The aim of this study was to determine the use and perceived utility of various learning resources available during the first-year Integrated Human Physiology course at the dental and medical schools at Harvard University. Dental and medical students of the Class of 2018 were surveyed anonymously online in 2015 regarding their use of 29 learning resources in this combined course. The learning resources had been grouped into four categories to discern frequency of use and perceived usefulness among the categories. The survey was distributed to 169 students, and 73 responded for a response rate of 43.2%. There was no significant difference among the learning resource categories in frequency of use; however, there was a statistically significant difference among categories in students' perceptions of usefulness. No correlation was found between frequency of use and perceived usefulness of each category. Students seemingly were not choosing the most useful resources for them. These results suggest that, in the current educational environment, where new technologies and self-directed learning are highly sought after, there remains a need for instructor-guided learning.
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http://dx.doi.org/10.21815/JDE.017.063 | DOI Listing |
Int J Med Inform
January 2025
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, United Kingdom. Electronic address:
Background: Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, emerged as a global health crisis in 2019, resulting in widespread morbidity and mortality. A persistent challenge during the pandemic has been the accuracy of reported epidemic data, particularly in underdeveloped regions with limited access to COVID-19 test kits and healthcare infrastructure. In the post-COVID era, this issue remains crucial.
View Article and Find Full Text PDFJMIR Cancer
January 2025
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging.
View Article and Find Full Text PDFSemin Cancer Biol
January 2025
Biomedical Research Center, Slovak Academy of Sciences, Dubravska Cesta 9, 84505 Bratislava, Slovakia. Electronic address:
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by late detection and poor prognosis. Recent research highlights the pivotal role of epigenetic alter- ations in driving PDAC development and progression. These changes, in conjunction with genetic mutations, contribute to the intricate molecular landscape of the disease.
View Article and Find Full Text PDFLancet Glob Health
January 2025
Pathogenesis and Control of Chronic and Emerging Infections, University of Montpellier, Institut National de la Santé et de la Recherche Médicale, Montpellier, France. Electronic address:
People who use drugs show a higher incidence and prevalence of tuberculosis than people who do not use drugs in areas where Mycobacterium tuberculosis is endemic. However, this population is largely neglected in national tuberculosis programmes. Strategies for active case finding, screening, and linkage to care designed for the general population are not adapted to the needs of people who use drugs, who are stigmatised and difficult to reach.
View Article and Find Full Text PDFJ Environ Manage
January 2025
School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin, 300401, China. Electronic address:
Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to complex operational and environmental interactions. Neural Ordinary Differential Equations, Elastic Net, Stacking, and Categorical Boosting were applied as artificial intelligence methods to predict chemical oxygen demand (COD) removal efficiency, biomass productivity, biomass yield, and energy yield. Among these, the Stacking model demonstrated superior predictive performance across all targets.
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