AIMC Topic: Urinalysis

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Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology.

Journal of biophotonics
Hierarchical variants of so-called deep convolutional neural networks (DCNNs) have facilitated breakthrough results for numerous pattern recognition tasks in recent years. We assess the potential of these novel whole-image classifiers for Raman-micro...

Accurate analysis of parabens in human urine using isotope-dilution ultrahigh-performance liquid chromatography-high resolution mass spectrometry.

Journal of pharmaceutical and biomedical analysis
An analytical method that utilizes isotope-dilution ultrahigh-performance liquid chromatography coupled with hybrid quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS or called UHPLC-HRMS) was developed, and validated to be highly precise and...

Design of a calix[4]arene-functionalized metal-organic framework probe for highly sensitive and selective monitor of hippuric acid for indexing toluene exposure.

Analytica chimica acta
In the present work, a novel metal-organic framework (MOF) fluorescent probe was prepared by post-synthetic modification of MIL-53-NH(Al) with carboxylatocalix[4]arene (CC[4]A). The introduced CC[4]A could not only enhance the fluorescence performanc...

Predicting Urine Culture Outcomes in Adult Patients Using Machine Learning with the Aim of Reducing Unnecessary Urine Cultures.

The journal of applied laboratory medicine
BACKGROUND: Urine cultures are frequently ordered tests with a low positivity rate. Development of a machine learning model to predict urine culture outcomes could not only reduce unnecessary urine cultures but also prevent preliminary antibiotic tre...

First Laboratory Evaluation of FUS-3000 Plus: A New-Generation Urine Analyzer.

The journal of applied laboratory medicine
BACKGROUND: Urine sediment analysis is a cornerstone of diagnostic testing. This study evaluates FUS-3000 Plus, an automated urine sediment analyzer using advanced imaging and artificial intelligence, to assess its technical performance and diagnosti...

Machine Learning Algorithms for Predicting Urinary Tract Infections: Integration of Demographic Data and Dipstick Reflectance Results.

Clinical chemistry
BACKGROUND: Urinary tract infections (UTIs) are among the most common infections encountered in healthcare settings. Current diagnostic practices often require 24-48 h due to the time needed for culture results. Given that 70%-80% of cultures return ...

Label-free urinary protein detection through machine learning analysis of single droplet evaporation patterns.

Analytica chimica acta
BACKGROUND: Chronic kidney disease (CKD) is a major global public health issue, with a steadily increasing incidence. Urinary protein detection serves as a crucial indicator for the diagnosis, monitoring and management of CKD. However, current method...

Light Bladder Net: Non-invasive Bladder Cancer Prediction by Weighted Deep Learning Approaches and Graphical Data Transformation.

Anticancer research
BACKGROUND/AIM: Bladder cancer (BCa) is associated with high recurrence rates, emphasizing the importance of early and accurate detection. This study aimed to develop a lightweight and fast deep learning model, Light-Bladder-Net (LBN), for non-invasi...

ML-UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper.

Physiological reports
The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter-laboratory di...

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

Journal of endourology
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 enro...