AIMC Topic: Middle Aged

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Assessing photoplethysmography signal quality for wearable devices during unrestricted daily activities.

Biomedical physics & engineering express
Photoplethysmography (PPG) is widely used in wearable health monitors for tracking fundamental physiological parameters (e.g., heart rate and blood oxygen saturation) and advancing applications requiring high-quality signals-such as blood pressure as...

Continuous Physiologic Markers of Heart Rate Variability Derived From Bedside Electrocardiogram Precede Onset of Acute Respiratory Distress Syndrome: A Physiologic Modeling Study.

Critical care explorations
OBJECTIVE: Acute respiratory distress syndrome (ARDS) is estimated to be prevalent in 10% of ICU patients and results in high mortality rates of up to 45%. The recognition of ARDS can be complex and is often delayed or missed entirely. Recognition of...

Development of machine learning models for prediction of current and future dementia.

PloS one
Dementia is among the most distressing and burdensome health challenges in aging populations. Treatment efficacy is limited; however, early diagnosis can delay or prevent disease progression. Previous machine learning-based prediction models have lim...

A machine learning model for predicting 28-day mortality in ICU patients with community-acquired pneumonia and acute kidney injury.

Scientific reports
Acute kidney injury is a common and critical complication in patients with community-acquired pneumonia who are admitted to intensive care units, substantially increasing their risk of short-term mortality. To enhance early clinical decision-making, ...

Shared human-machine control of an intelligent bionic hand improves grasping and decreases cognitive burden for transradial amputees.

Nature communications
Bionic hands can replicate many movements of the human hand, but our ability to intuitively control these bionic hands is limited. Humans' manual dexterity is partly due to control loops driven by sensory feedback. Here, we describe the integration o...

Development and validation of a plasma-urine metabolism diagnostic model for renal cell carcinoma using machine learning.

World journal of urology
BACKGROUND: Renal cell carcinoma (RCC), which accounts for 70-90% of kidney malignancies, remains difficult to diagnose early due to its asymptomatic onset and the lack of reliable biomarkers. This study aimed to develop a robust diagnostic model by ...

Identifying influential determinants of women's empowerment in Bangladesh using machine learning algorithms.

PloS one
BACKGROUND AND OBJECTIVES: Women's empowerment is a vital issue in lower-middle-income developing countries like Bangladesh, where it plays a pivotal role in advancing development across the nation. Thus, this study aimed to identify the influential ...

The Clinical Prognostic Value of Lactylation-Regulated Proteins in Gastric Cancer.

Journal of proteome research
Gastric cancer (GC) is a leading cause of cancer-related mortality globally. Histone lactylation, an emerging post-translational modification, holds promise as a therapeutic target and prognostic biomarker, though its expression patterns and clinical...

Circulating long non-coding RNAs as predictors of type 2 diabetes mellitus development: results from the CORDIOPREV study.

Cardiovascular diabetology
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a growing global health challenge. Conventional diagnostic tools have limited sensitivity and specificity for early-stage disease. In this context, long non-coding RNAs (lncRNAs) have emerged as promisin...

Construction and validation of the prediction model for kinesiophobia in older adults with chronic low back pain.

BMC geriatrics
BACKGROUND: Low back pain imposes a substantial burden on global healthcare systems. Kinesiophobia is highly prevalent among older adults with chronic low back pain, severely hindering effective intervention and treatment. However, current assessment...