AIMC Topic: Middle Aged

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Development and validation of machine learning models based on stacked generalization to predict psychosocial maladjustment in patients with acute myocardial infarction.

BMC psychiatry
BACKGROUND: Psychosocial maladjustment threatens the recovery of patients with acute myocardial infarction (AMI), and early identification of patients with psychosocial maladjustment may facilitate provision of reference to targeted interventions. Th...

Characterizing Brain-Cardiovascular Aging Using Multiorgan Imaging and Machine Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The structure and function of the brain and cardiovascular system change over the lifespan. In this study, we aim to establish the extent to which age-related changes in these two vital organs are linked. Utilizing normative models and data from the ...

Research on key factors influencing Chinese designers' use of AIGC: An extension based on TAM and TRI.

PloS one
With the rapid development of AI intelligent technology, AIGC can bring an innovative revolution to art creation, providing designers with unlimited possibilities but also challenges. These challenges affect the willingness to adopt and constrain the...

Profiling the gut microbiota to assess infection risk in -colonized patients.

Gut microbes
Vornhagen et al. introduced a model combining gut microbiota structure and genotype to assess infection risk in -colonized patients. Building on their findings, we investigated the gut microbiota composition and genotype in 16 colonized patients, f...

Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma.

Oncoimmunology
Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The combined positive score (CPS) for PD-L1 is the only biomarker approved to predict response ...

Moving Beyond CT Body Composition Analysis: Using Style Transfer for Bringing CT-Based Fully-Automated Body Composition Analysis to T2-Weighted MRI Sequences.

Investigative radiology
OBJECTIVES: Deep learning for body composition analysis (BCA) is gaining traction in clinical research, offering rapid and automated ways to measure body features like muscle or fat volume. However, most current methods prioritize computed tomography...

Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI.

Investigative radiology
OBJECTIVES: Double-dose contrast-enhanced brain imaging improves tumor delineation and detection of occult metastases but is limited by concerns about gadolinium-based contrast agents' effects on patients and the environment. The purpose of this stud...

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.

Journal of intensive care medicine
BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have examined how sepsis and its onset timing affect mortality in AP. This study evaluates the association between sepsis occurrence and all-cause mortality ...

Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.

European journal of nuclear medicine and molecular imaging
PURPOSE: Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ c...

Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features.

Biological psychiatry. Cognitive neuroscience and neuroimaging
Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive biomarkers is essential for personalized treatment. However, most prior research studies have used ...