March 2025 - 16 articles
Background: Critical studies have unwaveringly established the importance of peculiar single-nucleotide polymorphisms (SNPs) in apolipoproteins (Apos) genes as genetic risk factors for dyslipidemias and their related comorbidities. In this study, we...
Large language models (LLMs) have emerged as powerful tools for (semi-)automating the initial screening of abstracts in systematic reviews, offering the potential to significantly reduce the manual burden on research teams. This paper provides a broa...
Background: Epilepsy is one of the most common and devastating neurological disorders, manifesting with seizures and affecting approximately 1–2% of the world’s population. The criticality of seizure occurrence and associated risks, combi...
Background: Variations in the states of Gene Regulatory Networks significantly influence disease outcomes and drug development. Boolean Networks serve as a tool to conceptualize and understand the complex relationships between genes. Threshold comput...
Background: Large Language Models (LLMs) have demonstrated strong performances in clinical question-answering (QA) benchmarks, yet their effectiveness in addressing real-world consumer medical queries remains underexplored. This study evaluates the c...
Background: Understanding the dynamics of HIV transmission in heterogeneous populations is crucial for effective prevention strategies. This study introduces the Risk Modulation Point (RMP), a novel threshold identifying where HIV transmission transi...
Background: In clinical practice, identifying the location and extent of tumors and lesions is crucial for disease diagnosis and treatment. Artificial intelligence, particularly deep neural networks, offers precise and automated segmentation, yet lim...
Background: Tumor mutation burden (TMB), a genomic biomarker, has proven to be a strong predictor of immunotherapy response but is not widely adopted. This study investigates the association between TMB and immune checkpoint inhibitors (ICIs) respons...
EEG microstates are brief, stable topographical configurations of brain activity that provide insights into alterations in brain function and connectivity. Anomalies in microstates are associated with different neuropsychiatric conditions, especially...
Background: Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different...
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