UF-5000 Atyp.C parameter for urinary screening: Impact of detecting atypical and inclusion-bearing cells

Abstract

Urine sediment examination is vital for detecting atypical urothelial cells but is highly operator-dependent. The UF-5000 automated urine analyzer quantifies nucleic acid-containing particles. The atypical cell (Atyp.C) parameter reflects increased nucleic acid content and detects atypical urothelial cells and intracytoplasmic inclusion-bearing (ICIB) cells linked to viral infection or inflammation. We assessed the relationship between Atyp.C, atypical cells, and ICIBs and evaluated the parameter's screening performance. Overall, 264 urine sediment samples from 203 patients were analyzed using the UF-5000. Manual microscopy was used to identify atypical and ICIB-positive cells. Atyp.C values were compared between groups using the Mann–Whitney U test. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and sensitivity, specificity, and Cohen's κ calculation at cutoffs of 0.1, 0.3, and 0.5 cells/μL. Atyp.C values were significantly higher in atypical-cell-positive specimens (p < 0.001). Atypical cell and ICIB positivity showed moderate agreement (Cohen's κ = 0.534; 77 % agreement; p < 0.01). ROC analysis showed an area under the curve of 0.829 for atypical cells, which increased to 0.895 when ICIB-positive samples were considered positive. At a cutoff of 0.1 cells/μL, Atyp.C exhibited high sensitivity (97.8 %) with low specificity (46.5 %) for detecting atypical cells; a 0.3 cells/μL cutoff provided optimal balance (sensitivity 85.2 %, specificity 68.2 %). The UF-5000 Atyp.C parameter effectively detects cells with increased nucleic acid content, including atypical and ICIB-positive cells. Recognizing ICIBs as diagnostically relevant improves screening sensitivity, supporting Atyp.C as a valuable tool for urinalysis. Combining automated detection with manual microscopy may improve the efficiency of atypical cell detection.

Highlights

• •Atyp.C values were significantly higher in atypical cell-positive urine specimens.
• •Atypical cell and intracytoplasmic inclusion-bearing (ICIB) cell positivity showed moderate agreement.
• •Including ICIB-positive samples improved the diagnostic performance of Atyp.C for detecting atypical cells.
• •A 0.1 cells/μL cutoff provided high sensitivity, while 0.3 cells/μL balanced sensitivity and specificity.
• •Combining automated Atyp.C detection with manual microscopy may enhance atypical cell detection.

1. Introduction

Urinalysis has long been one of the most fundamental examinations in clinical laboratories because of its simplicity and noninvasiveness. Urine sediment examination, performed on unstained or Sternheimer-stained (S-stained) preparations, not only identifies common elements such as red and white blood cells (WBCs), bacteria, casts, and epithelial cells, but also enables the detection of abnormal or atypical urothelial cells suggestive of malignancy and intracytoplasmic inclusion-bearing (ICIB) cells associated with viral infection or degenerative changes (Fig. 1) [[1], [2], [3]].

Fig. 1.

Representative photomicrographs of urine sediment cells before and after Sternheimer (S) staining. (a) Atypical urothelial cell (derived from urothelial carcinoma), unstained. (b) Atypical urothelial cell (derived from urothelial carcinoma), stained by S stain. (c) Intracytoplasmic inclusion body (ICIB)-positive cell, unstained. (d) ICIB-positive cell, stained by S stain. Images were captured at × 400 magnification.

The detection of atypical cells, including those suggestive of urothelial carcinoma, has significant clinical value for early diagnosis and therapeutic decision-making [[4], [5], [6]]. However, microscopic examination is highly observer-dependent, and its diagnostic accuracy can be affected by examiner skill, fatigue, and subjective interpretation, which may lead to the oversight of rare findings, such as atypical cells [1,5].

Automated urine particle analyzers have been developed to address these limitations. The Sysmex UF-5000 is a representative example that uses fluorescence flow cytometry and waveform signal analysis to classify urine particles into a CR channel (nucleic acid-containing) and an SF channel (non-nucleic acid) [7,8].

The UF-5000 automatically quantifies nucleic acid-containing particles and reports the “Atyp.C” (atypical cell) parameter as a numerical value (cells/μL), reflecting the number of particles with increased nucleic acid content or nuclear complexity. In the CR channel, fluorescence, scatter, and waveform signals are integrated to estimate the nuclear content and internal complexity, which are summarized in the Atyp.C parameter [7,8]. This parameter is designed to identify cells with elevated nucleic acid content or nuclear abnormalities compared to normal urothelial cells, without relying on morphological assessment [[9], [10], [11]].

Technical documentation and recent studies have suggested that Atyp.C detects not only atypical urothelial cells but also ICIB cells and virus-infected (“decoy”) cells, which characteristically harbor high nuclear content [10]. This raises the hypothesis that ICIB cells flagged as Atyp.C may indirectly improve atypical cell detection rates by supplementing cases in which atypical cells are overlooked by microscopy [10]. Clinical studies have also demonstrated the diagnostic performance of Atyp.C for urothelial carcinoma and its additive value when combined with urine cytology or sediment analysis [[9], [10], [11]].

Based on this background, this study aimed to clarify the relationship between the UF-5000 Atyp.C parameter, microscopically confirmed atypical cells, and cytoplasmic inclusion–positive cells in urine sediment, and to determine whether inclusion-bearing cells contribute to improved detection sensitivity.

2. Methods

This study was approved by the Ethical Review Committee for Medical Research Involving Human Subjects at Gunma University (approval no. HS-2020-118) and the Ethical Review Committee of the Isesaki Municipal Hospital. This study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained using an opt-out approach, in which patients were informed of their right to refuse participation through public disclosure.

2.1. Patient characteristics and sample information

A total of 264 urine sediment samples obtained from 203 patients were analyzed (Table 1). The cohort comprised 153 men (75.4 %) and 50 women (24.6 %) with a mean age of 73 years (range: 8–95 years). Of these samples, 136 were subjected to cytological examination. The discrepancy between the number of patients and samples was due to multiple submissions from some individuals. Data are expressed as n (%) or mean (range) values.

Table 1.

Patient characteristics and sample information.

Characteristics	No.
No. of patients	203
No. of specimens	264
Median age(range), years	73(8–95)
Sex
Male	153(75.4 %)
Female	50(24.6 %)

A total of 264 urine sediment samples obtained from 203 patients were analyzed. The cohort comprised 153 men (75.4 %) and 50 women (24.6 %) with a mean age of 73 years (range: 8–95 years). Among all the samples, 136 were subjected to cytological examination. The difference between the number of patients and samples reflects the presence of multiple specimens from some individuals. Data are presented as n (%) or means (ranges).

2.2. Urine sediment microscopic examination

Urine sediment examinations were performed according to the guidelines of the Japanese Committee for Clinical Laboratory Standards (GP1–P4) [12] and the Japanese Association of Medical Technologists [13]. After thorough mixing, the urine samples were centrifuged, and the sediments were examined using optical microscopy (Olympus, Tokyo, Japan). Findings that included atypical cells were independently reviewed by two licensed medical technologists with more than 5 years of experience in urine microscopy to ensure reliability. For this study, atypical cells were defined as positive when at least one was observed per whole field, and ICIB cells were defined as positive when at least one cell was observed in three consecutive high-power fields ( × 400). All microscopic findings were retrieved from the laboratory information system and retrospectively analyzed. To ensure broader international applicability and harmonization with global practice, the procedures for urine sediment preparation and interpretation were further benchmarked against internationally accepted standards, including the Clinical and Laboratory Standards Institute GP16-A3 guideline [14] and the 2023 European Federation of Clinical Chemistry and Laboratory Medicine European Urinalysis Guidelines [2].

2.3. Cytological evaluation

Cytological findings were obtained from corresponding clinical cytology reports prepared and diagnosed by board-certified cytopathologists according to the criteria of the Japanese Society of Clinical Cytology and the Paris System for Reporting Urinary Cytology. Although cytological diagnoses were originally reported as classes (Class I–V), for this analysis, specimens classified as Class I or II (negative/reactive) were considered cytologically negative, whereas those classified as Class III–V (atypical or malignant) were regarded as cytologically positive. Cytology results were used as reference clinical data to validate the detection performance of the UF-5000 Atyp.C parameter. Cytoplasmic inclusions were not evaluated in the cytological preparations. Cytological and urinalysis data were matched on a per-sample basis using the specimen identification numbers.

2.4. UF-5000 analysis

Residual specimens were analyzed using the UF-5000 (Sysmex Corporation, Kobe, Japan), an automated urine particle analyzer based on fluorescence flow cytometry with a blue semiconductor laser [7,8]. The aspirated urine was divided into channels for nucleic acid-containing (CR) and non-nucleic acid-containing (SF) particles. In the CR channel, nucleic acids were fluorescently stained, and multiple signal features (fluorescence intensity, scatter, and waveform area) were integrated to estimate nuclear content and internal complexity. The Atyp.C parameter was automatically calculated to indicate particles with increased nucleic acid content [8].

2.5. Statistical analysis

Statistical analyses were performed using SPSS v17.0 (SPSS, Chicago, IL, USA) and Microsoft Excel 2010 (Microsoft, Redmond, WA, USA). Cross-tabulation with the chi-square (χ²) test or Fisher's exact test was used to assess associations, and the Mann–Whitney U test was used to compare non-normally distributed continuous variables. Agreement was measured using Cohen's κ and Cramer's v. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated using Microsoft Excel 2010. Correlation strength was categorized as slight (0.00–0.20), fair (0.20–0.40), moderate (0.40–0.60), substantial (0.60–0.80), and almost perfect (0.80–1.00). Statistical significance was set at p < 0.05.

3. Results

The relationship between atypical cells and ICIB cells in urine sediment was evaluated. The UF-5000-reported Atyp.C parameter was significantly higher in specimens positive for atypical cells on urine sediment microscopy than in those without atypical cells (Mann–Whitney U test, p < 0.001, Hodges–Lehmann estimator of the median difference = −1.30, 95 % confidence interval [CI]: −1.60 to −1.00) (Fig. 2a). ICIB-positive samples (red dots) were predominantly observed among atypical cell-positive specimens, whereas ICIB-negative samples (blue dots) were predominantly observed among atypical cell-negative specimens. Cross-tabulation analysis (Fig. 2b) demonstrated a significant association between ICIB positivity and the presence of atypical cells (χ² = 85.378, p < 0.01). The κ coefficient (κ = 0.534, 95 % CI: 0.44–0.63) indicated a moderate level of agreement between atypical cell and ICIB detection, with an overall agreement rate of 77 %. These findings suggest that the detection of ICIBs is closely related to the presence of atypical cells in urine sediments and is a supportive cytological feature for their identification.

Fig. 2.

Association between UF-5000-measured atypical cell (Atyp.C) counts and the presence of atypical cells or cytoplasmic inclusion bodies (ICIBs) in urine sediment. (a) Scatter plot of log-transformed Atyp.C values according to the presence or absence of atypical cells in urine sediment. Atyp.C values were significantly higher in atypical cell-positive specimens than in negative specimens (p < 0.001, Mann–Whitney U test). Blue and red dots represent ICIB-negative and ICIB-positive samples, respectively. (b) Cross-tabulation of atypical cells and ICIB positivity in urine sediment. The κ coefficient (κ = 0.534) indicated a moderate level of agreement between atypical cell and ICIB detection (p < 0.01, overall agreement rate = 77 %).

To evaluate the diagnostic performance of the UF-5000-reported Atyp.C parameter for detecting atypical cells in urine sediment, we performed a ROC analysis (Fig. 3). As shown in Fig. 3a, when atypical cell-positive specimens were used as reference, the Atyp.C parameter yielded an area under the curve (AUC) of 0.829 (95 % CI: 0.779–0.879), with an optimal cutoff value of 0.30 cells/μL determined using Youden's index. When atypical cell-positive and ICIB-positive specimens were considered positive (Fig. 3b), the diagnostic performance improved, with an AUC of 0.895 (95 % CI: 0.849–0.941) and an optimal cutoff of 0.15 cells/μL. These results indicate that the UF-5000 Atyp.C parameter has high diagnostic accuracy for identifying atypical cells in urine sediment and that including ICIB-positive cases as part of the target population enhances the overall sensitivity of the automated detection system.

Fig. 3.

Receiver operating characteristic (ROC) analysis of atypical cell (Atyp.C) values for detecting atypical cells in urine sediment. (a) ROC curve for identifying samples that were positive for atypical cells only. (b) ROC curve for identifying samples positive for either atypical cells or ICIB-positive cells. The blue line represents the ROC curve for the Atyp.C value, the gray line indicates the reference (no discrimination) line, and the yellow line represents the optimal cutoff value (Youden's index). The area under the curve (AUC) was 0.829 (95 % confidence interval [CI]: 0.779–0.879) for atypical cells and 0.895 (95 % CI: 0.849–0.941) for atypical and ICIB-positive cells.

To further assess the diagnostic performance, we compared three cutoff levels: 0.1, 0.3, and 0.5 cells/μL (Table 2). A cutoff of 0.1 cells/μL was selected considering the effective decimal precision of the UF-5000 output, whereas 0.5 cells/μL represents the manufacturer-recommended threshold (Sysmex). At 0.1 cells/μL, the Atyp.C parameter demonstrated the highest sensitivity (97.8 %) but relatively low specificity (46.5 %). Raising the cutoff improved specificity, with 0.3 cells/μL providing a more balanced diagnostic performance (sensitivity, 85.2 %; specificity, 68.2 %; κ, 0.536; p < 0.001). At the Sysmex-recommended cutoff of 0.5 cells/μL, the specificity increased to 72.1 % with a moderate κ value (0.567) and comparable sensitivity (84.4 %). Although the 0.3 cells/μL threshold offered the best balance between sensitivity and specificity, the 0.1 cells/μL cutoff achieved the highest sensitivity and is therefore considered suitable for screening purposes, where minimizing false-negative results is crucial.

Table 2.

Comparison of agreement and diagnostic performance of Atyp.C values at different cutoff levels for detecting atypical cells in cytology-evaluated urine specimens.

Urinary Sediment			Atyp.C Cutoff:0.1		κ	χ2	p	SEN%	SPE%	PPV%	NPV%
			Positive	Negative
	Atypical cells	Positive	132	3	0.448	71.218	<0.001	97.8	46.5	65.7	95.2
		Negative	69	60				(94.0–99.5)	(38.0–55.0)	(59.0–72.0)	(88.0–98.2)
			Atyp.C Cutoff:0.3		κ	χ2	p	SEN%	SPE%	PPV%	NPV%
			Positive	Negative
	Atypical cells	Positive	115	20	0.536	77.821	<0.001	85.2	68.2	73.7	81.5
		Negative	41	88				(78.0–90.8)	(59.6–75.9)	(66.0–80.2)	(73.4–87.7)
			Atyp.C Cutoff:0.5		κ	χ2	p	SEN%	SPE%	PPV%	NPV%
			Positive	Negative
	Atypical cells	Positive	114	21	0.567	85.942	<0.001	84.4	72.1	76.0	81.6
		Negative	36	93				(77.0–90.0)	(63.8–79.2)	(68.5–82.3)	(73.7–87.8)

Cutoff values of 0.1, 0.3, and 0.5 cells/μL were evaluated, and the kappa coefficient (κ), Pearson's chi-square (χ²) value, p-value, sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV) were calculated for each cutoff. Values in parentheses indicate 95 % confidence intervals.

Among the cytology-evaluated specimens, the diagnostic performance was similarly analyzed using cutoff levels of 0.1, 0.3, and 0.5 cells/μL (Table 3). At 0.1 cells/μL, the Atyp.C parameter showed the highest sensitivity (89.1 %) but relatively low specificity (54.4 %). Increasing the cutoff to 0.3 cells/μL improved specificity to 81.1 % while maintaining good sensitivity (80.4 %), yielding the highest κ coefficient (0.590, p < 0.001). At the Sysmex-recommended threshold of 0.5 cells/μL, the specificity increased to 84.4 %, whereas the sensitivity slightly decreased to 73.9 %. The 0.3 cells/μL cutoff provided the best balance between sensitivity and specificity for the cytology-evaluated group. However, consistent with the urine sediment findings, the 0.1 cells/μL cutoff achieved the highest sensitivity and was considered suitable for screening, where detecting atypical cells with minimal false negatives was prioritized. Across the analyses based on urine sediment microscopy and urine cytology, consistent trends were observed: lower Atyp.C thresholds were associated with higher sensitivity, whereas higher thresholds improved specificity. Consistent results across both analyses suggest that the diagnostic characteristics of the UF-5000 Atyp.C parameter are stable regardless of the reference standard, supporting its potential as a robust screening marker [9,15].

Table 3.

Comparison of agreement and diagnostic performance of Atyp.C values at different cutoff levels for detecting atypical cells in cytology-evaluated urine specimens.

Cytology-evaluate			Atyp.C Cutoff:0.1		κ	χ2	p	SEN%	SPE%	PPV%	NPV%
			Positive	Negative
	Atypical celsl or Malignant cell	Positive	41	5	0.366	24.144	<0.001	89.1	54.4	50.0	90.7
		Negative	41	49				(77.8–95.9)	(44.3–64.2)	(40.4–59.6)	(80.1–96.4)
			Atyp.C Cutoff:0.3		κ	χ2	p	SEN%	SPE%	PPV%	NPV%
			Positive	Negative
	Atypical cells or Malignant cell	Positive	37	9	0.590	48.165	<0.001	80.4	81.1	68.5	89.0
		Negative	17	73				(66.9–90.2)	(71.9–88.3)	(55.3–79.4)	(80.2–94.9)
			Atyp.C Cutoff:0.5		κ	χ2	p	SEN%	SPE%	PPV%	NPV%
			Positive	Negative
	Atypical cells or Malignant cell	Positive	34	12	0.577	45.395	<0.001	73.9	84.4	70.8	86.4
		Negative	14	76				(59.7–85.0)	(75.5–91.0)	(57.9–81.8)	(78.2–92.4)

Cutoff values of 0.1, 0.3, and 0.5 cells/μL were evaluated, and the kappa coefficient (κ), Pearson's chi-square (χ²) value, p-value, sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV) were calculated for each cutoff. Values in parentheses indicate 95 % confidence intervals.

4. Discussion

In this study, we evaluated the performance of the UF-5000 Atyp.C parameter in detecting atypical and ICIB cells in urine sediments. Atyp.C exhibited high sensitivity for identifying atypical urothelial cells, particularly at lower thresholds, and the inclusion of ICIB-positive cells further enhanced this sensitivity. This finding reflects the principle of the UF-5000: the Atyp.C parameter detects particles with increased nucleic acid content or nuclear complexity, encompassing atypical epithelial and inclusion-bearing cells. These findings indicate that Atyp.C is a sensitive indicator of cells with elevated levels of nuclear material, regardless of whether it is derived from malignant transformation or inclusion formation.

An important finding of this study was the close relationship between ICIB-positive cells and atypical urothelial cells in urine sediment. ICIB cells have traditionally been associated with polyomavirus infection, particularly BK virus reactivation in immunosuppressed patients, such as renal transplant recipients. However, inclusion-like changes can also occur under inflammatory, degenerative, or reactive conditions, even in immunocompetent individuals. These cells exhibit enlarged nuclei and abundant nuclear material, features that lead to high fluorescence signals on UF-5000 and thus elevated Atyp.C values. We observed that ICIB-positive cells frequently co-occurred with atypical urothelial cells, and including these cases as “positive” markedly improved the diagnostic performance of the Atyp.C parameter. This suggests that ICIB cells are not merely confounding artifacts but valuable indicators of underlying urothelial pathology, whether viral, neoplastic, or inflammatory. Their presence may serve as an early warning marker and should not be disregarded during automated or microscopic urine analyses.

Our results are consistent with those of previous studies that have demonstrated the diagnostic potential of Atyp.C. Shukuya et al. [10] reported an AUC of 0.856 (at a cutoff of 0.3 cells/μL) for detecting atypical cell-positive specimens. Meanwhile, Ren et al. [9] found that Atyp.C positivity correlated with high-grade urothelial lesions. Zhang et al. [11] also demonstrated markedly increased urinary Atyp.C levels in bladder carcinoma and a positive correlation with tumor grade. In this study, sensitivity was even higher when lower thresholds were applied, highlighting Atyp.C as a robust parameter for screening atypical or abnormal urothelial cells. The inclusion of ICIB-positive cells may also account for the enhanced detection rate observed compared to that in studies limited to cytologically confirmed atypical cells.

The high sensitivity of Atyp.C could be attributed to its measurement principle. UF-5000 uses fluorescence flow cytometry to quantify nucleic acid-containing particles. The Atyp.C parameter integrates fluorescence and scatter signals to identify cells with abnormally high nucleic acid contents. Consequently, cells harboring viral inclusions or undergoing degenerative changes are often classified as Atyp.C-positive because these inclusions are rich in nucleic acids [16]. In contrast, anucleate components, such as erythrocytes and mature squamous cells, showed minimal fluorescence. Therefore, Atyp.C primarily reflects the nuclear-to-cytoplasmic ratio, which is a fundamental morphological indicator of cellular atypia [17]. It is also possible that Atyp.C responds to inclusion-bearing or degenerative cells showing nuclear enlargement or increased nucleic acid signals, thereby broadening its diagnostic coverage.

The main clinical implication of these findings is that the UF-5000 Atyp.C parameter serves as a high-sensitivity screening tool for routine urinalysis. Because the test is automated and noninvasive, it can efficiently flag samples for further microscopic or cytological review without increasing the workload. Shukuya et al. [10] suggested that Atyp.C analysis complements manual microscopy for detecting atypical cells, and Zhang et al. [11] noted its potential use for the early diagnosis and surveillance of bladder cancer. Our findings reinforce these conclusions while emphasizing that microscopic examination of urinalysis sediment remains essential for morphological confirmation. The combination of automated Atyp.C screening and expert manual microscopy can maximize diagnostic accuracy. Automated detection ensures sensitivity and throughput, whereas direct microscopic evaluation allows verification of atypical morphology and exclusion of artifacts, such as leukocyte clumps or debris. However, this study has some limitations. First, patient-level clinical information, including tumor history, or inflammatory status was not available, thereby limiting the interpretability of Atyp.C elevations. Prospective studies incorporating detailed clinical data will be necessary to further validate its clinical utility. Second, false-positive Atyp.C elevations can occur as driven by confounding variables, including leukocyte aggregates, macrophages, etc.; these changes culminate in increased nucleic acid signals on fluorescence flow cytometry [9,10]. Recent studies have shown that WBC clumps, clue cells, and bacteriuria-associated epithelial cells are important contributors to elevated Atyp.C values, especially in specimens from women with pyuria or urinary tract infection. Establishing pre-analytical strategies, such as adequate midstream urine collection or treating infectious or inflammatory conditions prior to testing, may help reduce false positives [18]. Third, Atyp.C elevations are not specific to atypical or ICIB-positive cells; reactive, degenerative, or regenerating urothelial cells may also exhibit increased nucleic acid content and thus trigger elevated Atyp.C values. Automated detection should therefore be considered a screening tool rather than a stand-alone diagnostic marker. Finally, severe hematuria may influence optical detection despite red blood cells being anucleate. Although not assessed here, gross hematuria can alter sample clarity or interfere with scatter signals. Future research should refine threshold settings under inflammatory, infectious, or hemorrhagic conditions and evaluate whether combining Atyp.C with molecular or proteomic biomarkers could further enhance specificity and diagnostic accuracy.

5. Conclusion

The UF-5000 Atyp.C parameter sensitively detects cells with increased nucleic acid content, including atypical and ICIB cells. By identifying urine samples that potentially contain atypical cells through the detection of ICIB cells, Atyp.C serves as an effective and highly sensitive screening parameter in routine urinalysis. Combining automated Atyp.C detection with manual microscopic examination by clinical laboratory professionals may enhance the early detection of atypical urothelial cells while maintaining efficiency and reproducibility in laboratory workflows.

CRediT authorship contribution statement

Yuki Fujiwara: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Yuzuru Takei: Writing – review & editing, Data curation. Hiroto Nakazawa: Writing – review & editing, Data curation.

AI disclosure

Statement: During the preparation of this work, the authors used ChatGPT (GPT-5, OpenAI, San Francisco, CA, USA) in order to assist with English editing and language refinement. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Funding

This research was supported by the Sysmex Corporation (Kobe, Japan) . However, the sponsors were not involved in the development of the protocols, data collection, data analysis, or literature writing.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This research was supported by the Sysmex Corporation. However, the sponsors were not involved in the development of the protocols, data collection, data analysis, or literature writing. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.