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Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.

Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.

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Background: Tumor size (TS) in pancreatic ductal adenocarcinoma (PDAC) is one of the most important prognostic factors. However, discrepancies between TS on preoperative images (TSi) and pathological specimens (TSp) have been reported. This study aims to evaluate the factors associated with the differences between TSi and TSp.

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Pharmaceuticals removal from aqueous solution by water hyacinth (Eichhornia crassipes): a comprehensive investigation of kinetics, equilibrium, and thermodynamics.

Environ Sci Pollut Res Int

January 2025

Grupo de Investigación Materiales Con Impacto (Mat&Mpac), Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 No. 30-65, 050026, Medellín, Colombia.

This study shows the efficiency of WH-C450, an adsorbent obtained from water hyacinth (WH) biomass, in the removal of sulfamethoxazole (SMX) from aqueous solutions. The process involves calcination of WH at 450 °C to produce an optimal adsorbent material capable of removing up to 73% of SMX and maximum SMX adsorption capacity of 132.23 mg/g.

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Background And Objective: Limited information is available on the pharmacokinetics of rifampicin (RIF) along with that of its active metabolite, 25-deacetylrifampicin (25-dRIF). This study aimed to analyse the pharmacokinetic data of RIF and 25-dRIF collected in adult patients treated for tuberculosis.

Methods: In adult patients receiving 10 mg/kg of RIF as part of a standard regimen for drug-susceptible pulmonary tuberculosis enrolled in the Opti-4TB study, plasma RIF and 25-dRIF concentrations were measured at various occasions.

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Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers.

J Imaging Inform Med

January 2025

College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.

This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.

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