Journal of Translational Medicine

2 weeks ago | translational-medicine.biomedcentral.com | Anna Maria |Sesto Fiorentino |Neuroradiology Unit

All procedures were performed in compliance with relevant laws and institutional guidelines, and the study has been approved by the local Ethics Committee (ethics committee registration number: Regional Ethics Committee of Tuscany, Comitato Etico Area Vasta Centro [CEAVC] 16923_oss). Signed informed consent was obtained from all the surviving patients included in the study.

2 months ago | translational-medicine.biomedcentral.com | Laith Mohammad Abualigah |Kashif Saleem |Vaclav Snasel |Hazem Migdady |Raed Abu Zitar |Aseel Smerat | +3 more

Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

Jan 31, 2025 | translational-medicine.biomedcentral.com | Henan Normal

Planarians (Dugesia constrictive) were collected from a stream in Yuquan country, Hebi City, Henan Province, China, and asexual clones were reproduced by cutting in the laboratory. The selected worm length is about 4–6 mm, and it is fed with beef liver once a week. Before the experiment, the worms were starved for at least 7 days.

Jan 16, 2025 | translational-medicine.biomedcentral.com | Hong Kong Baptist

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Jan 15, 2025 | translational-medicine.biomedcentral.com | ANU College

The chronic illness Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) evolves and perpetuates from a combination of biological and environmental determinants. Onset often occurs after a trigger event [1], such as viral infection, trauma, or toxin exposure, which induces a physiological response.