AIMC Topic: Middle Aged

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Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning.

PloS one
Diabetic kidney disease (DKD) is a major cause of end-stage renal disease globally, with podocytes being implicated in its pathogenesis. However, the underlying mechanisms of podocyte involvement remain unclear. The aim of the present study was to id...

Analysis of aPTT predictors after unfractionated heparin administration in intensive care units using machine learning models.

PloS one
OBJECTIVES: Predicting optimal coagulation control using heparin in intensive care units (ICUs) remains a significant challenge. This study aimed to develop a machine learning (ML) model to predict activated partial thromboplastin time (aPTT) in ICU ...

Development and temporal validation of a nomogram for predicting ICU 28-day mortality in middle-aged and elderly sepsis patients: An eICU database study.

PloS one
BACKGROUND AND OBJECTIVE: Despite advances in intensive care, sepsis remains a leading cause of mortality in intensive care unit (ICU) patients, especially middle-aged and elderly individuals. Given the limitations of conventional scoring systems and...

Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device.

Scientific reports
Mild cognitive impairment (MCI) and dementia pose significant health challenges in aging societies, emphasizing the need for accessible, cost-effective, and noninvasive diagnostic tools. Electroencephalography (EEG) is a promising biomarker, but trad...

Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia.

Scientific reports
Fertility preferences significantly influence population dynamics and reproductive health outcomes, particularly in low-resource settings, such as Somalia, where high fertility rates and limited healthcare infrastructure pose significant challenges. ...

Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.

Scientific reports
Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that...

Application of image guided analyses to monitor fecal microbial composition and diversity in a human cohort.

Scientific reports
The critical role of gut microbiota in human health and disease has been increasingly illustrated over the past decades, with a significant amount of research demonstrating an unmet need for self-monitor of the fecal microbial composition in an easil...

Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study.

Scientific reports
To enhance thrombolysis eligibility in acute ischemic stroke, we developed a deep learning model to estimate stroke onset within 4.5 h using diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images. Given the variabilit...

Automatic generation and risk stratification of carotid plaque in virtual shear wave elastography using a generative adversarial network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Shear wave elastography (SWE) is an effective ultrasound imaging technique for assessing carotid plaque vulnerability. However, acquiring SWE images typically requires costly specialized equipment and must be performed by experienced radiologists, wh...