AIMC Topic: Prognosis

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Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Traumatic brain injury (TBI) is a critically ill disease with a high mortality rate, and clinical treatment is committed to continuously optimizing treatment strategies to improve survival rates.

A review of the use of tumour DNA methylation for breast cancer subtyping and prediction of outcomes.

Clinical epigenetics
DNA methylation in breast tumours has been extensively studied and has provided valuable insights into the clinical heterogeneity of breast cancer. In this review, we summarise the current literature that has used DNA methylation markers to subtype b...

Machine learning-based integration identifies plasma cells-related gene signature ST6GAL1 in idiopathic pulmonary fibrosis.

BMC pulmonary medicine
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a rare, progressive, and fibrotic disease with poor prognosis that lacks treatment options. As a major component of the lung adaptive immune system, plasma cells play a crucial regulatory role during...

Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above.

BMC geriatrics
BACKGROUND: Non-ST segment elevation myocardial infarction (Non-STEMI) is a severe cardiovascular condition mainly affecting individuals aged 75 and above, who are at higher risk of mortality due to age-related vulnerabilities and other health issues...

Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.

Scientific reports
Kidney pathology of immunoglobulin A nephropathy (IgAN), which is the key finding of both diagnosis and risk stratification, involves labor-intensive manual interpretation as well as unavoidable interpreter-dependent variabilities. We propose artific...

Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types.

Scientific reports
Breast cancer is the most prevalent and lethal form of cancer being the utmost common medical concern of women. Breast cancer etiology implicates numerous cellular protein receptors such as estrogen receptors (ER), progesterone receptors (PR), and hu...

Enhancing breast cancer diagnosis through machine learning algorithms.

Scientific reports
Among the most important health concerns in the world, and the number one cause of death in women, is breast cancer. Bearing in mind that there are more than 100 types of cancer, each presenting different symptoms, its early detection is indeed a big...

LncRNAs regulates cell death in osteosarcoma.

Scientific reports
Despite improvements, prognosis in osteosarcoma patients remains poor, making it essential to identify additional and more robust therapeutic targets. Non-apoptotic receptor-mediated cell death (RCD), which plays a crucial role in the pathogenesis of...

Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation.

Scientific reports
Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study was to find diagnostic markers and therapeutic targets for LUAD by integrating differential gene ex...

Identification of exosome-related genes in NSCLC via integrated bioinformatics and machine learning analysis.

Scientific reports
Exosomes are crucial in the development of non-small cell lung cancer (NSCLC), yet exosome-associated genes in NSCLC remain insufficiently explored. The present study identified 59 exosome-associated differentially expressed genes (EA-DEGs) from the ...