapping. As a result, disease terms should be interpreted as indicators of research attention rather than direct evidence of therapeutic effectiveness or translational maturity. Finally, LDA topic modeling has inherent methodological assumptions. It relies on a bag-of-words framework and does not fully capture word order, syntax, or deep semantic context. In this study, topic selection was supported by coherence, perplexity, and interpretability assessments; however, the identified topic structure may still vary depending on corpus composition, preprocessing strategies, and model parameter settings. Therefore, although the nine-topic solution provided a coherent and interpretable representation of the biomedical HM literature, it should not be regarded as the only possible semantic organization of the field. Accordingly, specialized or recently emerging fronts, such as granular biofabrication and HM-based immunotherapy, may be represented within broader latent themes without appearing as independent LDA topics or high-ranking keyword bursts. Because alternative topic modeling frameworks were not implemented in the present study, model-independent stability of the nine-topic solution cannot be established. Topic number selection, topic labeling, and cluster-level interpretation also involve a degree of expert judgment, even when guided by quantitative metrics and representative documents. Future studies incorporating alternative natural language processing approaches, including embedding-based topic models such as BERTopic, may further assess, refine, or extend the semantic structure identified in this study. In addition, PCA-based topic visualization provides a simplified two-dimensional projection of a higher-dimensional topic structure and should be interpreted as an exploratory representation rather than a definitive semantic map. Future studies incorporating multi-database retrieval, multilingual corpora, persistent author identifiers, patent and clinical trial data, and more advanced natural language processing or AI-assisted literature-mining methods may further characterize and extend the trends identified here [131].

Conclusion

This bibliometric and topic modeling study systematically mapped biomedical HM research from 2000 to 2025. The findings reveal a rapidly expanding and interdisciplinary field characterized by evolving material design strategies, diverse biomedical applications, and increasing integration of functional features. The findings highlight the diversification of biomedical HM platforms toward multifunctional designs that integrate material engineering, biological regulation, and application-oriented strategies. Nevertheless, these trends should be interpreted as indicators of research attention and platform positioning rather than evidence of established clinical efficacy or translational readiness. Further advancement of HM platforms will require continued efforts in scalable manufacturing, standardized characterization, long-term safety assessment, disease-specific validation, and closer collaboration among materials scientists, engineers, clinicians, and regulatory stakeholders.

Supplementary information

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(348.1KB, docx)}

Acknowledgements

We acknowledge the researchers whose contributions have helped advance this field. We also thank Home for Researchers (https://www.home-for-researchers.com) for language-editing support and gratefully recognize the funding agencies for their assistance. Figs. 1 and 12, and the graphical abstract were created with Figdraw (https://www.figdraw.com/static/index.html#/).

Abbreviations

AI: Artificial Intelligence
CSV: Comma-separated Value
ECM: Extracellular Matrix
HMs: Hydrogel Microspheres
IDD: Intervertebral Disc Degeneration
LDA: Latent Dirichlet Allocation
MeSH: New Approach Methodologies
OA: Osteoarthritis
ORCID: Open Researcher and Contributor Identifier
PCA: Principal Component Analysis
ROS: Reactive Oxygen Species
TS: Topic field
WoSCC: Web of Science Core Collection
3D: Three-dimensional

Author contributions

Siyang Cao: Conceptualisation, formal analysis, writing – original draft, and funding acquisition.Deren Lan and Yihao Wei: Visualization and validation.Guoqing Li: Data curation.Qiuyu He and Ruijie Chen: Investigation, methodology, and software.Mengjie Dong and Yongsheng Zhao: Writing – review and editing; resources.Hua Zhang: Project administration and supervision.

Funding

This research was supported by the National Natural Science Foundation of China (No. 82604653), the Fujian Provincial Natural Science Foundation of China (No. 2026J008155), the Outstanding Young Doctorates Scientific Research Project of Fujian Medical University Union Hospital (No. 2025XH031), and the Investigator Initiation Fund Project of Fujian Medical University Union Hospital (No. 2025XH066). The funders had no role in the study design; data collection, analysis, or interpretation; preparation of the manuscript; or the decision to submit the article for publication.

Data availability

The search strategies are provided in the Supplementary Materials. The processed bibliographic dataset, LDA outputs, and analysis files are available from the corresponding author upon reasonable request.

Declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Siyang Cao, Deren Lan and Yihao Wei contributed equally to this work.

Contributor Information

Guoqing Li, Email: guoqingli@pku.edu.cn.

Mengjie Dong, Email: dongmengjie@126.com.

Yongsheng Zhao, Email: zhaoys69@126.com.

Hua Zhang, Email: zhanghua@fjmu.edu.cn.

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PERMALINK

Mapping biomedical hydrogel microspheres through scientometric and topic modeling analysis of emerging nano-enabled design strategies

Siyang Cao

Deren Lan

Yihao Wei

Qiuyu He

Ruijie Chen

Guoqing Li

Mengjie Dong

Yongsheng Zhao

Hua Zhang

Abstract

Supplementary Information

Introduction

Fig. 1.

Methods

Data sources and retrieval strategy

Inclusion and exclusion criteria

Data analysis

Topic modeling analysis

Results

Publication growth trends

Fig. 2.

Knowledge production and collaboration landscape

Country/region distribution and collaboration

Fig. 3.

Institutional distribution and collaboration network

Fig. 4.

Author distribution and citation bursts

Fig. 5.

Journals, disciplinary fields, and co-cited references

Journals and citation analysis

Fig. 6.

Intellectual base and knowledge evolution: co-citation structure

Table 1.

Fig. 7.

Keyword co-occurrence, burst detection, and research frontiers

Fig. 8.

Biomedical application landscape and emerging nano-enabled design trends of HMs

Fig. 9.

LDA-based topic modeling of latent research themes

Table 2.

Fig. 10.

Cluster-level topic evolution of hm delivery, analysis, and biomedical applications

Fig. 11.

Discussion

Principal scientometric and topic modeling findings in biomedical HM research

From controlled release to disease-contextualized biomedical HM platforms

Disciplinary convergence between polymer science, biomaterials, and biomedical engineering

Application-oriented requirements for biomedical HM platforms

Table 3.

Table 4.

Manufacturing, standardization, and validation bottlenecks for polymeric HM platforms

Fig. 12.

Strengths and limitations

Conclusion

Supplementary information

Acknowledgements

Abbreviations

Author contributions

Funding

Data availability

Declarations

Ethics approval

Consent to participate

Consent to publish

Competing interests

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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