Massive open online course (or MOOC) is a new online and open access teaching approach aimed at unlimited participation and providing interactions among students and teaching staff. These academic courses, often still free, lead to the delivery of a certificate of attendance and could soon also deliver a diploma. The MOOC "Stratégies diagnostiques des cancers" will be hosted in autumn 2016 on the platform "France Université Numérique" and will have two levels of learners: students in the field of health and biology and the general public. This MOOC will also be integrated into the teaching program of medical students of Paris Diderot University and Paris 13 University. The educational objective of this MOOC is to convey to all participants an overview of the diagnostic steps of cancers and of the various medical specialties involved in this diagnosis. The second week of the MOOC, entitled "tumor samples, macroscopic and microscopic analysis", presents the pathology specialty with the technical treatment of tissue or cell samples and the basic elements of the tissue section analysis to get a diagnosis of benign or malignant tumor. After this MOOC, it is planned to assess the impact of this new modality of teaching the pathology specialty or pathology, especially by the general public.
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http://dx.doi.org/10.1016/j.annpat.2016.08.013 | DOI Listing |
J Epidemiol Glob Health
January 2025
Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia.
Background: Rabies is a preventable yet deadly public health threat. Despite the availability of effective vaccines for both humans and animals, the persistence of rabies-related fatalities underscores the need for enhanced public education strategies. This study aimed to develop and validate a Rabies Health Education Module delivered via a Massive Open Online Course, targeting adult dog owners in Kelantan, Malaysia.
View Article and Find Full Text PDFActa Naturae
January 2024
Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, 117997 Russian Federation.
The growing incidence of infections caused by antibiotic-resistant strains of pathogens is one of the key challenges of the 21 century. The development of novel technological platforms based on single-cell analysis of antibacterial activity at the whole-microbiome level enables the transition to massive screening of antimicrobial agents with various mechanisms of action. The microbiome of wild animals remains largely underinvestigated.
View Article and Find Full Text PDFPulmonary embolism (PE) is a life-threatening condition with varied presentations, occasionally mimicking ST-segment elevation myocardial infarction (STEMI). This case highlights a 52-year-old male patient with a history of venous thromboembolism (VTE) who presented with progressive shortness of breath over a month, culminating in dyspnea at rest, and anterior ST-segment elevation on electrocardiography (ECG). The initial evaluation suggested STEMI.
View Article and Find Full Text PDFNat Methods
January 2025
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. Here we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-wide genotype-phenotype maps comprising CRISPR-Cas9-based knockouts of >20,000 genes in >30 million cells. Our optical pooled cell profiling platform (PERISCOPE) combines a destainable high-dimensional phenotyping panel (based on Cell Painting) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries.
View Article and Find Full Text PDFAnal Chem
January 2025
State Key Laboratory of Cellular Stress Biology, Institute of Artificial Intelligence, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals.
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