AIMC Topic: Adult

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Preoperative prediction of the HER2 status and prognosis of patients with endometrial cancer using multiparametric MRI-based radiomics: a multicenter study.

Scientific reports
Non-invasive preoperative assessment of HER2 status is critical for identifying candidates for targeted therapy and personalizing treatment strategies in endometrial cancer (EC). This study aims to assess the preoperative value of multiparametric mag...

Multi-omics reveal critical roles of phosphatidylcholine and sphingomyelin in antipsychotic efficacy for schizophrenia.

Signal transduction and targeted therapy
Nearly 30% of patients with schizophrenia respond inadequately to current antipsychotics, with unclear markers and mechanisms of antipsychotic efficacy. A total of 208 patients with schizophrenia treated for 6 weeks with oral paliperidone were analyz...

Machine learning insights into obesity related genes XRCC4 and ARL6 in obstructive sleep apnea.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Obstructive sleep apnea (OSA) is highly prevalent among obese individuals, with a complex and bidirectional relationship wherein obesity not only serves as a primary risk factor for OSA but also exacerbates its severity. This interconnection...

Prediction of Personalised Hypertension Using Machine Learning in Indonesian Population.

Journal of medical systems
This study aims to enhance individual hypertension risk prediction in Indonesia using machine learning (ML) models. The research investigates the predictive accuracy of models with and without incorporating personal hypertension history, seeking to u...

Distinct electroencephalogram microstate in patients with methamphetamine use disorder and obsessive-compulsive disorder.

Journal of affective disorders
BACKGROUND: Electroencephalogram (EEG) microstates reflect momentary localized brain activity and may indicate spontaneous fluctuations within large-scale neural networks. Methamphetamine use disorder (MUD) and obsessive-compulsive disorder (OCD) exh...

Multifactorial Biomarkers for "Talk and Deteriorate" after Head Trauma Identified Using Machine Learning.

Neurologia medico-chirurgica
Talk and Deteriorate refers to a clinical course where a patient is able to speak immediately after a traumatic brain injury but subsequently deteriorates in consciousness. Talk and Deteriorate outcomes are poor, and reliable prediction may help impr...

Comparative analysis of robotic assisted vs. traditional spinal angiography in a large single-center experience.

Journal of the neurological sciences
BACKGROUND: Spinal angiography (SA) remains the gold standard for evaluating spinal cord vasculature, but traditional approaches expose operators and patients to significant ionizing radiation. Robotic-assisted platforms offer potential advantages th...

Using machine learning models to predict vaccine hesitancy: a showcase of COVID-19 vaccine hesitancy in rural populations during the pandemic.

Vaccine
Understanding vaccine hesitancy is a critical public health challenge, yet traditional statistical methods often fail to capture the complex drivers behind it. This study uses COVID-19 vaccine hesitancy in a rural population as a case study to demons...

Machine Learning Models to Predict Withdrawal of Life-Sustaining Therapy in Patients With Severe Traumatic Brain Injury.

Neurology
BACKGROUND AND OBJECTIVES: Over half of all deaths after traumatic brain injury (TBI) follow the decision to withdraw life-sustaining therapy (WLST). Despite recent improvements in TBI mortality, rates of WLST have remained unchanged, potentially ref...

Development of a Novel Hydroxylamine-Based Stable Isotope Labeling Reagent for Profiling Aldehyde Metabolic Biomarkers in Diabetes Using LC-MS/MS and Machine Learning.

Analytical chemistry
Aldehyde compounds are significantly associated with diabetes mellitus. The metabolic profile of aldehydes can enhance understanding of the mechanisms underlying development of diabetes. This study employed a pair of stable isotope labeling (SIL) rea...