Multiclass determination of endocrine disruptors in urine by hollow fiber microporous membrane and liquid chromatography.

Anal Biochem

Departamento de Química, Universidade Federal de Santa Catarina, Florianópolis, 88040900, SC, Brazil. Electronic address:

Published: September 2022

A simple and rapid methodology was developed using hollow fiber membrane microporous and a 96-well plate system for a high throughput multiclass determination of endocrine disruptors in human urine (diclofenac, diazepam, carbamazepine, ibuprofen, naproxen, carbofuran, methyl parathion, 17-α-ethynyl estradiol, bisphenol A and benzophenone). The quantification and detection of the chemicals were carried out by an HPLC-diode array detector. The fixed conditions for carrying out the method optimization were 1.5 mL of sample and 300 μL of solvent desorption. Multivariate and univariate models were applied to optimize the parameters of the method, achieving the following conditions: 20% diluted urine, 1-octanol of extraction solvent impregnated in the microporous membrane, 70 min extraction in pH 3.0 and 30 min with a mixture of 75% methanol and 25% acetonitrile (v/v) for the desorption. The R were ≤ 0.9973 for ibuprofen. The LOD ranged from 3.3 to 16.7 ng mL and the LOQ from 10 to 50 ng mL. Relative recoveries ranged from 71% to 126%. The repeatability (n = 3) ranged from 0.22% to 12.01%, and the intermediate precision (n = 9) ranged from 0.13% to 17.76%. The method presents a good alternative for the determination of different classes of compounds in human urine.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ab.2022.114725DOI Listing

Publication Analysis

Top Keywords

multiclass determination
8
determination endocrine
8
endocrine disruptors
8
hollow fiber
8
microporous membrane
8
human urine
8
urine
4
disruptors urine
4
urine hollow
4
fiber microporous
4

Similar Publications

Gastrointestinal tract-related cancers pose a significant health burden, with high mortality rates. In order to detect the anomalies of the gastrointestinal tract that may progress to cancer, a video capsule endoscopy procedure is employed. The number of video capsule endoscopic ( ) images produced per examination is enormous, which necessitates hours of analysis by clinicians.

View Article and Find Full Text PDF

Cervical cancer is one of the deadliest cancers that pose a significant threat to women's health. Early detection and treatment are commonly used methods to prevent cervical cancer. The use of pathological image analysis techniques for the automatic interpretation of cervical cells in pathological slides is a prominent area of research in the field of digital medicine.

View Article and Find Full Text PDF

Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer.

Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI).

View Article and Find Full Text PDF

Objective: This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning.

Materials And Methods: We compare a multi-organ segmentation approach and the fusion of multiple single-organ models, each dedicated to one OAR. All were trained using nnU-Net with the default parameters and the full-resolution configuration.

View Article and Find Full Text PDF

Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.

Adv Sci (Weinh)

December 2024

Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong SAR, China.

The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole-cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi-class classification.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!