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Using Fourier Transform Infrared spectroscopy (FTIR), it is possible to show chemical composition of materials and / or profile chemical changes occurring in tissues, cells, and body fluids during onset and progression of diseases. For diagnostic application, the use of blood would be the most appropriate in biospectroscopy studies since, (i) it is easily accessible and, (ii) enables frequent analyses of biochemical changes occurring in pathological states. At present, different studies have investigated potential of serum, plasma and sputum being alternative biofluids for lung cancer detection using FTIR.

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Pneumococcal infections are a serious health issue associated with increased morbidity and mortality. This systematic review evaluated the efficacy, effectiveness, immunogenicity, and safety of the pneumococcal conjugate vaccine (PCV)15 compared to other pneumococcal vaccines or no vaccination in children and adults. We identified 20 randomized controlled trials (RCTs).

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Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.

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A real-world pharmacovigilance analysis of potential ototoxicity associated with sacubitril/valsartan based on FDA Adverse Event Reporting System (FAERS).

Sci Rep

December 2024

Department of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.

Sacubitril/valsartan, a first-in-class angiotensin receptor neprilysin inhibitor, is widely used to treat heart failure. Despite its efficacy, sacubitril/valsartan inevitably causes adverse events such as hypotension, renal dysfunction, hyperkalemia, and angioedema. Sacubitril/valsartan-associated ototoxicity is often underreported in clinical studies and real-world settings.

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This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.

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