AIMC Topic: Case-Control Studies

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Investigation of serum neuroserpin levels in pregnant women diagnosed with pre-eclampsia: a prospective case-control study.

BMC pregnancy and childbirth
OBJECTIVE: Neuroserpin, a serine protease inhibitor, is recognized for its anti-inflammatory and neuroprotective properties. Given the central role of inflammation and neurological involvement in the pathophysiology of preeclampsia, this study aimed ...

Reduced blood EPAC1 protein levels as a marker of severe coronary artery disease: the role of hypoxic foam cell-transformed smooth muscle cells.

Journal of translational medicine
BACKGROUND: Vascular smooth muscle cells loaded with cholesterol (foam-VSMCs) play a crucial role in the progression of human atherosclerosis. Exchange Protein Directly Activated by cAMP 1 (EPAC1) is a critical protein in the regulation of vascular t...

Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data.

Cancer control : journal of the Moffitt Cancer Center
IntroductionEarly diagnosis of colorectal cancer (CRC) poses a significant clinical challenge. This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data.MethodsThis retrospective, single-center stu...

Development and Validation of a Cell-Free DNA Fragmentomics-Based Model for Early Detection of Pancreatic Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Pancreatic ductal adenocarcinoma (PDAC), known for its high fatality rate, is often diagnosed in its advanced stages where surgical options are not viable. This highlights the critical need for innovative and effective early detection techni...

Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care.

BMC medicine
BACKGROUND: The aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care and prescribed medication 1 year prior to PASC diagnosis.

Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case-control study.

BMC medicine
BACKGROUND: Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs of cancers. In present study, we aimed to identify a novel optimized autoantibody panel with high di...

The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.

Scientific reports
Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive disorders such as mild cognitive impairment and early-stage Alzheimer's disease. We previously develop...

Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.

Academic radiology
RATIONALE AND OBJECTIVES: Diagnosing Parkinson's disease (PD) typically relies on clinical evaluations, often detecting it in advanced stages. Recently, artificial intelligence has increasingly been applied to imaging for neurodegenerative disorders....

Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias.

The journal of prevention of Alzheimer's disease
BACKGROUND: Over 6 million patients in the United States are affected by Alzheimer's Disease and Related Dementias (ADRD). Early detection of ADRD can significantly improve patient outcomes through timely treatment.