AIMC Topic: Humans

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A dual-branch deep learning network for circulating tumor cells classification.

Journal of translational medicine
BACKGROUND: Circulating tumor cells (CTCs) in peripheral blood are crucial for prognosis, treatment response, disease monitoring, and personalized therapy. However, identifying CTCs remains challenging due to their scarcity and heterogeneity, even wi...

Integrative single-cell and machine learning approach to characterize immunogenic cell death and tumor microenvironment in LUAD.

Journal of translational medicine
BACKGROUND: Immunogenic cell death (ICD) triggers antitumor immune responses and plays a critical role in shaping the tumor microenvironment (TME). However, its specific contribution to lung adenocarcinoma (LUAD) progression and immunotherapy respons...

Machine learning model to predict mortality in patients with skin and soft tissue infection in emergency department.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: Accurately predicting mortality in patients with skin and soft-tissue infections (SSTIs) remains challenging. Machine learning models offer rapid processing, algorithmic impartiality, and strong predictive accuracy, which may improve earl...

scKAN: interpretable single-cell analysis for cell-type-specific gene discovery and drug repurposing via Kolmogorov-Arnold networks.

Genome biology
BACKGROUND: Analysis of single-cell RNA sequencing (scRNA-seq) data has revolutionized our understanding of cellular heterogeneity, yet current approaches face challenges in efficiency, interpretability, and connecting molecular insights to therapeut...

Deep learning and radiomics integration of photoacoustic/ultrasound imaging for non-invasive prediction of luminal and non-luminal breast cancer subtypes.

Breast cancer research : BCR
PURPOSE: This study aimed to develop a Deep Learning Radiomics integrated model (DLRN), which combines photoacoustic/ultrasound(PA/US)imaging with clinical and radiomics features to distinguish between luminal and non-luminal BC in a preoperative set...

Revolutionizing Wilson disease prognosis: a machine learning approach to predict acute-on-chronic liver failure.

Journal of translational medicine
BACKGROUND AND OBJECTIVES: Wilson disease (WD), an inherited copper metabolism disorder, is a cause of acute-on-chronic liver failure (ACLF), posing life-threatening risks due to rapid progression. This study aimed to develop a machine learning (ML)-...

Evaluation of ChatGPT-4's performance on pediatric dentistry questions: accuracy and completeness analysis.

BMC oral health
BACKGROUND: This study aimed to evaluate the accuracy and completeness of Chat Generative Pre-trained Transformer-4 (ChatGPT-4) responses to frequently asked questions (FAQs) posed by patients and parents, as well as curricular questions related to p...

Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning.

Journal of translational medicine
BACKGROUND: Infertility affects approximately 15% of couples globally, with assisted reproductive technologies (ARTs) becoming the primary interventions. Despite the growing use of ARTs, success rates have plateaued at around 30%, highlighting the ne...

Historical evolution and current research status of lymph node staging in gastric cancer: a review.

World journal of surgical oncology
Lymph node metastasis (LNM) is an independent prognostic factor for patients with gastric cancer (GC), and an accurate lymph node (LN) staging system is crucial for guiding adjuvant therapy and assessing patient prognosis. The most commonly used stag...

Association between geriatric nutritional risk index (GNRI) and asthma in elderly individuals aged 60 and above: a cross-sectional study of the NHANES 2005-2018.

BMC pulmonary medicine
OBJECTIVE: The geriatric nutritional risk index (GNRI) is a promising tool for predicting nutrition-related complications in older adults. This study aimed to explore the association between GNRI and asthma in individuals aged 60 and above.