AIMC Topic: Biomarkers

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Machine learning prediction of thrombolysis efficacy using hs-CRP and inflammatory markers in stroke.

Medicine
The aim of this study was to investigate the relationship between serum ultrasensitive C-reactive protein (hs-CRP) levels and stroke incidence and to assess its potential role in decision-making for thrombolytic therapy in stroke. Given that hs-CRP i...

ZNF143 as a diagnostic biomarker: Insights from gene expression and immune cell infiltration in COPD and asthma.

Medicine
Chronic obstructive pulmonary disease (COPD) and asthma are common and serious respiratory diseases worldwide. Their clinical overlap and lack of specificity in current biomarkers pose a great diagnostic challenge for early diagnosis. To address this...

Machine Learning-Based Biomarker Identification for Early Diagnosis of Metabolic Dysfunction-Associated Steatotic Liver Disease.

The Journal of clinical endocrinology and metabolism
CONTEXT: Metabolic dysfunction-associated steatotic liver disease (MASLD) is an umbrella term for simple hepatic steatosis and the more severe metabolic dysfunction-associated steatohepatitis. The current reliance on liver biopsy for diagnosis and a ...

Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to play a role in its pathophysiology. Peripheral inflammatory biomarkers, such as the neutrophil-to-lymph...

Addressing bias in biomarker discovery for inflammatory bowel diseases: A multi-faceted analytical approach.

International immunopharmacology
Xiang-Guang et al. investigate the identification of novel biomarkers linked to M1 macrophage infiltration in inflammatory bowel diseases (IBD). Utilizing advanced bioinformatics and machine learning techniques, the researchers developed predictive m...

MIA and CD163 as promising diagnostic biomarkers in vascular dementia: A multi-method study combining WGCNA, machine learning with validation in animal models and clinical samples.

International immunopharmacology
Vascular dementia (VaD), the second most common form of dementia, lacks reliable biomarkers for early diagnosis. Here, we integrated weighted gene co-expression network analysis (WGCNA) with machine learning to identify novel biomarkers and immune-me...

Decoding chronic stress: From behavioral-molecular dynamics in mice to clinical implications of cortisol and IL-17 in depression severity.

Journal of affective disorders
BACKGROUNDS: The etiology of depression involves chronic stress, a recognized determinant of onset and severity. This study adopts a translational approach, utilizing a mouse model and a clinical cohort to explore the relationship between chronic str...

Identification of lipid metabolism-associated biomarkers in lupus nephritis by SVM model and therapeutic potential of Alisol B 23-acetate.

Gene
Systemic lupus erythematosus (SLE), a multifaceted autoimmune disorder, has lupus nephritis (LN) as one of its grave complications and is strongly associated with dyslipidemia. This investigation sought to delineate renal-specific lipid metabolism-re...

[Research on risk prediction of acute respiratory distress syndrome complicated with acute kidney injury: progress and challenges].

Zhonghua yi xue za zhi
The risk of acute respiratory distress syndrome (ARDS) combined with acute kidney injury (AKI) is high and the prognosis is poor. Therefore, there is an urgent need for efficient and accurate methods to improve clinical doctors' early diagnosis and p...

Alternations of Gut Microbiome and Serum Metabolome With Prolongation of the Course of Type 1 Diabetes Mellitus.

Diabetes/metabolism research and reviews
AIMS: We aimed to explore the gut microbial and serum metabolic disturbances associated with the course of type 1 diabetes mellitus (T1DM), and identify potential biomarkers for discriminating T1DM from normoglycemia individuals by machine learning.