AIMC Topic: Humans

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Enhanced local feature extraction of lite network with scale-invariant CNN for precise segmentation of small brain tumors in MRI.

PloS one
Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global fea...

Hazediff: A training-free diffusion-based image dehazing method with pixel-level feature injection.

PloS one
In the current environmental context, significant emissions generated by industrial and transportation activities, coupled with an unreasonable energy structure, have resulted in recurrent haze phenomena. This consequently leads to degraded image con...

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband.

PloS one
The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, ...

Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists.

Platelets
Platelets are increasingly recognized as key players not only in hemostasis, but also in immunity and inflammation. However, the mechanisms and markers underlying their activation remain incompletely understood. This study aimed to decipher how plate...

GlyTrait: A Versatile Bioinformatics Tool for Glycomics Analysis.

Journal of proteome research
We developed GlyTrait, a Python-based framework designed to enhance Glycomics analysis through the innovative calculation and interpretation of derived traits from -glycome data. Glycomics research often grapples with the interpretability and biologi...

Barriers and enablers for generative artificial intelligence in clinical psychology: a qualitative study based on the COM-B and theoretical domains framework (TDF) models.

BMC psychology
BACKGROUND: This study investigated the perceptions of care psychologists regarding the adoption of generative artificial intelligence (GenAI) in therapeutic practice. As AI continues to be integrated into various sectors, including healthcare, under...

Predicting COVID-19 patient recovery or mortality using deep neural decision tree and forest.

BMC research notes
OBJECTIVE: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health...

Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.

Journal of translational medicine
Recent advances in single-cell multi-omics technologies have revolutionized cellular analysis, enabling comprehensive exploration of cellular heterogeneity, developmental trajectories, and disease mechanisms at unprecedented resolution. Foundation mo...

MX1 is a novel crucial prognostic and therapeutic target inducing chemoresistance in right-sided colon cancer: insights from machine learning-based multi-omics analysis.

Human genomics
BACKGROUND: Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aim...

Identification of coagulation-related biomarkers in osteoarthritis and immune infiltration analysis based on bioinformatics.

Hereditas
BACKGROUND: Osteoarthritis (OA) is a common degenerative disorder characterized primarily by articular cartilage degradation and chronic inflammation. Although direct evidence elucidating the specific mechanisms underlying the coagulation-immune axis...