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A blended learning approach for capacity strengthening to improve the quality of integrated HIV, TB, and malaria services during antenatal and postnatal care in LMICs: a feasibility study.
BMC Med Educ
January 2025
Department of International Public Health, Emergency Obstetric and Quality of Care Unit, Liverpool School of Tropical Medicine, Pembrooke Place, L3, 5QA, Liverpool, UK.
Background: The blended learning (BL) approach to training health care professionals is increasingly adopted in many countries because of high costs and disruption to service delivery in the light of severe human resource shortage in low resource settings. The Covid-19 pandemic increased the urgency to identify alternatives to traditional face-to-face (f2f) education approach. A four-day f2f antenatal care (ANC) and postnatal care (PNC) continuous professional development course (CPD) was repackaged into a 3-part BL course; (1) self-directed learning (16 h) (2) facilitated virtual sessions (2.
View Article and Find Full Text PDFElevated miR-221-3p inhibits epithelial-mesenchymal transition and biochemical recurrence of prostate cancer via targeting KPNA2: an evidence-based and knowledge-guided strategy.
BMC Cancer
January 2025
Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Background: Prostate cancer (PCa) is commonly occurred among males worldwide and its prognosis could be influenced by biochemical recurrence (BCR). MicroRNAs (miRNAs) are functional regulators in carcinogenesis, and miR-221-3p was reported as one of the significant candidates deregulated in PCa. However, its regulatory pattern in PCa BCR across literature reports was not consistent, and the targets and mechanisms in PCa malignant transition and BCR are less explored.
View Article and Find Full Text PDFKnowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas.
BMC Med Imaging
January 2025
Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Purpose: We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs).
Methods: 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%).
Medical large language models are vulnerable to data-poisoning attacks.
Nat Med
January 2025
Department of Neurosurgery, NYU Langone Health, New York, NY, USA.
The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development.
View Article and Find Full Text PDFA lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases.
Nat Biomed Eng
January 2025
Developing Brain Computing Lab, Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
In magnetic resonance imaging of the brain, an imaging-preprocessing step removes the skull and other non-brain tissue from the images. But methods for such a skull-stripping process often struggle with large data heterogeneity across medical sites and with dynamic changes in tissue contrast across lifespans. Here we report a skull-stripping model for magnetic resonance images that generalizes across lifespans by leveraging personalized priors from brain atlases.
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