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Biomarkers

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Unveiling the systemic impact of airborne microplastics: Integrating breathomics and machine learning with dual-tissue transcriptomics.

Journal of hazardous materials
Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for...

Machine learning approach on plasma proteomics identifies signatures associated with obesity in the KORA FF4 cohort.

Diabetes, obesity & metabolism
AIMS: This study investigated the role of plasma proteins in obesity to identify predictive biomarkers and explore underlying biological mechanisms.

Biomarker and clinical data-based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study.

BMJ health & care informatics
OBJECTIVES: To develop a machine learning-based software as a medical device to predict the endurance and outcomes of peritoneal dialysis (PD) patients in real time using effluent-measured biomarkers of the mesothelial-to-mesenchymal transition (MMT)...

Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning.

Scientific reports
Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary practice, and the leading cause of mortality in cats over five years of age. While diagnosing advanced CKD is straightforward, current routine tests ...

Complete Blood Count and Monocyte Distribution Width-Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study.

Journal of medical Internet research
BACKGROUND: Sepsis is an organ dysfunction caused by a dysregulated host response to infection. Early detection is fundamental to improving the patient outcome. Laboratory medicine can play a crucial role by providing biomarkers whose alteration can ...

Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning.

PeerJ
BACKGROUND: Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosi...

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.

Progress in neuro-psychopharmacology & biological psychiatry
Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral ...

Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.

Sensors (Basel, Switzerland)
Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are often limited by high costs, procedural risks, and patient discomfort. Non-invasive biomarkers represent a transformative alternative, providing diagno...

Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis.

Frontiers in immunology
BACKGROUND: Atherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely and accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays a criti...

Identification of Biomarkers for Response to Interferon in Chronic Hepatitis B Based on Bioinformatics Analysis and Machine Learning.

Viral immunology
Interferon (IFN) is a pivotal agent against hepatitis B virus (HBV) in clinic, but there is a lack of accurate biomarkers to predict the response to IFN therapy in patients with chronic hepatitis B (CHB). Our study aimed to investigate potential targ...