AIMC Topic: C-Reactive Protein

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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...

High procalcitonin level is related to blood stream infections, gram-negative pathogens, and ICU admission in infections of adult febrile cancer patients.

Journal of the Egyptian National Cancer Institute
BACKGROUND: Blood stream infection (BSI) represent a life-threatening condition. Thus, we aimed to investigate the role of procalcitonin (PCT) and C-reactive protein (CRP) tests in adult febrile patients with BSI and other clinical infections in hosp...

Individual and integrated indexes of inflammation predicting the risks of mental disorders - statistical analysis and artificial neural network.

BMC psychiatry
OBJECTIVE: The prevalence of mental illness in Taiwan increased. Identifying and mitigating risk factors for mental illness is essential. Inflammation may be a risk factor for mental illness; however, the predictive power of inflammation test values ...

Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Endocrine journal
Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a mac...

Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.

International journal of medical sciences
The clinical manifestation of aortic dissection (AD) is complex and varied, making early diagnosis crucial for patient survival. This study aimed to identify immune-related markers to establish a nomogram model for AD diagnosis. Three datasets from G...

Role of Artificial Intelligence in Identifying Vital Biomarkers with Greater Precision in Emergency Departments During Emerging Pandemics.

International journal of molecular sciences
The COVID-19 pandemic has accelerated advances in molecular biology and virology, enabling the identification of key biomarkers to differentiate between severe and mild cases. Furthermore, the use of artificial intelligence (AI) and machine learning ...

Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.

Clinical and translational gastroenterology
INTRODUCTION: Pediatric Crohn's disease (CD) easily progresses to an active disease compared with adult CD, making it important to predict and minimize CD relapses. However, prediction of relapse at various time points (TPs) during pediatric CD remai...

Blood Biomarker Signatures for Slow Gait Speed in Older Adults: An Explainable Machine Learning Approach.

Brain, behavior, and immunity
Maintaining physical function is crucial for independent living in older adults, with gait speed being a key predictor of health outcomes. Blood biomarkers may potentially monitor older adults' mobility, yet their association with slow gait speed sti...

Psoriatic arthritis in psoriasis: optimizing the current screening system for psoriatic arthritis based on serum data from U.S. and Chinese populations.

Frontiers in immunology
BACKGROUND: Psoriatic arthritis (PSA) is an inflammatory joint disease associated with psoriasis (PSO) that can be easily missed. Existing PSA screening tools ignore objective serologic indicators. The aim of this study was to develop a disease scree...

Predicting abnormal C-reactive protein level for improving utilization by deep neural network model.

International journal of medical informatics
BACKGROUND: C-reactive protein (CRP) is an inflammatory biomarker frequently used in clinical practice. However, insufficient evidence-based ordering inevitably results in its overuse or underuse. This study aims to predict its normal and abnormal le...