Urinary sediment analyzed by flow cytometry

Exosomal miRNAs in urine associated with children's cardiorenal parameters: a cross-sectional study

Aims:

The authors sought to examine associations between urinary exosomal miRNAs (exo-miRs), emerging biomarkers of renal health, and cardiorenal outcomes in early childhood.

Materials & methods:

The authors extracted exo-miRs in urine from 88 healthy Mexican children aged 4–6 years. The authors measured associations between 193 exo-miRs and cardiorenal outcomes: systolic/diastolic blood pressure, estimated glomerular filtration rate and urinary sodium and potassium levels. The authors adjusted for age, sex, BMI, socioeconomic status, indoor tobacco smoke exposure and urine specific gravity.

Results:

Multiple exo-miRs were identified meeting a false discovery rate threshold of q < 0.1. Specifically, three exo-miRs had increased expression with urinary sodium, 17 with urinary sodium-to-potassium ratio and one with decreased estimated glomerular filtration rate.

Conclusions:

These results highlight urinary exo-miRs as early-life biomarkers of children's cardiorenal health.

Inspection of visible components in urine based on deep learning

PURPOSE

Urinary particles are particularly important parameters in clinical urinalysis, especially for the diagnosis of nephropathy. Therefore, it is highly important to precisely detect urinary particles in the clinical setting. However, artificial microscopy is subjective and time consuming, and various previous detection algorithms lack the adequate accuracy. In this study, a method is proposed for the analysis of urinary particles based on deep learning.

METHODS

We used seven cellular components (i.e., erythrocytes, leukocytes, epithelial, low-transitional epithelium, casts, crystal, and squamous epithelial cells) in the microscopic imaging of urine as the detection targets. After the extraction of features using Resnet50, feature maps of different sizes are obtained in the last few layers of the feature pyramid net (FPN). The feature maps are then input into the classification subnetwork and regression subnetwork for classification and localization respectively, and detection results are obtained. First, we introduce the basic model (RetinaNet) to detect the cellular components in urinary particles, and the features of the objects can then be extracted more effectively by replacing different basic networks. Lastly, the effects of different weight initialization methods and different anchor scales on the performance of the model are investigated.

RESULTS

We obtained the optimal network structure based on the adjustment of the loss functional parameters, thereby achieving the best results in the test set of urinary particles. The experimental data yielded an accuracy of 88.65% with a processing time of only 0.2 s for each image on a GeForce GTX 1080 graphics processing unit (GPU). Our results demonstrate that this method cannot only achieve the speed of the first-stage target detector, but also the accuracy of the two-stage target algorithm in the analysis of urinary particles.

CONCLUSION

This study developed new automated analysis urinary particles based on deep learning, and this method is expected to be used for the automated analysis and detection of urinary particles. Moreover, our approach will be useful for the detection of other cells in the clinical setting.

The comparison of automated urine analyzers with manual microscopic examination for urinalysis automated urine analyzers and manual urinalysis

OBJECTIVES

Urinalysis is one of the most commonly performed tests in the clinical laboratory. However, manual microscopic sediment examination is labor-intensive, time-consuming, and lacks standardization in high-volume laboratories. In this study, the concordance of analyses between manual microscopic examination and two different automatic urine sediment analyzers has been evaluated.

DESIGN AND METHODS

209 urine samples were analyzed by the Iris iQ200 ELITE (İris Diagnostics, USA), Dirui FUS-200 (DIRUI Industrial Co., China) automatic urine sediment analyzers and by manual microscopic examination. The degree of concordance (Kappa coefficient) and the rates within the same grading were evaluated.

RESULTS

For erythrocytes, leukocytes, epithelial cells, bacteria, crystals and yeasts, the degree of concordance between the two instruments was better than the degree of concordance between the manual microscopic method and the individual devices. There was no concordance between all methods for casts.

CONCLUSION

The results from the automated analyzers for erythrocytes, leukocytes and epithelial cells were similar to the result of microscopic examination. However, in order to avoid any error or uncertainty, some images (particularly: dysmorphic cells, bacteria, yeasts, casts and crystals) have to be analyzed by manual microscopic examination by trained staff. Therefore, the software programs which are used in automatic urine sediment analysers need further development to recognize urinary shaped elements more accurately. Automated systems are important in terms of time saving and standardization.

Automated urinalysis: first experiences and a comparison between the Iris iQ200 urine microscopy system, the Sysmex UF-100 flow cytometer and manual microscopic particle counting

BACKGROUND

Automated analysis of insoluble urine components can reduce the workload of conventional microscopic examination of urine sediment and is possibly helpful for standardization. We compared the diagnostic performance of two automated urine sediment analyzers and combined dipstick/automated urine analysis with that of the traditional dipstick/microscopy algorithm.

METHODS

A total of 332 specimens were collected and analyzed for insoluble urine components by microscopy and automated analyzers, namely the Iris iQ200 (Iris Diagnostics) and the UF-100 flow cytometer (Sysmex).

RESULTS

The coefficients of variation for day-to-day quality control of the iQ200 and UF-100 analyzers were 6.5% and 5.5%, respectively, for red blood cells. We reached accuracy ranging from 68% (bacteria) to 97% (yeast) for the iQ200 and from 42% (bacteria) to 93% (yeast) for the UF-100. The combination of dipstick and automated urine sediment analysis increased the sensitivity of screening to approximately 98%.

CONCLUSIONS

We conclude that automated urine sediment analysis is sufficiently precise and improves the workflow in a routine laboratory. In addition, it allows sediment analysis of all urine samples and thereby helps to detect pathological samples that would have been missed in the conventional two-step procedure according to the European guidelines. Although it is not a substitute for microscopic sediment examination, it can, when combined with dipstick testing, reduce the number of specimens submitted to microscopy. Visual microscopy is still required for some samples, namely, dysmorphic erythrocytes, yeasts, Trichomonas, oval fat bodies, differentiation of casts and certain crystals.

Asymptomatic hyperleukocyturia in hemodialysis patients analyzed by the automated urinary flow cytometer

The number of urinary white blood cells (WBC) is useful marker of urinary tract infection in patients without renal insufficiency. On the other hand, it is unclear what urinalysis data are normal in hemodialysis (HD) patients. The occurrence and numbers of WBC in 75 asymptomatic HD patients and 133 healthy individuals as controls were determined by the automated urinary flow cytometer (UF-100, Sysmex Co., Kobe, Japan). The finding of hyperleukocyturia (>10 WBC per high power field of 400x microscopic magnification) was observed in 46.7% of HD patients. Urinary WBC significantly increased with decrease in the urinary volume. It may not be appropriate to indiscriminately apply the diagnostic criteria of pyuria for patients with normal renal function to dialysis patients because the number of urinary WBC was closely related to the urinary volume in HD patients.

Field evaluation of a second-generation cytometer UF-100 in diagnosis of acute urinary tract infections in adult patients

AIMS

The authors evaluated the analytical performance of the Sysmex UF-100 cytometer vs. the diagnosis of urinary tract infections (UTI).

METHODS

We considered 2010 subjects, aged between 18 and 78, 870 males and 1140 females. The majority (90.2%) of the samples were voided urine specimens collected by using the midstream technique. Each sample was subjected to microbiological evaluation (culture + residual antibacterial activity), dipstick tests, UF-100 examination and microscopic observation. In order to obtain a final diagnosis of UTI these laboratory results were taken into consideration together with clinical data and patients' characteristics. The analytical performance of the laboratory tests was obtained by adopting this diagnosis as standard practice.

RESULTS

Out of the total 2010 subjects considered a clinical diagnosis of UTI was obtained in 529 cases (26.32%). The UF-100-based screening had sensitivity, 0.94; specificity, 0.93; positive predictive value, 0.83; negative predictive value, 0.98; and correctly classified incidence, 0.93.

CONCLUSIONS

In our experience the results of the UF-100-based screening show a very good correlation with the diagnosis of acute UTI in adults patients.

Automated urinalysis. Evaluation of the Sysmex UF-50

We assessed the Sysmex UF-50 for reproducibility of results and carryover rate by performing between- and within-run precision analyses on 315 urine samples, evaluated the feasibility of using the UF-50 to measure urinary cellular and noncellular components by comparing results from the UF-50 with results of manual urinalysis using the Kova system, and performed side-by-side comparison of the within-run reproducibility from the UF-50, the UF-100, and the Kova system. Results from the UF-50 and UF-100 were highly reproducible, and the carryover rate was 0.5% or less for the urinary components. In between-run precision assays, the coefficients of variation for UF-50 results for all cellular components were less than 10%. The agreement (gamma statistics) between values from the UF-50 and the Kova system was excellent for RBC, WBC, and bacterial counts. The cell counts from the UF-50 for RBCs, WBCs, epithelial cells, and bacteria were 52%, 63%, 54%, and 110%, respectively, of those measured by manual urinalysis. The UF-50 performed quantitative analysis in 72 seconds, compared with 330 seconds for manual methods. The UF-50 is suitable for the first screening to detect hematuria, pyuria, and bacteriuria.

Automated Flow Cytometry Compared with an Automated Dipstick Reader for Urinalysis

Recently, the Sysmex UF-100 flow cytometer was developed to automate urinalysis. We compared UF-100 test results with those of an automated dipstick reader. A cross-check of UF-100, dipstick, and microscopic sediment data was performed in 1001 urine samples. Good agreements (P &lt;0.001) were obtained between UF-100 and dipstick data for erythrocytes (r = 0.636) and leukocytes (r = 0.785). Even in urine with low conductivity, the UF-100 could detect lysed erythrocytes. The UF-100 bacterial count was higher among nitrite-positive urine samples (P &lt;0.0001) and was positively correlated with the UF-100 leukocyte count (r = 0.745; P &lt;0.001). In stored urine (24 h), bacterial counts increased, whereas the forward light scatter of leukocytes decreased (P &lt;0.01). Casts and yeast cells reported by the UF-100 should be confirmed by microscopic review because false positives occurred. We suggest that a computer-assisted cross-check of UF-100 and dipstick data allows a clinically acceptable sieving system to reduce the workload of microscopic sediment urinalysis.