We examined the structural neuroplastic changes associated with the learning of computer programming in university students with no previous programming experience. They participated in a 15-week course (26 lessons) on the "Processing" computer programming language. We have conducted a longitudinal analysis of gray matter volume (GMV) in the magnetic resonance images obtained before and after learning computer programming. Significant neuroplastic changes appeared in the following 8 sites: the left frontal pole; the right frontal pole; the right medial frontal gyrus; the left cuneus; the left lateral cerebellum (posterior lobule and tuber); the medial cerebellum (uvula and tonsil); the right pallidum; and the left pallidum. The amount of change in the GMV of the right frontal pole correlated positively with the final product score. Furthermore, the amount of change in the GMV of the right medial frontal gyrus and the bilateral pallidum correlated positively with the test scores. Thus, the right frontal pole was presumably associated with the function of persistent attempts to accomplish tasks (goal achievement-related function). The right medial frontal gyrus and the bilateral pallidum were presumably related to deduction and reward functions, respectively. Therefore, multiple brain regions appear to be involved in programming learning through different functions.
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http://dx.doi.org/10.1093/cercor/bhac425 | DOI Listing |
Endocrine
January 2025
Department of Health Management, Chronic Health Management Laboratory, Henan Provincial People's Hospital, Zhengzhou, 450003, China.
Background: The impact of fatty liver disease on lumbar bone mineral density (BMD) represents an intriguing area of study, particularly in light of established research linking obesity to bone metabolism. However, there remains limited investigation into the correlation between quantifying liver fat content (LFC) and lumbar BMD among overweight and obese populations, particularly within the Chinese demographic. This study aims to accurately quantify LFC and investigate its association with lumbar BMD in overweight or obese individuals.
View Article and Find Full Text PDFBiotechnol J
January 2025
Faculty of Pharmacy, iMed.ULisboa - Research Institute for Medicines, University of Lisbon, Lisbon, Portugal.
Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer that remains an unmet medical need. Because TNBC cells do not express the most common markers of breast cancers, there is an active search for novel molecular targets in triple-negative tumors. Additionally, this subtype of breast cancer presents strong immunogenic characteristics which have been encouraging the development of immunotherapeutic approaches against the disease.
View Article and Find Full Text PDFJ Periodontol
January 2025
Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.
Background: The clinical evidence about alveolar ridge changes following molar extraction and how the alveolar bone morphology influences the ridge dimensional changes remains limited.
Methods: A total of 192 patients with 199 molar extractions were included in this retrospective study. Cone-beam computed tomography (CBCT) images of patients were obtained 0-3 months pre extraction and 6-12 months post extraction.
J Intern Med
January 2025
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Background: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.
Purpose: To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).
Material And Methods: We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.
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