AIMC Topic: Middle Aged

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Multi-omics insights of immune cells in the risk and prognosis of idiopathic membranous nephropathy.

Communications biology
Idiopathic membranous nephropathy (IMN) is the major cause of autoimmune-related nephrotic syndrome. The role immune cells play in the risk and prognosis of IMN remains elusive. We employ multi-omics data and a variety of approaches to evaluate the c...

Machine learning pipeline with custom grid search for colorectal Raman spectroscopy data.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Colorectal cancer remains a major health burden, and its early detection is crucial for effective treatment. This study investigates the use of a handheld Raman spectrometer in combination with machine learning to classify colorectal tissue samples c...

Deep Learning-aided H-MR Spectroscopy for Differentiating between Patients with and without Hepatocellular Carcinoma.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Among patients with hepatitis B virus-associated liver cirrhosis (HBV-LC), there may be differences in the hepatic parenchyma between those with and without hepatocellular carcinoma (HCC). Proton MR spectroscopy (H-MRS) is a well-established...

Omics-driven strategy develops a tumor biomarker-derived signature to forecast the occurrence and progression of heart failure.

International journal of cardiology
BACKGROUND: Tumor biomarkers have been implicated in heart failure (HF). This study aimed to explore the role of tumor biomarkers in the occurrence and progression of HF.

Serum peptide biomarkers by MALDI-TOF MS coupled with machine learning for diagnosis and classification of hepato-pancreato-biliary cancers.

Scientific reports
This study aimed to investigate the potential of peptide mass fingerprints (PMFs) of the serum peptidome using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), in combination with machine learning algorithm...

Exploring the feasibility of AI-based analysis of histopathological variability in salivary gland tumours.

Scientific reports
This study uses artificial intelligence (AI) for differentiation between salivary gland tumours (SGT) using digitised Haematoxylin and Eosin stained whole-slide images (WSI). Machine learning (ML) classifiers were developed and tested using 320 scann...

Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherence.

Scientific reports
Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005-2024), we appl...

Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study.

BMC medical informatics and decision making
BACKGROUND: Anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy has revolutionized cancer treatment. However, it can cause immune-related adverse events, including acute kidney injury (AKI). Such adverse e...

Machine learning based analysis of leucocyte cell population data by Sysmex XN series hematology analyzer for the diagnosis of bacteremia.

Scientific reports
In clinical practice, early recognition of bacteremia leads to prognostic improvement. Recently, cell population data (CPD) from the Sysmex XN-series hematology analyzer has attracted attention as a new method for the early diagnosis of bacteremia, b...

Exposotypes in psychotic disorders.

Scientific reports
Psychiatry lags in adopting etiological approaches to diagnosis, prognosis, and outcome prediction compared to the rest of medicine. Etiological factors such as childhood trauma (CHT), substance use (SU), and socioeconomic status (SES) significantly ...