To explore the molecular mechanism of IRF7 involved in acute kidney injury in sepsis based on proteomics

Li Xiang; Ma Wanli; Song Jiannan; Hu Zhanfei; Zhou Qi; Li Haibo

doi:10.1186/s12953-025-00244-5

. 2025 Jul 17;23:6. doi: 10.1186/s12953-025-00244-5

To explore the molecular mechanism of IRF7 involved in acute kidney injury in sepsis based on proteomics

Li Xiang ^1,^#, Ma Wanli ^2,^#, Song Jiannan ², Hu Zhanfei ², Zhou Qi ^2,^✉, Li Haibo ^2,^✉

PMCID: PMC12273476 PMID: 40676657

Abstract

Background

Acute kidney injury is a common complication of sepsis, and its mechanism is very complicated. The purpose of this study was to investigate the mechanism of key differentially expressed proteins and their related signaling pathways in the occurrence and development of acute kidney injury in sepsis through proteomics.

Methods

Acute kidney injury was induced by intraperitoneal injection of lipopolysaccharide at 10 mg/kg. Renal tissues were analyzed by TMT quantitative proteomic analysis. Differentially expressed proteins (DEPs) were screened. Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network analysis were performed.

Results

We obtained 530 DEPs. GO analysis showed that the biological process of DEPs was mainly stress response. The molecular functions of DEPs mainly focus on catalytic activity. The cellular components of DEPs were mainly located in the intracellular and cytoplasm. KEGG analysis showed that DEPs were mainly involved in metabolic pathways. Ten key proteins with interaction degree, such as Isg15, Irf7, Oasl2, Ifit3, Apob, Oasl, Ube2l6, Ifit2, Ifih1 and Ifit1 were identified. Irf7 was significantly up-regulated in rat kidney tissues.

Conclusion

The upregulation of Irf7 plays an important role in the mechanism of acute renal injury induced by sepsis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12953-025-00244-5.

Keywords: Sepsis, Acute kidney injury, Bioinformatics, Proteomics, Irf7

Introduction

Sepsis is an organ dysfunction caused by the host’s dysfunctional response to infection [1]. Sepsis affects millions of people worldwide each year and, if not detected and treated early, can progress to septic shock, multiple organ failure and even death, with a mortality rate of between one in three and one in six [2]. In the course of occurrence and development, sepsis can lead to acute functional injury of many organs in the body, and acute kidney injury (AKI) is one of the common complications of sepsis. The incidence of AKI in patients with sepsis is as high as 60%, with poor prognosis and mortality as high as 35% [3]. Therefore, it is important to fully understand the occurrence and development mechanism of septic AKI.

However, the molecular mechanisms underlying septic AKI are complex, including overactivated inflammatory responses, NLRP3 inflammasome, programmed cell death, mitochondrial dysfunction, and metabolic reprogramming [4, 5]. In the occurrence of septic AKI, inflammation is the main defense mechanism of the host against infection, and the overactivity of NLRP3 inflammasome can induce an inflammatory cascade that aggravates septic AKI [6]. Studies have shown that Caspase-11 and NLRP3 inflammasome may play an important role in renal tubular epithelial pyroptosis and septic AKI [7, 8]. In addition, autophagy levels in septic AKI also play a dual role in cell survival and cell death, with different signaling molecules and pathways affecting the expression of autophagy related genes, leading to an increase or decrease in autophagy [9].

Although there has been great progress in the study of the molecular mechanism of sepsis AKI in recent years, many signaling pathways have been confirmed to play an important role in sepsis AKI, but the specific links between the upstream and downstream of these signaling pathways are still unclear, and many important key proteins need to be further studied.

Interferon regulatory factor 7 (Irf7) is a member of the interferon regulatory factors (Irfs) family. Irf7 promotes the development of inflammation and plays an important role in immunity [10]. Irf7is located downstream of pattern recognition receptors (PRRs) mediated signaling pathway that positively regulates the production of the molecule type I interferon (IFN-I). The positive feedback loop formed by Irf7-IFN-I produces a large amount of IFN, which ultimately fights infection by pathogenic microorganisms. Irf7 is also essential for the induction of IFN-α/β gene expression via the viral-activated myeloid differentiation primary response88 (MyD88) independent pathway and the Toll-like receptors (TLRs)-activated MyD88-dependent pathway [11]. Irf7 is a multifunctional transcription factor that plays a pro-inflammatory or anti-inflammatory role in different inflammatory diseases through multiple signaling pathways. Irf7 inhibits pro-inflammatory cytokines and promotes anti-inflammatory cytokine IL-1β [12]. Irf7 was also able to inhibit NF-κB activation, resulting in significantly lower levels of pro-inflammatory cytokines such as TNF-α and IL-6 [13]. These studies suggest that Irf7 is closely related to important molecular mechanisms of sepsis.

In recent years, mass spectrometry technology has been rapidly developed, proteomics has become a powerful tool to study pathogenesis and identify potential biomarkers [14]. The qualitative and quantitative differences of protein can reflect the pathological status of sepsis. The identification and quantitative analysis of a large number of proteins through proteomics is a necessary way to further understand the pathogenesis of sepsis [15]. Sepsis has complex features at the molecular level, and it is necessary to obtain a global picture of the AKI proteome of sepsis in order to search for pathways and proteins that play key roles in the pathogenesis. The improvement of the stability of mass spectrometers and the improvement of quantitative experimental methods have made the identification and quantification of proteins and peptides in tissues more accurate. Due to its high sensitivity and specificity, proteomics using mass spectrometry is an effective way to search for key differential proteins in septic AKI [16].

In this study, we used the sepsis rat model to explore the proteins that play an important role in the pathogenesis of sepsis AKI through proteomic techniques, and analyzed the molecular mechanism of sepsis AKI through their participation in signaling pathways and the connections between upstream and downstream molecules.

Methods

Animal and septic AKI model

Twelve healthy and clean SD male rats, aged 6–8 weeks, weight 162–179 g, Animal Ethics number (CFMH-LAEC-202311-01). The room temperature is maintained at 20 ~ 24℃, the relative humidity is 40% ~ 60%, the light/dark cycle is 12 h, and the water is free to eat. The rats were divided into 2 groups (n = 6) by random number table method: normal group (C group) and sepsis group (S group). The lipopolysaccharide (Solarbio, L8880) in our study was dissolved in 0.9% normal saline at an intraperitoneal dose of 10 mg/kg. The rat model of sepsis was established in S group by intraperitoneal injection of lipopolysaccharide 10 mg/kg. C group was treated with intraperitoneal injection of the same dose of normal saline. The lipopolysaccharide was molded and killed for 12 h. The blood and kidney tissues of the eyeballs of the rats were collected for subsequent experiments. In the sepsis model, the RIFLE injury grade of acute renal dysfunction was used as the standard [17], that is, the serum creatinine level in S group increased to twice that in C group.

Renal histological evaluation

The rat kidney tissue was fixed with 4% paraformaldehyde and embedded in paraffin, and the sections were stained with hematoxylin and eosin (HE) to evaluate the kidney injury. The tissue sections stained by HE were observed under microscope (×20). Look at the slide with a microscope. Tubular injury was defined as tubular edge loss, tubular dilation/flattening, tubular degeneration and vacuolation. Tissue damage was scored on a 0–4 scale, with 0–4 representing 0%, < 25%, 26–50%, 51–75%, and > 76% tubular damage, respectively [18]. Renal pathological injury was scored by 3 independent pathologists in the Department of Pathology of our hospital.

Proteomic data collection

Renal tissue samples were taken from rats and cracked. Appropriate amount of RIPA working liquid was added to each group of samples, then the samples were ground at low temperature, followed by ice bath ultrasound with cell ultrasonic fragmentation apparatus, and fully cracked and centrifuged. Total protein was extracted and quantified by bicinchoninic acid (BCA) method. The absorbance was measured with 562nm wavelength. The standard curve was fitted according to the standard protein and the protein concentration of the corresponding sample was calculated. Trypsin enzymolysis protein, the maximum allowable number of missing cuts during enzymolysis is 2. The labeled polypeptide samples were obtained according to the instructions of Thermo’ s TMT labeling kit. 2 µg total peptides were separated from each sample by nano-UPLC liquid phase system EASY-nLC1200, and then data were collected by mass spectrometer equipped with nanolitre ion source (Q Exactive HFX). The data dependent acquisition mode was used for mass spectrometry analysis. The total analysis time was 90 min and the positive ion detection mode was adopted. Raw data files use Proteome Discoverer software (version2.4.0.305, Thermo Fisher Scientific) and built-in Sequest HT search engine search library analysis. For the identification of proteins and peptides, the FDR is set to ≤ 1%.

Proteomics database analysis

Fold change ≤ 0.83 or fold change ≥ 1.2 of TMT items were used as screening criteria, and p-value < 0.05 of Student ‘s t-test was used as screening criteria for differentially expressed proteins (DEPs). After obtaining DEPs between C group and S group, these proteins were annotated by Gene Ontology (GO) function and analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment [19]. GO recognizes three aspects of biology: cellular component (CC), biological process(BP), and molecular function (MF), and is of great significance in exploring biological functions. KEGG analysis was used to explore which potential signaling pathways were involved in DEPs changes. The R software package “clusterProfiler” was used for enrichment analysis of GO and KEGG pathways in DEPs, and p < 0.05 was considered statistically significant. Protein-protein interaction (PPI) network analysis of DEPs was performed through STRING (https://cn.string-db.org/) database to screen hub proteins [20].

We used the gene expression set GSE95233 downloaded from the GEO (http://www.ncbi.nlm.nih.gov/geo/) database to analyze the expression of the corresponding differential genes of hub proteins to verify the accuracy of the rat database we established.

Western blot verification of PPI hub protein

Another 6 SD rats were divided into 2 groups (n = 3) by random number table method: normal group and sepsis group. The molding treatment method is the same as before. Kidney tissue samples were taken from each group and put into a clean centrifuge tube. RIPA lysate was used to crack them. The total protein concentration was determined by BCA method, and the protein concentration was adjusted to 1 mg/ml. The samples were boiled at 100℃ for 5 min, electrophoreted on a 10%SDS-PAGE (120 V), and transferred to a 0.45 μm PVDF membrane. The membrane was sealed with 5% skim milk powder at room temperature for 2 h, and then incubated with primary antibody at 4℃ overnight. The primary antibodies Irf7, Isg15, Ifit3, Ube2l6, Ifit2, Ifih1, Ifit1 and β-actin were diluted at 1:1000, 1:500, 1:500, 1:1000, 1:500, 1:1000 and 1:1000, respectively. After removal, it was incubated with secondary antibody (1:5000) for 1 h. Western blots were visualized by ECL and analyzed by ImageJ software.

Statistical analysis

Statistical analysis was performed by GraphPadPrism9.5.1 software. Measurements conforming to the normal distribution are expressed as mean ± standard deviation (x ± s). The difference between the two groups was determined by two-independent sample t test, and p < 0.05 was considered statistically significant.

Results

Establish the AKI model of sepsis in rats

HE staining showed that the kidney tissue of C group (Fig. 1A) had no abnormalities, swelling, degeneration, necrosis, and inflammatory cell infiltration. In S group (Fig. 1B), renal histopathologic changes were aggravated, renal tubule dilatation and vacuolar degeneration. Renal tubule injury score was increased (Fig. 1C), which was scored by 3 pathologists in our hospital. Serum creatinine and blood urea nitrogen were estimated. The levels of serum creatinine (Fig. 1D) and blood urea nitrogen (Fig. 1E) in S group were twice as high as those in C group, indicating that the AKI model of rat sepsis was successfully established.

Principal component analysis (PCA)

PCA is a mathematical dimensionality reduction method that is commonly used to reflect differences within and between groups of samples. We use R (version3.6.3) for logarithmic and centralized processing of C group and S Group data, and then conduct modeling analysis (Fig. 2A). Results There was a difference between C group and S group. The horizontal and vertical coordinates PC[1] and PC[2] represent the scores of the first and second principal components, respectively, with each scatter representing a sample, and the color and shape of the scatter representing different groupings. Within the 95% confidence interval (Hotelling ‘s T-squared ellipse) of the samples, there was an inter-sample difference between the rats in S group and C group.

Fig. 2 — Correlation analysis of DEPs between C group and S group. **(A)** Two-dimensional diagram of principal component analysis of DEPs. **(B)** DEPs volcano map. **(C)** Layer cluster analysis heat map of DEPs. **(D)** Pie chart of DEPs subcellular localization analysis. **(E)** KOG analysis histogram of DEPs

Screening and layer cluster analysis of deps

The kidney tissues of rats in C group and S group were collected for quantitative analysis of TMT protein. With the p-value < 0.05 of Student ‘s t-test and fold change ≤ 0.83 or fold change ≥ 1.2 as screening criteria, 530 DEPs in C group and S group were identified, among which 280 upregulated proteins (red nodes in Fig. 2B). 250 down-regulated proteins (blue nodes in Fig. 2B).

We screened the top 10 up-regulated and down-regulated DEPs with fold change value for hierarchical cluster analysis and visualized them with heat maps. In the figure (Fig. 2C), the horizontal coordinate is C group and S group, the vertical coordinate represents the corresponding DEPs in this group, and the color blocks at different positions represent the relative expression of proteins at corresponding positions, with red representing high expression and blue representing low expression.

Subcellular localization analysis and eukaryotic orthologous groups (KOG) of proteins

Bioinformatics is used to predict the subcellular location and possible function of proteins. The results of subcellular localization analysis of DEPs were visualized in pie chart form (Fig. 2D). The results showed that DEPs mainly distributed in the nucleus, followed by cytoplasm, plasma membrane and extracellular. The KOG analysis was performed on DEPs, and the results were visualized in the form of histograms. The results showed that the functions of the analyzed DEPs (Fig. 2E) were mainly involved in the signal transduction mechanism (T), General function prediction (R), Post-translational modification, protein turnover, chaperone protein (O).

GO analysis and KEGG analysis of deps

We analyzed the biological effects of 530 DEPs in BP, CC and MF concentrations. The results of GO enrichment analysis showed (Fig. 3A) that in BP, the main pathways were stress response, response to organic substance, response to external stimuli, and response to bacteria and innate immunity. CC was mainly enriched in cytoplasm. MF is mainly involved in catalytic activity, small molecule binding, hydrolase activity, transporter activity and oxidoreductase activity.

We analyzed the metabolic pathways with significant DEPs enrichment and presented the results of the pathway enrichment analysis in the form of bubble map. KEGG analysis results showed (Fig. 3B) that the top ten pathways were metabolic pathways, human papillomavirus infection, influenza A pathway, measles, complement and coagulation cascade, amebiasis, glutathione pathway, malaria, African trypanosomiasis and collecting duct acid secretion.

PPI analysis of deps and Western blot

Based on the interaction between DEPs, we constructed the PPI diagram of DEPs (Fig. 4A). The 10 proteins with the highest interaction scores in the subnetwork were selected for display (Fig. 4B), namely Isg15, Irf7, Oasl2, Ifit3, Apob, Oasl, Ube2l6, Ifit2, Ifih1, and Ifit1.

We selected Irf7, Isg15, Ifit3, Ube2l6, Ifit2, Ifih1 and Ifit1 for Western blot verification, and the results showed that Irf7 was significantly up-regulated in the kidney tissues of AKI rats with sepsis (Fig. 4C and D), while the expression levels of other proteins were not significantly different (Fig. 4E). At the same time, we validated the GEO gene set GSE95233 (22 normal patients and 51 sepsis patients). The results showed that Irf7 was significantly upregulated, which also confirmed our verification results (Fig. 4F and G).

Discussion

In recent years, septic AKI accounts for 45–70% of all critically ill AKI cases, and its mortality rate remains high. Proteomic analysis can enable us to understand the molecular mechanisms of disease occurrence and progression, provide new ideas for identifying potential diagnostic biomarkers, and can also be a potential target for the treatment of septic AKI. In this study, a rat model of sepsis AKI was established. Heat map (Fig. 2C) shows the standardized expression of DEPs in sepsis. Irf7 is highlighted for its significant upregulation, suggesting its role in inflammation and immune regulation in septic AKI. Ifit2, Ifit3 and Ptx3 are noted for their involvement in innate immunity and inflammatory responses, which align with the pathophysiology of sepsis-induced AKI. Cyp27b1 and Neil3 are discussed for their potential roles in cellular stress response and DNA repair mechanisms, which may contribute to kidney injury during sepsis. Ten key proteins with interaction degree, such as Isg15, Irf7, Oasl2, Ifit3, Apob, Oasl, Ube2l6, Ifit2, Ifih1 and Ifit1 were identified. We demonstrated that Irf7 was significantly up-regulated in rat renal tissues.

Irf and its effectors are closely related to the balance of immunosuppression, and Irf is a key mediator of signal transduction related to host immune response and immune regulation. Immunosuppression also plays a key role in sepsis induced organ failure. Irf is an important regulatory protein in TLR and IFN signaling pathways. Studies have shown that Irf7 is a key regulator of type I interferon against pathogen infection. Various pathogen-associated molecular patterns (PAMPs) and damage associated molecular patterns are sensed by innate pattern recognition receptors, including TLRs, RIG-I-like receptors, C-type lectin receptors and NOD-like receptors [21]. Our KEGG analysis also confirmed that Irf7 plays an important role in TLR (map04620), RIG-I-like receptor (map04622) and NOD-like receptor (map04621) signaling pathways. Irf also contributes to the action of TLR. After PAMPs bind to TLRs or IFN binds to IFN receptors, a signaling cascade causes Irfs to activate and relocate to the nucleus, where they activate gene expression. As such TLR4 downstream signaling pathways either work in a manner dependent on the universal adapter protein called MyD88, or in a MyD88-independent way. In the MyD88-dependent group of TLR4 signal transduction, NF-κB is able to translocation and induce the expression of proinflammatory cytokine genes, such as TNF-α and IL-6 [10, 22].

Thus, blocking Irf-DNA binding with Irf-specific or pan-Irf inhibitors is a promising tool for the treatment of sepsis. Indirect regulation can be achieved by targeting known activators and regulators of Irf expression and key pathways downstream of Irf. The significant upregulation of Irf7 suggests that it could be a potential biomarker for early diagnosis of septic AKI. Future studies could further validate its functional mechanisms in inflammation and kidney injury by knocking down or overexpressing Irf7 in animal and cellular models. Design experiments prove transcriptional upregulation of Irf7 is regulated upstream by phosphorylation of various signaling proteins. Identify the cell type responsible for Irf7 upregulation and validate its signal transduction pathway. The diagnostic predictive ability of sepsis can also be evaluated by clinical detection of Irf7 levels in the blood, peritoneal fluid, or urine of patients with sepsis. At the same time, the development of specific intervention strategies targeting the Irf7 signaling pathway is expected to provide a new direction for the treatment of septic AKI.

Type I IFN induces the expression of more than 500 genes, which are collectively known as IFN-stimulating genes (Isgs). IRF closely controls transcriptional activation of Isg. Interferon stimulating gene 15 (Isg15) is the earliest interferon-induced gene. The free Isg15 protein synthesized by the Isg15 gene is coupled to the cellular protein after translation and secreted by the cell into the extracellular environment. Type I interferon stimulated the activation of more than 2000 Isg transcripts. Ube2l6 (Isg15/ ubiquitin E2 binding enzyme) is one of the key enzymes of Isg. We now know that the occurrence of sepsis AKI is closely related to autophagy, and the autophagy pathway is up-regulated under cellular stress [23]. Our data show that Ube2l6 and Isg15 protein expression is significantly upregulated in septic AKI renal tissue. Studies have confirmed that the expression level of Isg15mRNA is positively correlated with the occurrence of sepsis and septic shock [24]. Recent studies have shown that Isg15 overexpression can interact with HDAC6 and p62 to promote aggregate formation and thus promote autophagy [25]. Therefore, we hypothesize that decreasing the expression of Isg15 and Ube2l6 may reduce autophagy to alleviate sepsis AKI.

The interferon-induced tetrapeptide repeating protein (Ifit) gene is a prominent Isg. Ifit proteins are involved in a variety of biological processes, including host innate immunity, antiviral immune response, virus-induced translation initiation, replication, double-stranded RNA signaling, and PAMP recognition [26]. Although Ifit protein was originally studied as an antiviral protein, recent studies have shown that its expression in the context of sepsis also has important implications. In this study, Ifit3, Ifit2, and Ifit1 were significantly up-regulated in septic AKI. Alexandra Siegfried’s research showed that Ifit2 is a key signaling medium in LPS-induced septic shock, and the expression of Ifit2 is significantly up-regulated under LPS stimulation in IFN-a receptor and Ifit9-dependent manner, and Ifit3 and Ifit1 are also up-regulated accordingly [27].

In addition, Ifit1, Ifit2, and Ifit3 combine into a stable trimer complex in humans, where Ifit3 enhances and regulates the central hub of Ifit1RNA binding, while Ifit2 also promotes apoptosis. Ifit2-mediated apoptosis acts through the mitochondrial pathway, in which the balance between pro-apoptotic and anti-apoptotic Bcl-2 family proteins regulates the permeability of the mitochondrial outer membrane, and overexpression of Ifit2 leads to activation of Caspase-3 and disruption of plasma membrane asymmetry, which is characteristic of apoptotic cell death [28, 29]. In overexpression studies, Ifit3 co-expression has been shown to improve Ifit2-dependent apoptosis. In addition, Ifit2 is involved in the negative regulation of inflammation by down-regulating Toll-like receptor 4 response and regulating reactive oxygen species production. Ifit family members participate in a variety of pathophysiological processes in the body, regulate the homeostasis and differentiation of a variety of cells, including immune cells, and are closely related to a variety of autoimmune diseases, which is expected to become a new therapeutic target [30]. Therefore, Ifit1, Ifit2 and Ifit3 may be the key nodes in the pathogenesis of sepsis AKI.

In this study, a peritoneal injection of lipopolysaccharide sepsis AKI model was established. The degree of AKI infection in this model can be controlled by the dose of lipopolysaccharide, and the model is stable and reproducible [31]. However, there are some limitations in this study, and our hypothesis based on database calculation needs to be further verified. In future studies, we plan to expand the sample size and validate it in conjunction with other animal models, such as different species or methods of sepsis induction. In the future, we will increase cell experiments and clinical trials to verify various aspects. In this study, proteomics was used to analyze the molecular mechanism of sepsis AKI, which should be combined with genomics, metabolomics, and bioinformatics to construct a global molecular network of sepsis AKI.

Conclusions

In summary, we obtained Isg15, Irf7, Oasl2, Ifit3, Apob, Oasl, Ube2l6, Ifit2, Ifih1, Ifit1 and other proteins that are significantly up-regulated in septic AKI, and verified that Irf7 is significantly up-regulated in renal tissue of septic AKI rats.

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Acknowledgements

Thanks to Chifeng Hospital laboratory teachers for their help in this study. We thank the authors of the GSE95233 dataset for their contributions and the GEO tool for providing the data analysis platform.

Abbreviations

AKI: Acute kidney injury
Irf: Interferon regulatory factor
PRRs: Pattern recognition receptors
IFN: Type I interferon
MyD88: Myeloid differentiation primary response88
TLRs: Toll-like receptors
HE: Hematoxylin and eosin
BCA: Bicinchoninic acid
DEPs: Differentially expressed proteins
GO: Gene Ontology
KEGG: Kyoto Encyclopedia of Genes and Genomes
CC: Cellular component
BP: Biological process
MF: Molecular function
PPI: Protein-protein interaction
PCA: Principal Component Analysis
Isg: IFN-stimulating gene
Ifit: Interferon-induced tetrapeptide repeating protein

Author contributions

LX is responsible for writing papers and completing experiments; Statistical data is the responsibility of MWL; SJN is responsible for reviewing the content of the paper and revising the paper; HZF is responsible for experimental guidance; ZQ is responsible for financial support; LHB is responsible for financial support. All the authors reviewed the manuscript. All authors reviewed the manuscript.

Funding

National Natural Science Fundation(82204895); Inner Mongolia Natural Science Foundation(2022MS08001); Inner Mongolia Natural Science Foundation(2023MS08036).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

This study has been approved by the Management and Ethics of Experimental Animals, Chifeng Hospital Ethics Committee.

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.

Li Xiang and Ma Wanli contributed equally to this work and share first authorship.

Contributor Information

Zhou Qi, Email: zhouqi1020818@sina.com.

Li Haibo, Email: 475857584@qq.com.

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