The enhanced catalytic properties of bimetallic nanoparticles have been extensively investigated. In this study, bimetallic Ag-M (M = Au, Pt, or Pd) cotton fabrics were fabricated using a combination of electroless deposition and galvanic replacement reactions, and improvement in their peroxidase-mimicking catalytic activity compared to that of the parent Ag fabric was studied. The Ag-Pt bimetallic nanozyme fabric, which showed the highest catalytic activity and ability to simultaneously generate hydroxyl (•OH) and superoxide (O2•-) radicals, was assessed as a urine glucose sensor. This nanozyme fabric sensor could directly detect urinary glucose in the pathophysiologically relevant high millimolar range without requiring sample predilution. The sensor could achieve performance on par with that of the current clinical gold standard assay. These features of the Ag-Pt nanozyme sensor, particularly its ability to avoid interference effects from complex urinary matrices, position it as a viable candidate for point-of-care urinary glucose monitoring.

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.

A general urine test is considered one of the basic diagnostic tests using in healthcare. This study aimed to analyze sociodemographic factors associated with the frequency of urine testing in Poland. This cross-sectional survey was conducted using computer-assisted web interviewing (CAWI) between 1 March and 4 March 2024. A representative sample of 1113 adults in Poland (aged 18-86 years, 52.5% of whom were females) took part in the study. The survey showed that 46.3% of adults in Poland had a urinalysis in the last 12 months. One-fifth (20.7%) of the participants had a urinalysis more than a year ago but not more than 2 years ago. Moreover, 26.7% had a urinalysis performed 2-3 years ago. Among all participants, female gender (OR = 1.31 [1.01-1.68]; p < 0.05), being aged 70 years and over (OR = 2.22 [1.23-4.02]; p < 0.01), having children (OR = 1.45 [1.01-2.09]; p < 0.05), and having urologic diseases (OR = 2.34 [1.79-3.02]; p < 0.001) were significantly associated with having urinalysis in the last 12 months. Among respondents without urologic diseases, female gender (OR = 1.33 [1.02-1.74]; p < 0.05), being aged 60 years and over (p < 0.05), and being married (OR = 1.45 [1.09-1.94]; p < 0.05) were significantly associated with having a urinalysis in the last 12 months. There was no significant impact of educational level, occupational status, or financial situation on the frequency of urinalysis.

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).
CONCLUSIONS: 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.

OBJECTIVE: 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.
METHODS: A retrospective population-based study using administrative data.
METHODS: IQVIA Medical Research Database (IMRD) data from general practices in England, 2015-2022.
METHODS: Patients who have consulted with an uncomplicated UTI in England general practices captured in the IMRD.
METHODS: 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.
CONCLUSIONS: 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.

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.

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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.

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.

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.