Comparison of five automated urine sediment analyzers with manual microscopy for accurate identification of urine sediment

AI Driven Lab-on-Chip Cartridge for Automated Urinalysis

After haematology, urinalysis is the most common biological test performed in clinical settings. Hence, simplified workflow and automated analysis of urine elements are of absolute necessities. In the present work, a novel lab-on-chip cartridge (Gravity Sedimentation Cartridge) for the auto analysis of urine elements is developed. The GSC consists of a capillary chamber that uptakes a raw urine sample by capillary force and performs particles and cells enrichment within 5 min through a gravity sedimentation process for the microscopic examination. Centrifugation, which is necessary for enrichment in the conventional method, was circumvented in this approach. The AI100 device (Image based autoanalyzer) captures microscopic images from the cartridge at 40x magnification and uploads them into the cloud. Further, these images were auto-analyzed using an AI-based object detection model, which delivers the reports. These reports were available for expert review on a web-based platform that enables evidence-based tele reporting. A comparative analysis was carried out for various analytical parameters of the data generated through GSC (manual microscopy, tele reporting, and AI model) with the gold standard method. The presented approach makes it a viable product for automated urinalysis in point-of-care and large-scale settings.

Establishing an External Quality Assessment (EQA) Program for Urinalysis in Medical Laboratories of Thailand

Thailand Association of Clinical Biochemists (TACB) introduced External Quality Assurance schemes (EQAs) for urinalysis (UA) using urine strips in medical laboratories of Thailand. The few available External Quality Assessment (EQA) programs on urinary microalbumin rarely include an evaluation of clinical cases. The aim of the present study was to assess a descriptive analysis of biochemical urinalysis including urine microalbumin in the Thailand laboratory practice. From January 2021 to December 2021, four surveys were organized. EQA urine samples were distributed to the participants by mail. The participants measured the UA of 2 samples quarterly and returned the results together with the information about their instruments and suggestion for the performance of the laboratory report quarterly. Moreover, summary of the situation of each laboratory performance was feedbacked by online system. Fifty-eight laboratories participated in the survey. The EQA panels included positive and negative samples. The analytical results for passed parameters of urine chemical test range from 79.3-100%. All special tests; microalbumin, creatinine, and beta-HCG showed correct result from 85.1-96.1%. The overall accuracy, specificity, and sensitivity were 92.6, 85.7, and 75,4%, respectively. The major issues were observed: the low sensitivity for the detection of low-concentration samples and the incapacity of several methods to detect the positive sample. The assessment is needed to continuously evaluate the improvement proficiency of laboratories in Thailand.

Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization

The diagnosis of kidney disease often involves analysing urine sediment particles. Traditionally, urinalysis was performed manually by collecting urine samples and using a centrifuge, which was prone to manual errors and relied on labour-intensive processes. Automated urine sediment microscopy, based on machine learning models, requires segmentation and feature extraction, which can hinder model performance due to intrinsic characteristics of microscopic images. Deep learning models based on convolutional neural networks (CNNs) often rely on a large number of manually annotated data, making the system computationally complex. This study propose an advanced deep learning model based on YOLOv5, which offers faster performance and requires comparatively less data. The proposed model used five variants of the YOLOv5 model (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) to detect six categories of urine particles (erythrocyte, leukocyte, crystals, cast, mycete, epithelial cells) from microscopic urine sediment images. The dataset involved 5376 images of urine sediments with 6 particles. There are 30 sets of hyperparamreteres are employed in the YOLOv5 model. To optimize the hyperparameters and fine-tune with the urine sediment dataset and for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among the six categories of detected particles mycete achieved maximum performance with a mAP of 97.6 % and crystals achieved minimum performance with a mAP of 81.7 % with YOLOv5x model compared to other particles. To optimize the hyperparameters for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among all the models, YOLOv5l and YOLOv5x performed the best. YOLOv5l achieved a mean average precision (mAP) of 85.8 % while YOLOv5x achieved a mAP of 85.4 % at an IoU threshold of 0.5. The detection speed per image was 23.4 ms for YOLOv5l and 28.4 ms for YOLOv5x. The proposed method developed a faster and better automated microscopic model using advanced deep learning techniques to detect urinary particles from microscopic urine sediment images for kidney disease identification. The method demonstrated strong performance in urinalysis.

Evaluation of the fully automated urine particle analyzer UF-1500

BACKGROUND AND AIMS

This study primarily assessed the performance of the UF-1500, the novel and compact model of the fully automated urine particle analyzer and evaluated its performance against the existing UF-5000 instrument.

MATERIALS AND METHODS

A total of 648 residual urine specimens were randomly collected and examined using both the UF-1500 and UF-5000 instruments as well as manual microscopy. For each parameter, the concordance rates and detection accuracy of the UF-1500 against manual microscopy were compared with the UF-5000.

RESULTS

The concordance rates between the UF-1500 and manual microscopy were 75.3%-98.5%. The UF-1500 concordance rates within one group agreement were observed to be >90%, for all parameters except for YLCs. The differences within one group agreement between the UF-1500 and manual microscopy were insignificant, in comparison to the UF-5000, with exceptions noted for ECs and YLCs. The sensitivity and specificity of the UF-1500 for RBCs, WBCs, Squa.ECs, and BACT exceeded 80%, while the positive predictive values of ECs and CASTs were below 70%.

CONCLUSION

The UF-1500 exhibited a performance that was comparable to the existing instrument, the UF-5000, and was suitable to be introduced in clinical practice. For the samples with suspected false-positive or false-negative results, a manual microscopic examination is required for accurate testing.

Sysmex UN2000 detection of protein/creatinine ratio and of renal tubular epithelial cells can be used for screening lupus nephritis

Objectives

This study is aimed to evaluate if automated urine sediment analysis UN2000 can be used to screen lupus nephritis.

Methods

UN2000 was used to examine 160 urine samples from patients with systemic lupus erythematosus and 124 urine samples from Lupus nephritis. The result of protein/creatinine ratio(P/C) and renal tubular epithelial cells (RTEC) were evaluated. With biochemical analysis and microscopic examination as the gold standard, the Kappa consistency test was used to analyze the accuracy of P/C and RTEC. Analysis was to evaluate the accuracy of P/C single item or RTEC single item and both screening lupusnephritis.

Results

The consistency of P/C and the gold standard, and that of RTEC and the gold standard are respectively strong and good (0.858 vs. 0.673). The specificity, positive predictive value, and coincidence were the highest when P/C ≥ 2 + was set as the only screening standard for lupus nephritis. When the standard was selected between P/C ≥ 2 + or RTEC > 2.8 cells/µl, the sensitivity and negative predictive value were the highest.

Conclusion

UN 2000 can be used to screen lupus nephritis by detecting P/C and RTEC.

Differential identification of urine crystals with morphologic characteristics and solubility test

BACKGROUND

Urinary crystals are the most diverse forms of urine sediments. Reference images for typical urinary crystals are common, however, but images for interpreting atypical urinary crystals are very rare. The authors reviewed various forms and solubility tests of urine crystals to interpret atypical crystals found in clinical specimens.

METHODS

We reviewed textbooks on urinary crystals and articles published in PubMed. Some atypical crystals were confirmed using a solubility test.

RESULTS

The classification, shape, chemical structure, and solubility of the crystals were summarized. In the solubility test, some crystals showed different results; therefore, a new solubility test was proposed based on the literature review. We presented various types of calcium oxalates.

CONCLUSIONS

These review articles will be helpful in the examination of atypical crystals found in clinical specimens. The solubility test requires additional studies to discriminate the inconsistent results between the authors.

A novel approach to screening and managing the urinary tract infections suspected sample in the general human population

Background

Automated urine technology providing standard urinalysis data can be used to support clinicians in screening and managing a UTI-suspected sample. Fully automated urinalysis systems have expanded in laboratory practice. Commonly used were devices based on digital imaging with automatic particle recognition, which expresses urinary sediment results on an ordinal scale. There were introduced fluorescent flow cytometry analyzers reporting all parameters quantitatively. There is a need to harmonize the result and support comparing bacteria and WBC qualitative versus semiquantitative results.

Methods

A total of 1,131 urine samples were analyzed on both automated urinalysis systems. The chemical components of urinalysis (leukocyte esterase and nitrate reductase) and the sediment results (leukocytes and bacteria) were investigated as potential UTI indicators. Additionally, 106 specimens were analyzed on UF-5000 and compared with culture plating to establish cut-offs that can be suitable for standard urinalysis requirements and help to guide on how to interpret urinalysis results in the context of cultivation reflex.

Results

The medians of bacteria counts varies from 16.2 (absence), 43.0 (trace), 443.5 (few), 5,389.2 (moderate), 19,356.6 (many) to 32,545.2 (massive) for particular digital microscopic bacteriuria thresholds. For pyuria thresholds, the medians of WBC counts varies from 0.8 (absence), 2.0 (0-1), 7.7 (2-3), 21.3 (4-6), 38.9 (7-10), 61.3 (11-15) to 242.2 (>30). Comparing the culture and FFC data (bacterial and/or WBC counts) was performed. Satisfactory sensitivity (100%), specificity (83.7%), negative predictive value (100%), and positive predictive value (75%) were obtained using indicators with the following cut-off values: leukocytes ≥40/µl or bacteria ≥300/µl.

Conclusions

Accurate urinalysis gives information about the count of bacteria and leukocytes as useful indicators in UTIs, in general practice it can be a future tool to cross-link clinical and microbiology laboratories. However, the cut-off adjustments require individual optimization.

Consistency analysis of the Sysmex UF-5000 and Atellica UAS 800 urine sedimentation analyzers

OBJECTIVE

To evaluate the consistency between the results of Sysmex UF-5000 system and Atellica® UAS 800 Urine Sediment Analyzer.

METHODS

A total of 636 random urine samples were collected from inpatients and outpatients from March to September 2021. Urine was collected for analysis by the Sysmex UF-5000, Atellica UAS 800 systems, and manual microscopic examination. The results of manual microscopy as the gold standard, the coincidence rate and false-negative rate of Sysmex UF-5000 and Atellica UAS 800 systems in the detection of red blood cells, white blood cells, and casts were calculated.

RESULTS

The coincidence rates of red blood cells, white blood cells, and cast, crystals, and other sediment components for the Sysmex UF-5000 system were 85.37%, 87.89%, 91.67%, 88.36%, and 71.86%. The false-negative rates were 28.47%, 3.75%, 68.97%, 37.25%, and 30.63%. The coincidence rates of red blood cells, white blood cells, and cast, crystals, and other sediment components for the Atellica UAS 800 system were 85.06%, 90.25%, 59.12%, 91.67%, and 67.45% and the false-negative rates were 60.42%, 21.25%, 36.21%, 19.64%, and 35.80%.

CONCLUSION

Two instruments are superior in the detection of red blood cells and white blood cells. The Atellica UAS 800 system with image review has a good coincidence rate in the identification of crystals and casts. The identification of various sediment components in urine by both instruments meets the laboratory requirements. Two instruments with different methodologies have their own characteristics, and we should reasonably use them according to the conditions of the laboratory.

Evaluation of the Atellica® UAS 800: a new member of the automated urine sediment analyzer family

BACKGROUND

In 2017 the Atellica^® UAS 800 urine sediment analyzer was introduced by Siemens Healthineers. We investigated its applicability in the standardization and automation of the laboratory urinalysis workflow, including the prediction of urine culture outcome and glomerular pathology.

METHODS

We evaluated the performance characteristics of the Atellica^® UAS 800 and its correlation with the iQ200 (Beckman Coulter). In addition, we studied the agreement between Atellica^® UAS 800 and CLINITEK Novus^® and determined the predictive value of bacteria and leukocyte counts for urine culture outcome. Furthermore, we investigated the ability of Atellica^® UAS 800 to identify pathological casts and dysmorphic erythrocytes in comparison to manual microscopy.

RESULTS

Erythrocyte and leukocyte analyses indicated high intra- and inter-run precisions and good correlations with the iQ200. We found that the Atellica^® UAS 800 detects bacteria with higher sensitivity than the iQ200. The Atellica^® UAS 800 and CLINITEK Novus^® showed a high degree of conformity. We determined seven combinations of clinical cut-off values of bacteria and leukocytes for predicting urine culture outcome with sensitivity, specificity, and negative predictive values of 95%, 52%, and 93%, respectively. Using the Atellica^® UAS 800, hyaline casts, erythrocyte casts, leukocyte casts, and dysmorphic erythrocytes were correctly recognized in 76%, 22%, 2%, and 39% of the samples, respectively.

CONCLUSIONS

The Atellica^® UAS 800 is a robust, fast, and user-friendly analyzer, which accurately quantifies erythrocytes, leukocytes, bacteria and squamous epithelial cells, and may be utilized for predicting positive urine cultures. The detection of clinically important pathological casts and dysmorphic erythrocytes proved insufficient.

Revisiting approaches to and considerations for urinalysis and urine culture reflexive testing

Urinalysis is considered the world's oldest laboratory test. Today, many laboratories use macroscopic urinalysis as a screening tool to determine when to subject urine samples for a microscopic urinalysis and/or bacterial culture. While reflexive urine microscopy has been practiced for decades, and reflexive urine culture, more recently, evidence-based guidelines regarding optimal reflexive criteria and workflows are lacking. Standard approaches are hindered, in part, by a lack of harmonization of urinalysis and urine culture practices, heterogeneity in patient populations that are studied, and lack of provider adherence to recommended practices. This review summarizes studies that have evaluated the performance of reflexive urine microscopy and reflexive urine culture, particularly in the context of urinary tract infections. It also examines reported clinical outcomes from reflexive urinalysis interventions and their impact on antibiotic stewardship efforts. Finally, it discusses laboratory operational considerations for the implementation of reflexive algorithms.

AACC Guidance Document on Laboratory Investigation of Acute Kidney Injury

BACKGROUND

Acute kidney injury (AKI) is a sudden episode of kidney damage or failure affecting up to 15% of hospitalized patients and is associated with serious short- and long-term complications, mortality, and health care costs. Current practices to diagnose and stage AKI are variable and do not factor in our improved understanding of the biological and analytical variability of creatinine. In addition, the emergence of biomarkers, for example, cystatin C, insulin-like growth factor binding protein 7, and tissue inhibitor of metalloproteinases 2, and electronic notification tools for earlier detection of AKI, highlights the need for updated recommendations to address these developments.

CONTENT

This AACC Academy guidance document is intended to provide laboratorians and clinicians up-to-date information regarding current best practices for the laboratory investigation of AKI. Topics covered include: clinical indications for further investigating potential AKI, analytical considerations for creatinine assays, the impact of biological variability on diagnostic thresholds, defining "baseline" creatinine, role of traditional markers (urine sodium, fractional excretion of sodium, fractional excretion of urea, and blood urea-to-creatinine ratio), urinary microscopic examination, new biomarkers, improving AKI-associated test utilization, and the utility of automated AKI alerts.

SUMMARY

The previous decade brought us a significant number of new studies characterizing the performance of existing and new biomarkers, as well as potential new tools for early detection and notification of AKI. This guidance document is intended to inform clinicians and laboratorians on the best practices for the laboratory investigation of AKI, based on expert recommendations where the preponderance of evidence is available.

The Article COMPARISON OF LX-8000R AND URISED 2 FULL-AUTOMATED URINE ANALIZERS WITH MANUAL MICROSCOPIC EXAMINATION

Background

Urinalysis has an important place in evaluating kidney and urinary tract infections. Automated urine analyzers enhance productivity and turnover in laboratories and economize time and labor required for analysis. In the present study, we evaluated and compared analytic and diagnostic performance of UriSed2 with LX-8000R, which is a novel image-based automated urine sediment analyzer.

Methods

A total of 178 urine samples sent to our laboratory were evaluated by the two urine analyzers and standard manual microscopy. Precision and comparison studies were done in accordance with CLSI guidelines.

Results

Sensitivity assessment revealed similar outcomes with both UriSed2 and LX-8000R devices for erythrocyte count (RBC), whereas UriSed2 device yielded higher outcomes for leukocyte count (WBC) and epithelial cells (EPI) than LX-8000R analyzer. Specificity of UriSed2 for WBC and RBC was higher than that of LX-8000R device. According to Gamma statistics, both urine analyzers showed perfect consistency for WBC, RBC and EPI cell counts. Manuel microscopy revealed statistically significant correlation between LX-8000R and UriSed2 in terms of WBC and RBC. Manual evaluation by Bland-Altman analysis demonstrated lower WBC and RBC values and higher EPI as compared to both UriSed2 and LX-8000R devices. As the result of Passing-Bablok regression analysis, both devices were found to be inconsistent with manual microscopy.

Conclusions

We think that evaluation of automated urine analyzers will be more meaningful when they are evaluated together with urine samples and patient clinical findings in addition to comparing with manual microscopy.

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence-A Narrative Review

Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As the next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.

Rapid diagnosis and reduced workload for urinary tract infection using flowcytometry combined with direct antibiotic susceptibility testing

BACKGROUND

We evaluated if flowcytometry, using Sysmex UF-5000, could improve diagnosis of urinary tract infections by rapid identification of culture negative and contaminated samples prior to culture plating, thus reducing culture plating workload and response time. We also evaluated if it is possible to reduce the response time for antibiotic susceptibility profiles using the bacteria information flag on Sysmex UF-5000 to differentiate between Gram positive and negative bacteria, followed by direct Antibiotic Susceptibility Testing (dAST) on the positive urine samples.

METHODS

One thousand urine samples were analyzed for bacteria, white blood cells and squamous cells by flowcytometry before culture plating. Results from flowcytometric analysis at different cut-off values were compared to results of culture plating. We evaluated dAST on 100 urine samples that were analyzed as positive by flowcytometry, containing either Gram positive or Gram negative bacteria.

RESULTS

Using a cut-off value with bacterial count ≥100.000/mL and WBCs ≥10/μL, flowcytometry predicted 42,1% of samples with non-significant growth. We found that most contaminated samples contain few squamous cells. For 52/56 positive samples containing Gram negative bacteria dAST was identical to routine testing. Overall, there was concordance in 555/560 tested antibiotic combinations.

CONCLUSION

Flowcytometry offers advantages for diagnosis of urinary tract infections. Screening for negative urine samples on the day of arrival reduces culture plating and workload, and results in shorter response time for the negative samples. The bacteria information flag predicts positive samples containing Gram negative bacteria for dAST with high accuracy, thus Antibiotic Susceptibility Profile can be reported the day after arrival. For the positive samples containing Gram negative bacteria the concordance was very good between dAST and Antibiotic Susceptibility Testing in routine. For positive samples containing Gram positive bacteria the results were not convincing. We did not find any correlation between epithelial cells and contamination.

Comparison of the reliability of Gram-negative and Gram-positive flags of the Sysmex UF-5000 with manual Gram stain and urine culture results

OBJECTIVES

Recently, the fully automated flow cytometry-based UF-5000 (Sysmex Corboration, Kobe, Japan) urine sediment analyzer was developed providing bacteria (BACT) info flags for more accurate bacterial discrimination of urinary tract infections (UTIs). This study aimed to compare the reliability of the UF-5000 BACT-info flags with manual Gram stain and urine culture as the gold standard method.

METHODS

A total of 344 urine samples were analyzed on the UF-5000 and compared with manual microscopic Gram stain and urine cultures. Agreement was assessed by Cohen's kappa (κ) analysis. The Youden index was used to determine the optimal BACT and white blood cell (WBC) cut-off points for discriminating positive and negative urine cultures.

RESULTS

Overall 98/344 (28.5%) samples were urine culture positive at a cut-off of ≥105 CFU/mL. "Gram-negative?" UF-5000 BACT-Info flags showed a better concordance of 25/40 (62.5%) with urine culture compared to Gram stain with 30/50 (60%). The results for UF-5000 discrimination of Gram-positive and Gram-negative microorganisms demonstrated a substantial (κ = 0.78) and fair (κ = 0.40) agreement with urine culture. Optimal cut-off points detecting positive urine cultures were 135 BACT/µL (sensitivity [SE]: 92.1%, specificity [SP]: 85.4%, positive predictive value [PPV]: 71%, negative predictive value [NPV]: 96%) and 23 WBC/µL (SE: 73.5%, SP: 84.1%, PPV: 65%, NPV: 89%).

CONCLUSIONS

The UF-5000 analyzer (Sysmex) is a reliable diagnostic tool for UTI screening. The displayed BACT-Info flags allow a quick diagnostic orientation for the clinician. However, the authors suggest verifying the automated Gram categories with urine culture.

A comparison of automated urine analyzers cobas 6500, UN 3000-111b and iRICELL 3000 with manual microscopic urinalysis

Objectives

Microscopic examination is essential in urine analysis. This is a simple way to collect informative data but it is also labor-intensive, time-consuming, and requires experienced staff for accurate results and interpretation. Several automated urine analyzers have been introduced for urine analysis in medical laboratories. The aim of this study was to assess and compare the performance of the most common three automated urine analyzers, Cobas 6500, UN3000-111b and iRICELL 3000.

Design

and Methods: A total of 100 routine urine samples were used in the study. Results from the three machines were compared with the routine procedure results including physical, chemical and sediment analysis.

Results

There was good correlation of urine physical and chemical analyses between the three analyzers with an overall concordance level of more than 80%. For sediment analysis, the degree of concordance between manual analysis and the three instruments was very good to good for white blood cells, red blood cells and epithelial cells, and moderate for bacteria. There were fair to good agreements between manual microscopy and the three instruments, Cobas 6500, UN3000-111b and iRICELL 3000, for casts (Cohen's kappa 0.42, 0.38 and 0.62, respectively).

Conclusions

The three automated urine analyzers showed similar performances and good correlation with manual microscopy. The results of this study indicate that automated urine analyzers could be used for initial urine testing to reduce high workloads and to save time, but manual microscopic analysis by experienced staff is still necessary to classify urine sediments for confirmation, especially in pathologic specimens.

Improved efficiency of urine cell image segmentation using droplet microfluidics technology

Recent advances in the recognition of biological samples using machine vision have made this technology increasingly important in research and detection. Image segmentation is an important step in this process. This study focuses on how to reduce the interference factors such as the overlap between different types (or within the same type) of urine cells according to microfluidics and improve the machine vision segmentation accuracy for cell images. In this study, we demonstrate that the platform can realize this hypothesis using urine cell image segmentation as an example application. We first discuss the reported urine cell droplet microfluidic chip system, which can realize the test conditions in which urine cells are encapsulated in the droplet and isolated from salt crystallization and/or bacteria and other urine-formed elements. Then, based on the analysis conditions set in the aforementioned experiment, the proportions of red blood cells, white blood cells, and squamous epithelial cells covered by various formed elements in the total urine cells in the same urine sample are measured. We simultaneously analyze the percentage of urine cells covered by salt crystallization and the incidence of overlapping between urine cells. Finally, the Otsu algorithm is used to segment the urine cell images encapsulated by the droplet and the urine cell images not encapsulated by the droplet, and the Dice, Jaccard, precision, and recall values are calculated. The results suggest that the method of encapsulating single cells based on droplets can improve the image segmentation effect without optimizing the algorithm.

Urine Sediment Findings and the Immune Response to Pathologies in Fungal Urinary Tract Infections Caused by Candida spp

Fungi are pathogenic agents that can also cause disseminated infections involving the kidneys. Besides Candida, other agents like Cryptococcus spp. can cause urinary tract infection (UTI), as well as other non-yeast fungi, especially among immunocompromised patients. The detection and identification of fungi in urine samples (by microscopy and culture) plays an essential role in the diagnosis of fungal UTI. However, variable cutoff definitions and unreliable culture techniques may skew analysis of the incidence and outcome of candiduria. The sediment analysis plays a key role in the identification of fungal UTI because both yeasts and pseudohyphae are easily identified and can be used as a clinical sign of fungal UTI but should not be overinterpreted. Indeed, urine markers of the immune response (leukocytes), urine barriers of tissue protection (epithelial cells), and urine markers of kidney disease (urinary casts) can be found in urine samples. This work explores the manifestations associated with the fungal UTI from the urinalysis perspective, namely the urinary findings and clinical picture of patients with fungal UTI caused by Candida spp., aspects associated with the immune response, and the future perspectives of urinalysis in the diagnosis of this clinical condition.

A Microfluidic Detection System for Bladder Cancer Tumor Cells

The clinical characteristics of excreted tumor cells can be found in the urine of bladder cancer patients, meaning the identification of tumor cells in urine can assist in bladder cancer diagnosis. The presence of white blood cells and epithelial cells in the urine interferes with the recognition of tumor cells. In this paper, a technique for detecting cancer cells in urine based on microfluidics provides a novel approach to bladder cancer diagnosis. The bladder cancer cell line (T24) and MeT-5A were used as positive bladder tumor cells and non-tumor cells, respectively. The practicality of the tumor cell detection system based on microfluidic cell chip detection technology is discussed. The tumor cell (T24) concentration was around 1 × 10⁴ to 300 × 10⁴ cells/mL. When phosphate buffer saline (PBS) was the diluted solution, the tumor cell detected rate was 63–71% and the detection of tumor cell number stability (coefficient of variation, CV%) was 6.7–4.1%, while when urine was the diluted solution, the tumor cell detected rate was 64–72% and the detection of tumor cell number stability (CV%) was 6.3–3.9%. In addition, both PBS and urine are tumor cell dilution fluid solutions. The sample was analyzed at a speed of 750 microns per hour. Based on the above experiments, a system for detecting bladder cancer cells in urine by microfluidic analysis chip technology was reported. The rate of recognizing bladder cancer cells reached 68.4%, and the speed reached 2 mL/h.

Comparison of the diagnostic performance of two automated urine sediment analyzers with manual phase-contrast microscopy

Background

Recently, several manufacturers have launched automated urinalysis platforms. This study aimed to compare the diagnostic performance of the UF-5000 (Sysmex Corporation, Kobe, Japan) and the cobas® u 701 (Roche Diagnostics, Rotkreuz, Switzerland) urine sediment analyzers with manual phase-contrast microscopy as the reference method.

Methods

A total of 195 urine samples were analyzed on both automated platforms and subjected to manual microscopic examination. Agreement was assessed by Cohen’s kappa (κ) analysis. Sensitivities and specificities were calculated.

Results

The agreement of the UF-5000 with manual microscopy was almost perfect (κ > 0.8) for red (RBC) and white blood cells (WBC), renal tubular epithel cells, hyaline casts, bacteria (BACT) and yeast (YLC), substantial (κ = 0.61–0.80) for squamous epithel cells (SEC) and pathologic casts, and moderate (κ = 0.41–0.60) for transitional epithel cells. The cobas® u 701 showed substantial agreement (κ = 0.61–0.80) for WBC, moderate agreement (κ = 0.41–0.60) for hyaline casts, and fair agreement (κ = 0.21–0.40) for RBC, SEC, non-squamous epithel (NEC), pathologic casts, BACT and YLC. The UF-5000 sensitivities ranged between 98.5% for RBC and 83.3% for pathological casts. The cobas® u 701 showed sensitivities between 83.0% for WBC and 31.6% for YLC.

Conclusions

The UF-5000 (Sysmex) analyzer showed a better diagnostic agreement with manual phase-contrast microscopy compared to the cobas® u 701 (Roche) module. The Sysmex platform showed reliable results for urine sediment analysis. However, pathological samples should be verified with manual microscopy.