A bibliometric study about the subject content of the articles published in the Mexican scientific journal Archives of Medical Research is reported. The journal, published by the Mexican Institute of Social Security (IMSS), is comprised of 100 regular issues and 12 special supplements giving a total amount of 1,424 reports on medical research performed in Mexico during the last 25 years. According to the type of studies published during this period, we found that there is a similar percent of biomedical and clinical reports in the journal (47 and 42%, respectively) and a low proportion of epidemiological and medical educational reports (8 and 3%, respectively). Six thematic areas of research have been permanently published in this journal: investigations on infectious and neurological diseases being the areas mainly represented (34% of the total, corresponding to 17% in each area), followed by studies in reproductive biology (10%) and endocrine (7%), oncological (5%) and cardiovascular (3%) diseases. The tendency of the subjects covered by the journal during this period shows an increment in reports on infectious and parasitic disorders together with an increase in publications related to medicinal plant pharmacology; reproductive biology and endocrine studies show also an increasing tendency. On the other hand, a moderate decrease in studies related to neurological, oncological and cardiovascular diseases is observed. The origin of contributions during the last five years has balanced the proportion of papers published from IMSS scientists, other Mexican biomedical researchers and foreign contributions, thus reflecting favorably the recent changes in the journal's policies. This journal represents a clear example of a scientific publication edited in a developing country, originating as a national publication that evolved progressively into an international biomedical journal.
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Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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January 2025
Imaging AI Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
Background: Publicly available data are essential for the progress of medical image analysis, in particular for crafting machine learning models. Glioma is the most common group of primary brain tumors, and magnetic resonance imaging (MRI) is a widely used modality in their diagnosis and treatment. However, the availability and quality of public datasets for glioma MRI are not well known.
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March 2025
Laboratory Section, Medical Commission Department, Ministry of Public Health, Doha, Qatar.
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January 2025
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China.
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Institute for Medical Research, National Institutes of Health, Ministry of Health, Block C, 1 Jalan Setia Murni U13/52, Seksyen U13 Setia Alam, 40170 Selangor, Malaysia.
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