[Arabic medical literature in the nineteenth century]

The first Arabic medical publication was a book on smallpox written by a French author, translated by a Syrian translator and printed in Cairo around the year 1800. A few years later in 1827, a medical school, teaching in Arabic, was opened in Cairo, followed by a similar medical school in Beirut in 1867. The two schools triggered the production of a host of Arabic textbooks, dictionaries and medical journals. Despite the lack of available information, this paper endeavours to review the Arabic medical literature that appeared at the time of these two pioneer schools in the nineteenth century

Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm was trained by Term Frequency Inverse Document Frequency (TF-IDF) bigram features, which contribute a network training model. The algorithm was tested on two different real-world datasets from the CBC news network and COVID-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97–99%. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents, which may contribute negatively to the COVID-19 pandemic.

Fighting the COVID-19 Infodemic in News articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is tested on two different real-world datasets from the CBC news network and Covid-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99 %. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents which may contribute negatively to the COVID-19 pandemic.

Fighting the COVID-19 Infodemic in New articles and False Publications: NeoNet, a Text-based Supervised Machine Learning Algorithm

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is tested on two different real-world datasets from the CBC news network and Covid-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99 %. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents which may contribute negatively to the COVID-19 pandemic.

Indonesian Authors in Geneeskundige Tijdschrift voor Nederlands Indie as Constructors of Medical Science

Access to the publication Geneeskundig Tijdschrift voor Nederlandsch-Indië (GTNI), a Dutch Indies medical journal, was limited to European doctors. Although Stovia (School ter Opleiding van Inlandsche Artsen) was established to produce indigenous (Bumiputra) doctors, its students and graduates were not given access to GTNI. In response, educators at Stovia founded the Tijdschrift Voor Inlandsche Geneeskundigen (TVIG) as a special journal for indigenous doctors. Due to limited funds, TVIG – the only scientific medical publication for indigenous doctors – ceased publication in 1922. The physicians formed Vereeniging van Inlandsche Geneeskundigen (VIG) an association for pribumi (native) doctors to express various demands for equal rights, one of which was the right to access GTNI. The protests and demands of the bumiputra doctors resulted not only in being granted reading access rights but also being able to become writers for GTNI. Bumiputra doctors who contributed to GTNI included Bahder Djohan and Johannes Leimena. However, they were not the only authors who contributed to GTNI during the Dutch East Indies era. After Indonesia became independent, both doctors played major roles in laying the foundation for Indonesia’s health education system and implementing village-based health policies. This article is part of a research project on Indonesia’s health history using the archives of the GTNI, TVIG and books written by doctors who contributed to GTNI which were published from the early twentieth century onwards. This paper reconstructs the role of GTNI writers in building Indonesian health knowledge.

MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

The need to remotely monitor people with disabilities has increased due to growth in their number in recent years. The democratization of Internet of Things (IoT) devices facilitates the implementation of healthcare-monitoring systems (HMSs) that are capable of supporting disabilities and diseases. However, to achieve their full potential, these devices must efficiently address the customization demanded by different IoT HMS scenarios. This work introduces a new approach, called Modeling Scenarios of Internet of Things (MoSIoT), which allows healthcare experts to model and simulate IoT HMS scenarios defined for different disabilities and diseases. MoSIoT comprises a set of models based on the model-driven engineering (MDE) paradigm, which first allows simulation of a complete IoT HMS scenario, followed by generation of a final IoT system. In the current study, we used a real scenario defined by a recognized medical publication for a patient with Alzheimer’s disease to validate this proposal. Furthermore, we present an implementation based on an enterprise cloud architecture that provides the simulation data to a commercial IoT hub, such as Azure IoT Central.

Preparación de un manuscrito para su presentación a una revista científica

Publishing the product of scientific work is an art and a necessity. If the results of the research are not reported through publication in a scientific journal, researchers miss the opportunity for their work to be known. Preparation of the manuscript is a challenge, particularly for researchers who begin their experience in scientific publication. Each journal has its publication standards available to authors; however, most biomedical publications follow the recommendations of the International Committee of Medical Journal Editors. This document describes the importance of the research process, each of its parts and the different types of scientific documents, but focuses on the description of original articles, highlighting practical recommendations for their proper realization, and presents the most frequent errors observed with the intention of guiding novice researchers throughout the process. Keywords: Scientific paper, Medical publication, Research, recommendations to publish, Original articles.

Multiple Voices on Authorship and Authority in Biomedical Publications

The intersection of industry sponsorship, government regulation, academic interests, and medical journals is a core interest in biomedical research, and one that overlaps with concerns in the rhetoric of health and medicine (RHM). At stake in conversations about this intersection are authority and participation: who is and is not invited to offer opinions and, even when invited, whose opinions are taken seriously. Following, colleagues with ties to the International Society of Medical Publication Professionals (ISMPP) present their ideas in response to questions about authorship and authority posed by another, who is also an RHM scholar. The answers of medical journal editors and publications professionals employed by corporate entities largely align with the view that both authorship and authority should be determined by scientific practice and knowledge rather than power relations or politics. A philosopher who gave an invited plenary talk at the national ISMPP meeting and participated in the organization’s first white paper offers a different perspective, considering the ways that fields self-constitute in part by bounding authority and authorship.

Investigate the relationship between men's health and medical publication

Background and Objectives: The role of medical papers in different disciplines in men's health has not been well-clarified. The aim was to study qualitatively and quantitatively the impacts of medical papers in different disciplines of medical science on men's health. Materials and Methods: The data have been analyzed by combining correlation analysis, principal component analysis, and multivariate regression methods. This combination method can clean input data by using its correlation with the target data. Thus, the weight of an input parameter by using the multivariate regression method can be obtained. Thus, the proposed method is used to perform personalized optimization. Results: 69.3% of the variation (P < 0.01) in the male mortality rate of China can be explained by the first principal component. 75.8% of the variation (P < 0.01) in the male life expectancy at birth in China can be explained by the first and the second principal component. Conclusion: The published medical papers in different disciplines, education, income, and GDP have equally important effects on men's health.