Articles: disease.
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Gallstone disease (GD) is a common gastrointestinal disease. Although traditional diagnostic techniques, such as ultrasonography, CT, and MRI, detect gallstones, they have some limitations, including high cost and potential inaccuracies in certain populations. This study proposes a machine learning-based prediction model for gallstone disease using bioimpedance and laboratory data. ⋯ State-of-the-art machine learning techniques were performed on the dataset to detect gallstones. The experimental results showed that vitamin D, C-reactive protein (CRP) level, total body water, and lean mass are crucial features, and the gradient boosting technique achieved the highest accuracy (85.42%) in predicting gallstones. The proposed technique offers a viable alternative to conventional imaging techniques for early prediction of gallstone disease.
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Meta Analysis
Association between CCL5, CCL11, and CCL17 polymorphisms and atopic dermatitis risk: A systematic review and meta-analysis.
Atopic dermatitis (AD) is a common and recurrent inflammatory disease with strong genetic susceptibility. The abnormal production of chemokines plays an important role in the occurrence and development of AD. ⋯ Our results show that the A allele, AG and AA + AG genotypes of the CCL5 - 403G/A polymorphism, the G allele and GG + GC genotype of the CCL5 - 28C/G polymorphism are risk factors for AD. Future studies with large population are still needed to further explore those correlations.
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The goal of this study was to investigate the clinical characteristics, prognostic variables, and survival of patients with primary mediastinal diffuse large B cell lymphoma (PMBCL) in the rituximab era. The Surveillance, Epidemiology, and End Results (SEER) database was used to identify PMBCL patients diagnosed between 2000 and 2019. The Kaplan-Meier (K-M) technique and log-rank test were used to assess overall survival (OS) and disease-specific survival (DSS). ⋯ Rituximab based immunochemotherapy has emerged as an effective treatment option, leading to enhanced OS and DSS outcomes. Furthermore, the nomograms specifically developed for PMBCL have demonstrated robustness and accuracy in forecasting OS and DSS rates at 1, 5, and 10 years. These predictive tools can be valuable for clinicians in accurately estimating prognosis and establishing personalized treatment plans and follow-up protocols.
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Acute heart failure (AHF) is one of the most common cardiovascular diseases. Early diagnosis and prognosis are essential, as they can eventually lead to a fatal condition. Recently, brain natriuretic peptide (BNP) has been recognized as one of the most popular biomarkers for AHF. Changes in glomerular filtration rate (GFR) are often observed in AHF. ⋯ Results indicated that BNP was a promising prognostic biomarker of AHF, whereas GFR was found to be negatively correlated with AHF.
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Diabetic nephropathy (DN), a multifaceted disease with various contributing factors, presents challenges in understanding its underlying causes. Uncovering biomarkers linked to this condition can shed light on its pathogenesis and support the creation of new diagnostic and treatment methods. Gene expression data were sourced from accessible public databases, and Weighted Gene Co-expression Network Analysis (WGCNA)was employed to pinpoint gene co-expression modules relevant to DN. ⋯ Our research uncovered potential biomarkers for DN through the application of WGCNA and various machine learning methods. The results indicate that 2 central genes could serve as innovative diagnostic indicators and therapeutic targets for this disease. This discovery offers fresh perspectives on the development of DN and could contribute to the advancement of new diagnostic and treatment approaches.