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Monocytes to Apolipoprotein A1 ratio is associated with metabolic dysfunction-associated fatty liver disease in type 2 diabetes mellitus.
Sci Rep
December 2024
Longyan First Affiliated Hospital of Fujian Medical University, Longyan, 364000, Fujian, China.
The monocyte-to-Apolipoprotein A1 ratio (MAR) emerges as a potentially valuable inflammatory biomarker indicative of metabolic dysfunction-associated fatty liver disease (MASLD). Accordingly, this investigation primarily aims to assess the correlation between MAR and MASLD risk. A cohort comprising 957 individuals diagnosed with type 2 diabetes mellitus (T2DM) participated in this study.
View Article and Find Full Text PDFMXene-based composite photocatalysts for efficient degradation of antibiotics in wastewater.
Sci Rep
December 2024
Department of Nano-Chemical Engineering, Faculty of Advanced Technologies, Shiraz University, Shiraz, Iran.
MXene-based (nano)materials have recently emerged as promising solutions for antibiotic photodegradation from aquatic environments, yet they are limited by scalability, stability, and selectivity challenges in practical settings. We formulated FeO-SiO/MXene ternary nano-photocomposites via coupled wet impregnation and sonochemistry approach for optimised tetracycline (TC) removal (the second most used antibiotic worldwide) from water using response surface methodology-central composite design (RSM-CCD). The photocatalysts containing various loading of FeO/SiO (5-45 wt%) on the MXene with a range of calcination temperatures (300-600 °C) via RSM optimisation were synthesised, characterised regarding crystallinity properties, surface morphology, binding energy, and light absorption capability, and analysed for TC degradation efficiency.
View Article and Find Full Text PDFExplainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.
Sci Rep
December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
View Article and Find Full Text PDFComparison of different puncture needles used for endoscopic ultrasound-guided fine-needle biopsy of Gastrointestinal subepithelial lesions (≤ 2 cm) with respect to the adequacy of specimen collection: study protocol for a multicenter randomized prospective trial.
Trials
December 2024
Second Department of Internal Medicine, Wakayama Medical University, 811-1, Kimiidera, Wakayama City, 641-0012, Japan.
Background: Gastrointestinal subepithelial lesions (SELs) range from benign to malignant. Endoscopic ultrasound (EUS)-guided fine-needle biopsy (EUS-FNB) is used widely for pathological diagnosis of SELs. Early diagnosis and treatment are important because all Gastrointestinal stromal tumors (GISTs) have some degree of malignant potential.
View Article and Find Full Text PDFComparison of Population Pharmacokinetic Modeling and Machine Learning Approaches for Predicting Voriconazole Trough Concentrations in Critically Ill Patients.
Int J Antimicrob Agents
December 2024
Department of Pharmacy, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510163, China. Electronic address:
Despite the widespread use of voriconazole in antifungal treatment, its high pharmacokinetic and pharmacodynamic variability may lead to suboptimal efficacy, especially in intensive care unit (ICU) patients. Machine learning (ML), an artificial intelligence modeling approach, is increasingly being applied to personalized medicine. The effectiveness of ML models for predicting voriconazole blood concentrations in ICU patients, compared to traditional population pharmacokinetics (popPK) models, has been uncertain until now.
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