AIMC Topic: Biomarkers

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Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.

BMC infectious diseases
BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.

Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for diagnostic and therapeutic insights.

Renal failure
BACKGROUND: Renal fibrosis is a critical factor in chronic kidney disease progression, with limited diagnostic and therapeutic options. Emerging evidence suggests RNA-binding proteins (RBPs) are pivotal in regulating cellular mechanisms underlying fi...

Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.

Cardiovascular diabetology
BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationsh...

Identification of biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis.

Mammalian genome : official journal of the International Mammalian Genome Society
BACKGROUND: Coronary artery disease (CAD) is the leading cause of death worldwide, and aberrant phagocytosis may be involved in its development. Understanding this aspect may provide new avenues for prompt CAD diagnosis.

A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.

BMC infectious diseases
BACKGROUND: Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline afte...

Developing a nomogram model for predicting non-obstructive azoospermia using machine learning techniques.

Scientific reports
Azoospermia, defined by the absence of sperm in the ejaculate, manifests as obstructive azoospermia (OA) or non-obstructive azoospermia (NOA). Reliable predictive models utilizing biomarkers could aid in clinical decision-making. This study included ...

Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.

Translational psychiatry
We apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models, emphasizing peripheral inflammatory biomarkers, which are classified into treatment response subgro...

Simultaneous detection of trace protein biomarkers from a single drop of blood using AI-enhanced smartphone-based digital microscopy.

Biosensors & bioelectronics
The detection of early-stage diseases is often impeded by the low concentrations of protein biomarkers, necessitating sophisticated and costly technologies. In response, we have developed an advanced cyber-physical system that integrates blood plasma...

Molecular structure of NRG-1 protein and its impact on adult hypertension and heart failure: A new clinical Indicator diagnosis based on advanced machine learning.

International journal of biological macromolecules
The purpose of this study was to investigate the molecular structure of NRG-1 protein and its mechanism of action in adult hypertensive heart failure. The amino acid sequence of NRG-1 protein was analyzed by bioinformatics method. High-throughput seq...

Identification of critical biomarkers and immune infiltration in preeclampsia through bioinformatics and machine learning methods.

BMC pregnancy and childbirth
BACKGROUND: Preeclampsia (PE) is a multisystem progressive disease that occurs during pregnancy. Previous studies have shown that the immune system is involved in the placental trophoblast function and the pathological process of uterine vascular rem...