COVID-19

PSMA PET/CT for prostate cancer diagnosis: current applications and future directions.

Prostate cancer (PCa) requires improved diagnostic strategies beyond conventional imaging. This review aimed to evaluate the role of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) in diagnosing advanced PCa. The review analyzed the diagnostic performance of PSMA PET/CT in various clinical contexts, including locoregional staging, extracapsular ext...

Application of multivalent aptamers in tumor diagnosis, analysis and therapy (Review).

Cancer remains one of the leading causes of mortality worldwide, making early diagnosis and precise treatment crucial for enhancing patient survival rates. Previously, nucleic acid aptamers have emerged as promising tools in tumor diagnosis, analysis and therapy, owing to their high specificity, cost-effectiveness and ease of modification. Unlike monovalent aptamers, multivalent aptamers markedly ...

Accurate identification and mechanistic evaluation of pathogenic missense variants with .

Understanding the effects of missense mutations or single amino acid variants (SAVs) on protein function is crucial for elucidating the molecular basi...

Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis.

Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associa...

Artificial intelligence-based modeling of biogas production in a combined microbial electrolysis cell-anaerobic digestion system using artificial neural networks and adaptive neuro-fuzzy inference system.

Accurate prediction of biogas production is essential for optimizing process performance, enhancing system stability, and enabling efficient resource ...

UniAda: Domain Unifying and Adapting Network for Generalizable Medical Image Segmentation.

Learning a generalizable medical image segmentation model is an important but challenging task since the unseen (testing) domains may have significant...

The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [F]FDG and [Ga]Ga-PSMA in PET/CT.

This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep lear...

A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks.

Predicting the evolutionary patterns of emerging and endemic viruses is key for mitigating their spread. In particular, it is critical to rapidly iden...

Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.

AI is emerging as a promising tool for diagnosing COVID-19 based on chest CT scans. The aim of this study was the comparison of AI models for COVID-19...

Enhancing detection and monitoring of circulating tumor cells: Integrative approaches in liquid biopsy advances.

Liquid biopsy offers a minimally invasive method for detecting and monitoring cancer, with key biomarkers including circulating tumor cells (CTCs), ce...

Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy.

This study looked at possible targets for hypertrophic cardiomyopathy (HCM), a condition marked by thickening of the ventricular wall, primarily in th...

Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.

BACKGROUND: Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical approach after neoadjuvant chemotherapy (NAC). We...

Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.

Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an early stage could allow for more appropriate triage...

Accurate prediction of absolute prokaryotic abundance from DNA concentration.

Quantification of the absolute microbial abundance in a human stool sample is crucial for a comprehensive understanding of the microbial ecosystem, bu...

Detection of β-Thalassemia trait from a heterogeneous population with red cell indices and parameters.

BACKGROUND: India is home to about 42 million people with β-thalassemia trait (βTT) necessitating screening of βTT to stop spread of the disease. Over...

Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.

PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic respons...

An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.

INTRODUCTION: Machine learning models have been employed to predict COVID-19 infections and mortality, but many models were built on training and test...

Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification.

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to in...

Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer.

BACKGROUND: Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critical prognostic marker for patients with breast canc...

Harnessing genotype and phenotype data for population-scale variant classification using large language models and bayesian inference.

Variants of Uncertain Significance (VUS) in genetic testing for hereditary diseases burden patients and clinicians, yet clinical data that could reduc...