AIMC Topic: Female

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Functional, Anatomical, and Morphological Networks Highlight the Role of Basal Ganglia-Thalamus-Cortex Circuits in Schizophrenia.

Schizophrenia bulletin
Evidence from electrophysiological, functional, and structural research suggests that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. However, most previous studies have focused on single modalities only, ...

Expert artificial intelligence-based natural language processing characterises childhood asthma.

BMJ open respiratory research
INTRODUCTION: The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert a...

Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning.

Medical archives (Sarajevo, Bosnia and Herzegovina)
INTRODUCTION: The World Health Organization has estimated that 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the developed countries are due to cardiovascular diseases. The early prognosis of cardiovascular d...

Identification of prognostic immune-related signature predicting the overall survival for colorectal cancer.

European review for medical and pharmacological sciences
OBJECTIVE: The morbidity and mortality of patients with colorectal cancer, one of the most common malignant tumors worldwide, is steadily increasing. The aim of this study was to investigate the association between prognostic immune-related gene prof...

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.

Journal of biomedical optics
SIGNIFICANCE: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biops...

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study.

Lancet (London, England)
BACKGROUND: Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal ...