The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.

End-stage kidney disease (ESKD) presents a significant public health challenge, with hemodialysis (HD) remaining one of the most prevalent kidney replacement therapies. Ensuring the longevity and functionality of arteriovenous accesses is challenging for HD patients. Blood flow sound, which contains valuable information, has often been neglected in the past. However, machine learning offers a new approach, leveraging data non-invasively and learning autonomously to match the experience of healthcare professionas. This study aimed to devise a model for detecting arteriovenous grafts (AVGs) stenosis. A smartphone stethoscope was used to record the sound of AVG blood flow at the arterial and venous sides, with each recording lasting one minute. The sound recordings were transformed into mel spectrograms, and a 14-layer convolutional neural network (CNN) was employed to detect stenosis. The CNN comprised six convolution blocks with 3x3 kernel mapping, batch normalization, and rectified linear unit activation function. We applied contrastive learning to train the pre-training audio neural networks model with unlabeled data through self-supervised learning, followed by fine-tuning. In total, 27,406 dialysis session blood flow sounds were documented, including 180 stenosis blood flow sounds. Our proposed framework demonstrated a significant improvement (p<0.05) over training from scratch and a popular pre-trained audio neural networks (PANNs) model, achieving an accuracy of 0.9279, precision of 0.8462, and recall of 0.8077, compared to previous values of 0.8649, 0.7391, and 0.6538. This study illustrates how contrastive learning with unlabeled blood flow sound data can enhance convolutional neural networks for detecting AVG stenosis in HD patients.

Rapid microwave synthesis of MOF microrods: Dispersive SPE coupled with UHPLC-MS/MS to determine fluoroquinolones in honey.

A novel sorbent Cu-S metal-organic framework (MOF) microrods was prepared for dispersive solid-phase extraction via microwave synthesis and used to determine 12 fluoroquinolones (FQs) in honey samples employing ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The best extraction efficiency was achieved by optimizing sample pH, sorbent quantity, eluent type/volume, and extraction and elution time. The proposed MOF exhibits advantages such as rapid synthesis time (20 min) and outstanding adsorption ability toward zwitterionic FQs. These advantages can be attributed to multiple interactions, including hydrogen bonding, π-π interaction, and hydrophobic interaction. The limits of detection of analytes were 0.005-0.045 ng g-1. Acceptable recoveries (79.3%-95.6%) were obtained under the optimal conditions. Precision (relative standard deviation, RSD) was <9.2%. These results demonstrate the utility of our sample preparation method and the high capacity of Cu-S MOF microrods for rapid and selective extraction of FQs from honey samples.

Polydopamine and silica nanoparticles magnetic solid phase extraction coupled with liquid chromatography-tandem mass spectrometry to determine phenolic acids and flavonoids in fruit wine.

Magnetic solid phase extraction (MSPE) have been widely applied in a variety of sample preparation techniques. Herein, Fe3O4@pDA as the sorbents for MSPE, were developed for the determination of phenolic acids and flavonoids in fruit wine samples in combination with LC-MS/MS. The Fe3O4@pDA were characterized by Fourier transform infrared spectroscopy (FT-IR), powder X-ray diffraction (PXRD), transmission electron microscopy (TEM), Superconducting Quantum Interference Device Magnetometer (SQUID) and thermogravimetric analysis (TGA) in detail. In the present study, a new, rapid, and efficient MSPE by LC-MS/MS was established for the extraction and sensitive detection of phenolic acids and flavonoids. Under the optimized condition of extraction procedure including the pH value of 4.0, 10 mg of Fe3O4@pDA, 60 s extraction time, and 600 µL desorption solvent volume, good responses were investigated. Results showed that the limits of detection (S/N = 3) for phenolic acids and flavonoids were in the range of 0.01-0.29 ng/ mL. The correlation coefficients of all analytes were more than 0.9985. The method was satisfactorily used for the detection of eleven analytes, and the recoveries of these targets for the two spiked wines (white grape wine and litchi wine) ranged from 80.03 to 116.68% and from 84.00 to 116.1%, respectively.

Relationships among cigarette smoking, urinary biomarkers, and urothelial carcinoma risk: a case-control study.

Cigarette smoke is a known risk factor for urothelial carcinoma (UC). However, there is limited information about the distributions and effects of volatile organic compounds (VOCs) on smoking-related UC risk. With this hospital-based case-control study, we explored the associations between urinary levels of cotinine and VOC metabolites (acrylamide, 1,3-butadiene, and benzene) and the risk of UC. Urological examinations and pathological verifications were used to confirm the diagnoses of UC. All study participants provided smoking-related information via questionnaires and face-to-face interviews; they also provided urine samples for the measurement of VOC metabolites, cotinine, and 8-hydroxydeoxyguanosine (8-OHdG), which was used as an indicator of oxidative stress. We applied multiple logistic regression analysis to estimate the risk of UC, and we found that levels of urinary cotinine and 8-OHdG were higher in the UC group than in the control group. Furthermore, urinary levels of VOC metabolites, including N-acetyl-S-(2-carbamoylethyl)-L-cysteine (AAMA), N-acetyl-S-(2-carbamoyl-2-hydroxyethyl)-L-cysteine, N-acetyl-S-(4-hydroxy-2-buten-1-yl)-L-cysteine-3, trans,trans-muconic acid (t,t-MA), and S-phenylmercapturic acid (SPMA), increased with increasing levels of urinary cotinine. After adjusting for potential risk factors, dose-response relationships were observed between UC risk and urinary levels of AAMA, t,t-MA, SPMA, and 8-OHdG. Participants with high urinary levels of cotinine, AAMA, t,t-MA, SPMA, and 8-OHdG had risks of UC that were 3.5- to 6-fold higher than those of participants with lower levels. Future, large-scale investigations of the risks of UC should be explored, and repeated measurement of VOC metabolites should be assessed.