CYSTOCENTESIS AND URINALYSIS IN ZOOMEDICINE: AN UNDERESTIMATED TOOL FOR LARGE FELID STANDARD HEALTH CHECKS.

Chronic kidney disease (CKD) is a prevalent disease among felids; yet its origin is still poorly understood, and the disease often remains asymptomatic for years, underscoring the need for early diagnosis. This study aimed to investigate the diagnostic value of urinalysis in accurately staging CKD, particularly as routine health checks in large felids often overlook its significance. In this research, ultrasound-guided cystocentesis (UGC) was performed on 50 captive nondomestic felids during routine veterinary health checks under general anesthesia. Urinalysis included microscopic examination of the sediment, measurement of urine specific gravity (USG) and protein to creatinine ratio (UPC). Additional serum kidney markers, such as creatinine and symmetric dimethylarginine, were compared with USG and UPC to assess their diagnostic value as urinary biomarkers. The results demonstrated proteinuria (UPC > 0.4) or borderline proteinuria (UPC 0.2-0.4) in 49% of the animals. Among these cases, 62% were of renal origin, and 38% were postrenal causes. USG was significantly higher in felids with borderline proteinuria compared to those with proteinuria. A moderate, but significant negative correlation between serum parameters and USG was observed, emphasizing the importance of assessing both diagnostic parameters during kidney evaluations. Additionally, felids with CKD have an increased risk of urinary tract infections, necessitating microscopic urinalysis and bacterial culture analysis. Abnormalities, including hematuria, pyuria, crystalluria, and bacteriuria, were found in approximately 38% of cases through microscopical examination of urine. No complications associated with UGC were observed and abnormal findings were detected in 60% of the cases. Based on these results, the authors recommend the inclusion of UGC and urinalysis as standard diagnostic tools in general health checks for nondomestic felids. This approach provides valuable insights into the early detection and staging of CKD, supporting early intervention and supportive medical care to prolong renal health in these animals.

Trends in urine sampling rates of general practice patients with suspected lower urinary tract infections in England, 2015-2022: a population-based study.

OBJECTIVES: Inappropriate prescribing of antibiotics is a key driver of antimicrobial resistance. This study aimed to describe urine sampling rates and antibiotic prescribing for patients with lower urinary tract infections (UTIs) in English general practice. DESIGN: A retrospective population-based study using administrative data. SETTING: IQVIA Medical Research Database (IMRD) data from general practices in England, 2015-2022. PARTICIPANTS: Patients who have consulted with an uncomplicated UTI in England general practices captured in the IMRD. OUTCOME MEASURES: Trends in UTI episodes (episodes were defined as UTI diagnosis codes occurring within 14 days of each other), testing and antibiotic prescribing on the same day as initial UTI consultation were assessed from January 2015 to December 2022. Associations, using univariate and multivariate logistic regressions, were examined between consultation and demographic factors on the odds of a urine test. RESULTS: There were 743 350 UTI episodes; 50.8% had a urine test. Testing rates fluctuated with an upward trend and large decline in 2020. Same-day UTI antibiotic prescribing occurred in 78.2% of episodes. In multivariate modelling, factors found to decrease odds of a urine test included age ≥85 years (0.83, 95% CI 0.82 to 0.84), consultation type (remote vs face to face, 0.45, 95% CI 0.45 to 0.46), episodes in London compared with the South (0.74, 95% CI 0.72 to 0.75) and increasing practice size (0.77, 95% CI 0.76 to 0.78). Odds of urine tests increased in males (OR 1.11, 95% CI 1.10 to 1.13), for those episodes without a same-day UTI antibiotic (1.10, 95% CI 1.04 to 1.16) for episodes for those with higher deprivation status (Indices of Multiple Deprivation 8 vs 1, 1.51, 95% CI 1.48 to 1.54). Compared with 2015, 2016-2019 saw increased odds of testing while 2020 and 2021 saw decreases, with 2022 showing increased odds. CONCLUSION: Urine testing for UTI in general practice in England showed an upward trend, with same-day antibiotic prescribing remaining consistent, suggesting greater alignment to national guidelines. The COVID-19 pandemic impacted testing rates, though as of 2022, they began to recover.

A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-Hour Urine Data.

Objectives: The absence of predictive markers for kidney stone recurrence poses a challenge for the clinical management of stone disease. The unpredictability of stone events is also a significant limitation for clinical trials, where many patients must be enrolled to obtain sufficient stone events for analysis. In this study, we sought to use machine learning methods to identify a novel algorithm to predict stone recurrence. Subjects/Patients and Methods: Patients enrolled in the Registry for Stones of the Kidney and Ureter (ReSKU), a registry of nephrolithiasis patients collected between 2015-2020, with at least one prospectively collected 24-hour urine test (Litholink 24-hour urine test; Labcorp) were included in the training set. A validation set was obtained from chart review of stone patients not enrolled in ReSKU with 24-hour urine data. Stone events were defined as either an office visit where a patient reports symptomatic passage of stones or a surgical procedure for stone removal. Seven prediction classification methods were evaluated. Predictive analyses and receiver operator characteristics (ROC) curve generation were performed in R. Results: A training set of 423 kidney stone patients with stone event data and 24-hour urine samples were trained using the prediction classification methods. The highest performing prediction model was a Logistic Regression with ElasticNet machine learning model (area under curve [AUC] = 0.65). Restricting analysis to high confidence predictions significantly improved model accuracy (AUC = 0.82). The prediction model was validated on a validation set of 172 stone patients with stone event data and 24-hour urine samples. Prediction accuracy in the validation set demonstrated moderate discriminative ability (AUC = 0.64). Repeat modeling was performed with four of the highest scoring features, and ROC analyses demonstrated minimal loss in accuracy (AUC = 0.63). Conclusion: Machine-learning models based on 24-hour urine data can predict stone recurrences with a moderate degree of accuracy.

Significance of scattered small echogenic foci floating in urinary bladder as ultrasonography finding in dogs.

BACKGROUND: Despite the prevalence of echogenic foci floating in the urinary bladder seen in ultrasonography in dogs, surprisingly little has been written on its significance, including its potential association with urinalysis. The objective of the study was to determine the diagnostic value of the echogenic foci floating in urinary bladders in dogs. RESULTS: - Cystosonography was performed on 45 dogs. Bladder contents were examined and divided into positive (containing echogenic particles) and negative (absent echogenic particles) groups according to the presence and absence of floating echogenic particles. Five mL of urine was collected via cystocentesis. Urine analysis and culture were done and the relationship between ultrasound evaluation and urinalysis results was investigated. In dogs with bladder echogenic particles in ultrasonography, the prevalence of hematuria, pyuria, bacteriuria, and lipiduria were 88.9%, 92.6%, 29.6%, and 70.3%, respectively. However, in dogs in which echogenic particles were not observed in their bladders, the prevalence of hematuria, pyuria, bacteriuria, and lipiduria was 77%, 50%, 5.5%, and 77%, respectively. There was a significant association between bladder debris and positive urine culture, with an odds ratio of 7.15 (95% confidence interval: 0.81-63.28) compared with matched controls. Furthermore, there was a significant relationship between the presence of floating echogenic particles with pyuria, and urine color ( p ≤ 0.05). CONCLUSION: In conclusion, the present results showed the detection of bladder debris on ultrasound can be a predictor for pyuria and positive urine culture in dogs.

Age-related trends in urine specific gravity in apparently healthy cats.

OBJECTIVES: Urine specific gravity (USG) is the most common method for the estimation of urine concentration in cats. Utilization of USG as a screening tool is easily accessible and is of low cost to the client if strategically utilized in settings of higher diagnostic value. There is currently minimal population information regarding how USG changes across ages in cats. METHODS: Data were collected from electronic pet medical records from more than 1000 hospitals and screened for cats with an apparently healthy clinical status and complete diagnostic information. USG was compared with age in multiple analyses to examine the relationship between the variables. RESULTS: In the absence of other indicators of disease, renal concentrating ability begins to diminish, on average, starting at approximately 9 years of age. By age group, cats aged 11-15 years (1.044, 95% confidence interval [CI] 1.043-1.044) had statistically significantly lower mean USGs compared with cats aged less than 1 year (1.049, 95% CI 1.048-1.051; P <0.001), 1-6 years (1.049, 95% CI 1.049-1.050; P <0.001) or 7-10 years (1.049, 95% CI 1.048-1.049; P <0.001). Cats aged ⩾15 years (1.038, 95% CI 1.036-1.040) had statistically significantly lower mean USGs compared with cats aged less than 1 year (P <0.001), 1-6 years (P <0.001), 7-10 years (P <0.001) or 11-15 years (P <0.001). CONCLUSIONS AND RELEVANCE: Renal concentrating ability begins to diminish, on average, starting at approximately 9 years of age and is progressive as cat age increases. This study provides important and new information to help improve screening practices for disorders of concentrating ability in cats.

Advances and Progress in Automated Urine Analyzers.

The clinical analysis of urine has classically focused on conventional chemical-based urinalysis and urine microscopy. Contemporary advances in both analysis subsets have started to employ new technologies such as automated image analysis, flow cytometry, and mass spectrometry. In addition to new detection technologies, current analyzers have incorporated more advanced imaging, automated sample handing, and machine learning analyses into their workflow. The most advanced semiautomated analyzers can be interfaced with hospital medical record systems, and in the point-of-care setting, smartphones can be used for image analysis. This review will discuss current technological advancements in the field of urinalysis and urine microscopy.

The Importance of Urine Sediment Analysis.

BACKGROUND: Urinary sediment is an important part of routine urine test, which plays an irreplaceable role in the diagnosis of diseases, monitoring of treatment effect, and prognosis judgment [1]. METHODS: Through the results of urine dry chemistry and microscopic examination of urinary sediment, we inter-preted and analyzed the clinical significance of urinary casts in urinary sediment. RESULTS: In patients with new urinary system diseases abnormal urine results appear earlier than changes in serum renal function indicators, especially when the urine sediment shows typical casts, which can provide an important basis for clinical diagnosis. CONCLUSIONS: Clinical laboratory personnel should attach great importance to the morphological examination of urinary sediment and master the diagnostic significance of the formed components of urinary sediment for various diseases, so as to better assist clinical disease diagnosis.

Detection of Mycobacterium tuberculosis transrenal DNA in urine samples among adults in Peru.

Diagnosis of pulmonary tuberculosis (TB) relies on a sputum sample, which cannot be obtained from all symptomatic individuals. Mycobacterium tuberculosis (Mtb) transrenal DNA (trDNA) has been detected in urine, an easily obtainable, noninvasive, alternative sample type. However, reported sensitivities have been variable and likely depend on collection and assay procedures and aspects of trDNA biology. We analyzed three serial urine samples from each of 75 adults with culture-confirmed pulmonary TB disease in Lima, Peru for detection of trDNA using short-fragment real-time PCR. Additionally, we examined host, urine, and sampling factors associated with detection. Overall per-sample sensitivity was 38 % (95 % Confidence Interval [CI] 30-45 %). On an individual level (i.e., any of the three samples positive), sensitivity was 73 % (95 % CI: 62-83 %). Sensitivity was highest among samples from patients with smear-positive TB, 92 % (95 % CI: 62-100 %). Specificity from a single sample from each of 10 healthy controls was 100 % (95 % CI: 69-100 %). Adjusting our assay positivity threshold increased individual-level sensitivity to 88 % (95 % CI: 78-94 %) overall without affecting the specificity. We did not find associations between Mtb trDNA detection and individual characteristics or urine sample characteristics. Overall, our results support the potential of trDNA detection for TB diagnosis.

Dryfilm-ATR-FTIR analysis of urinary profiles as a point-of-care tool to evaluate aerobic exercise.

The understanding of metabolic alterations triggered by intense exercise can provide a biological basis for the development of new training and recovery methods. One popular way to monitor these changes is the non-invasive analysis of the composition of urine. This work evaluates the use of attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multivariate analysis as a rapid and cost-effective way to investigate changes in urine composition after intense exercise. The urine FTIR spectra of 21 volunteers (14 going through aerobic exercise and 7 controls) were measured before and immediately, 2, 5, 11, and 24 h after running 10 km. Principal component analysis (PCA) and partial least squares analysis (PLS) regression were used to investigate the changes in the spectra as a function of the recovery time. PLS models obtained for the prediction of the time points in the exercise group were deemed significant (p < 0.05, rand t-test permutation testing in cross-validation), showing changes in the urine composition after the exercise, reaching a maximum after 11 hours as opposed to the control group which did not show any significant relationship with the recovery time. In a second step, spectra of the protean extract isolated from urines at significant timepoints (before, immediately after, and 11 hours after exercise) were measured. The PCA of the protein spectra showed clear differences in the spectra obtained at the separation between the recovery time points, especially after the end of the exercise, where the protein profile was significantly different from the other times. Results indicate that the technique was able to find differences in the urine after physical exertion and holds strong potential for an easy-to-use and simple screening metabolic evaluation of recovery methods.

[Importance of Measuring the Urine Protein/Creatinine Ratio in Patients with Advanced Hepatocellular Carcinoma Receiving Atezolizumab plus Bevacizumab Treatment].

INTRODUCTION: When we administer atezolizumab plus bevacizumab treatment to patients with advanced hepatocellular carcinoma, we often encounter inconsistent results between the qualitative dipstick urinalysis and the urine protein/creatinine ratio(UPCR)measurements. In this study, we investigated the relationship between qualitative dipstick urinalysis and UPCR in these patients, and assessed whether incorporating UPCR into the testing protocol could prevent unnecessary interruptions during bevacizumab treatment. SUBJECTS AND METHODS: This study analyzed 298 urine samples collected from 61 patients of advanced hepatocellular carcinoma, who were treated with atezolizumab plus bevacizumab at our institution between October 1, 2020, and August 31, 2021. We used UPCR as an alternative test to the 24-hour urine protein and set the discontinuation criteria for bevacizumab at a UPCR of 2.0 or higher. RESULTS: Among the 41 samples that tested positive for 2+ on the dipstick test, only one(2.4%)had a UPCR exceeding 2.0. Additionally, among the 44 samples that showed a 3+ result, 24 samples(54.5%)had a UPCR higher than 2.0. If our decision to discontinue bevacizumab had been based on a dipstick urinalysis result of 2+, we could have continued administering bevacizumab in 97.6%(40/41)of the cases. Even if the decision had been based on a dipstick urinalysis result of 3+, we could have continued administering bevacizumab in almost half of the cases(45.5%, 20/44). CONCLUSIONS: Our findings suggest that the addition of UPCR to the qualitative dipstick urinalysis during atezolizumab plus bevacizumab treatment for patients with advanced hepatocellular carcinoma could help prevent unnecessary interruptions of bevacizumab and offer more clinical benefits in real-world practice, compared to using qualitative dipstick urinalysis alone.

Utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients.

BACKGROUND: The successful treatment and improvement of acute kidney injury (AKI) depend on early-stage diagnosis. However, no study has differentiated between the three stages of AKI and non-AKI patients following heart surgery. This study will fill this gap in the literature and help to improve kidney disease management in the future. METHODS: In this study, we applied Raman spectroscopy (RS) to uncover unique urine biomarkers distinguishing heart surgery patients with and without AKI. Given the amplified risk of renal complications post-cardiac surgery, this approach is of paramount importance. Further, we employed the partial least squares-support vector machine (PLS-SVM) model to distinguish between all three stages of AKI and non-AKI patients. RESULTS: We noted significant metabolic disparities among the groups. Each AKI stage presented a distinct metabolic profile: stage 1 had elevated uric acid and reduced creatinine levels; stage 2 demonstrated increased tryptophan and nitrogenous compounds with diminished uric acid; stage 3 displayed the highest neopterin and the lowest creatinine levels. We utilized the PLS-SVM model for discriminant analysis, achieving over 90% identification rate in distinguishing AKI patients, encompassing all stages, from non-AKI subjects. CONCLUSIONS: This study characterizes the incidence and risk factors for AKI after cardiac surgery. The unique spectral information garnered from this study can also pave the way for developing an in vivo RS method to detect and monitor AKI effectively.

The applicability of the urine color scale in pediatric urology.

AIMS: To determine the level of agreement between healthcare professionals, patients and their parents/guardians in the interpretation of the urine color scale (UCS) in cases of urinary dysfunction, analyzing the applicability of the scale as a diagnostic tool determining the hydration status. METHODS: This was a cross-sectional study involving 5-17-year-old patients with lower urinary tract symptoms (LUTS) and enuresis. The study was conducted in a public healthcare referral center for pediatric urology in the Brazilian state of Bahia between October 2019 and March 2020. The Kolmogorov-Smirnov test was used to assess the distribution of the variables. Agreement was assessed using the kappa coefficient and weighted kappa. The z-test was used to determine significant differences between the kappa and weighted kappa. The statistical analysis was conducted using SPSS, version 14, and significance was established at p < 0.05. RESULTS: Forty-four patients were included. The kappa value was 32.4% (p = 0.000) for the agreement between healthcare professionals and patients, 41.9% (p = 0.000) for agreement between healthcare professionals and parents/guardians, and 25.0% (p = 0.001) for agreement between patients and parents/guardians. The weighted kappa was 70.6% (p = 0.000) for agreement between healthcare professionals and patients, 82.4% (p = 0.000) for agreement between healthcare professionals and parents/guardians, and 51.5% (p = 0.001) for agreement between patients and parents/guardians. There was a statistically significant difference in kappa values when the healthcare professionals were compared with the other groups. CONCLUSIONS: Although there were some inconsistencies in interpretation, the UCS proved to be a useful tool with which to evaluate patients' hydration status.

Blending Samples to Increase Accuracy and Precision of 1H NMR Urine Metabolomics.

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

An artificial intelligence-driven support tool for prediction of urine culture test results.

BACKGROUND AND AIMS: We aimed to develop an easily deployable artificial intelligence (AI)-driven model for rapid prediction of urine culture test results. MATERIAL AND METHODS: We utilized a training dataset (n = 34,584 urine samples) and two separate, unseen test sets (n = 10,083 and 9,289 samples). Various machine learning models were compared for diagnostic performance. Predictive parameters included urinalysis results (dipstick and flow cytometry), patient demographics (age and gender), and sample collection method. RESULTS: Although more complex models achieved the highest AUCs for predicting positive cultures (highest: multilayer perceptron (MLP) with AUC of 0.884, 95% CI 0.878-0.89), multiple logistic regression (MLR) using only flow cytometry parameters achieved a very good AUC (0.858, 95% CI 0.852-0.865). To aid interpretation, prediction results of the MLP and MLR models were categorized based on likelihood ratio (LR) for positivity: highly unlikely (LR 0.1), unlikely (LR 0.3), grey zone (LR 0.9), likely (LR 5.0), and highly likely (LR 40). This resulted in 17%, 28%, 34%, 9%, and 13% of samples falling into each respective category for the MLR model and 20%, 26%, 31%, 7%, and 16% for the MLP model. CONCLUSIONS: In conclusion, this robust model has the potential to assist clinicians in their decision-making process by providing insights prior to the availability of urine culture results in a significant portion of samples (∼2/3rd).

Automated identification and segmentation of urine spots based on deep-learning.

Micturition serves an essential physiological function that allows the body to eliminate metabolic wastes and maintain water-electrolyte balance. The urine spot assay (VSA), as a simple and economical assay, has been widely used in the study of micturition behavior in rodents. However, the traditional VSA method relies on manual judgment, introduces subjective errors, faces difficulty in obtaining appearance time of each urine spot, and struggles with quantitative analysis of overlapping spots. To address these challenges, we developed a deep learning-based approach for the automatic identification and segmentation of urine spots. Our system employs a target detection network to efficiently detect each urine spot and utilizes an instance segmentation network to achieve precise segmentation of overlapping urine spots. Compared with the traditional VSA method, our system achieves automated detection of urine spot area of micturition in rodents, greatly reducing subjective errors. It accurately determines the urination time of each spot and effectively quantifies the overlapping spots. This study enables high-throughput and precise urine spot detection, providing important technical support for the analysis of urination behavior and the study of the neural mechanism underlying urination.

Evaluation of the DxU 850m Iris automated urine microscopy analyzer for identifying culture-negative urine samples: From a perspective of reducing urine culture number.

We evaluated the DxU 850m Iris Urine Microscopy analyzer as a screening tool for excluding negative urine samples (n = 1337). At a cutoff of 103 colony counts·mL-1, sensitivity was 55.1 %, specificity 68.6 %. The DxU 850m Iris does not offer acceptable prediction of culture-negative urine samples at the tested cutoff.