ObjectiveThe objective of our study was to determine whether cadaveric dissection is a necessity in medical education. Another purpose of our study was to assess the attitude and perception of consultants, residents, and fellows about cadaveric dissection and whether it helped them in their medical practices.MethodWe performed an analytical cross-sectional study among consultants, fellows, and residents of different specialty areas practicing in Punjab. A self-constructed questionnaire compromising of 41 items was used to assess the perception of doctors about cadaveric dissection and other alternative anatomy teaching methods. Consultants, fellows, and residents who were in clinical practice for more than six months were included in the study.ResultsOut of the total sample size of 842, 44.7% were female medical doctors and 55.3 % were male medical doctors. Cadaveric dissection was thought to be the most effective method for teaching anatomy by 27.9% of the doctors. Mean cadaveric dissection, prosection and didactic teaching components were scored significantly higher by doctors in surgery and allied fields (p<0.001). Doctors in the surgical and allied field were 0.55 times less likely to think that cadaveric dissection was unethical as compared to doctors working in medicine and allied fields (p<0.001).ConclusionDissection is still considered by several doctors as a valuable source of learning anatomy. However, the future of teaching anatomy does not depend on any single method. It is, in fact, the right combination of all available resources and using them in an interactive way that maximizes outcomes.

Ebola virus disease (EVD) has mostly affected economically deprived countries as limited resources adversely affect a country’s infrastructure and administration. Probing into the factors that led to the widespread outbreak, setting forth plans to counter EVD cases in developing countries, and devising definitive measures to limit the spread of the disease are essential steps that must be immediately taken. In this review we summarize the pathogenesis of EVD and the factors that led to its spread. We also highlight interventions employed by certain countries that have successfully limited the epidemic, and add a few preventive measures after studying the current data. According to the available data, barriers to prevent and control the disease in affected countries include irresolute and disorganized health systems, substandard sanitary conditions, poor personal hygiene practices, and false beliefs and stigma related to EVD. The public health sector along with the respective chief authorities in developing countries must devise strategies, keeping the available resources in mind, to deal with the outbreak before it occurs. As a first step, communities should be educated on EVD’s symptoms, history, mode of transmission, and methods of protection, including the importance of personal hygiene practices, via seminars, newspapers, and other social media. A popular opinion leader (POL) giving this information would further help to remove the misconception about the nature of the disease and indirectly improve the quality of life of affected patients and their families.Electronic supplementary materialThe online version of this article (doi:10.1186/s40249-015-0048-y) contains supplementary material, which is available to authorized users.

Interactions with embodied conversational agents can be enhanced using human-like co-speech gestures. Traditionally, rule-based co-speech gesture mapping has been utilized for this purpose. However, the creation of this mapping is laborious and often requires human experts. Moreover, human-created mapping tends to be limited, therefore prone to generate repeated gestures. In this article, we present an approach to automate the generation of rule-based co-speech gesture mapping from publicly available large video data set without the intervention of human experts. At run-time, word embedding is utilized for rule searching to get the semantic-aware, meaningful, and accurate rule. The evaluation indicated that our method achieved comparable performance with the manual map generated by human experts, with a more variety of gestures activated. Moreover, synergy effects were observed in users' perception of generated co-speech gestures when combined with the manual map.

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ObjectivesOral health is essential for general health and quality of life. It is a state of being free from mouth and facial pain, oral and throat cancer, oral infections and sores, periodontal disease, tooth decay, tooth loss, and other diseases and disorders that limit an individual’s capacity to bite, chew, smile, and speak; it affects psychosocial well-being too. The objective of our study was to assess teeth cleaning techniques and oral hygiene practices among medical students.MethodsThe data of the study were collected in two stages. The first stage involved the administration of a self-constructed questionnaire among medical students. In the second step, the students were asked to demonstrate their teeth cleaning techniques on a model. A standard teeth cleaning checklist was used to evaluate the students. The students were then given the checklist and a video on teeth cleaning techniques was shown to them. The data obtained was analyzed on IBM's statistical package for the social sciences (SPSS) version 21. ResultsOut of a total of 444 students, 256 (57.7 percent) were males while 188 (42.3 percent) were females. About 254 (57.2 percent) participants were preclinical medical students while 190 (42.8 percent) were clinical year medical students. A majority of medical students used medium consistency toothbrushes (177; 39.9 percent) and soft consistency toothbrushes (137; 30.9 percent). Most medical students (248; 55.9 percent) brushed two times a day while 163 (36.7 percent) brushed only one time. About 212 (47.7 percent) of the medical students used mouthwash along with a toothbrush while only 36 (8.1 percent) used floss along with a toothbrush. About 157 participants (35.4 percent) changed their toothbrush once in two months while 132 (26.7 percent) changed their toothbrush once in three months. The mean duration that participants brushed their teeth was 134.99 ± 69.01 seconds.ConclusionMedical students were found to have a faulty teeth cleaning technique. There is a dire need to spread awareness about correct teeth cleaning techniques because poor oral hygiene can have a detrimental effect on the overall health and quality of life of an individual.

Guillain-Barré syndrome (GBS) is a rare but severe autoimmune disease and the usage of the Brighton criteria can be a source of great help in resource-limited settings. It encompasses all ages. Late diagnosis of GBS can have a significant negative impact on the prognosis. The Brighton criteria are used to assist in the diagnosis of GBS and help distinguish between lowrisk and high-risk patients. In this article, we have discussed the challenges regarding the diagnosis of the GBS and the possible solutions that can help in the early diagnosis and management of GBS.

Social media and easy internet access have allowed the instant sharing of news, ideas, and information on a global scale. However, rapid spread and instant access to information/news can also enable rumors or fake news to spread very easily and rapidly. In order to monitor and minimize the spread of fake news in the digital community, fake news detection using Natural Language Processing (NLP) has attracted significant attention. In NLP, different text feature extractors and word embeddings are used to process the text data. The aim of this paper is to analyze the performance of a fake news detection model based on neural networks using 3 feature extractors: TD-IDF vectorizer, Glove embeddings, and BERT embeddings. For the evaluation, multiple metrics, namely accuracy, precision, F1, recall, AUC ROC, and AUC PR were computed for each feature extractor. All the transformation techniques were fed to the deep learning model. It was found that BERT embeddings for text transformation delivered the best performance. TD-IDF has been performed far better than Glove and competed the BERT as well at some stages.

Recent technological advancements have changed significantly the way news is produced, consumed, and disseminated. Frequent and on-spot news reporting has been enabled, which smartphones can access anywhere and anytime. News categorization or classification can significantly help in its proper and timely dissemination. This study evaluates and compares news category predictors' performance based on four supervised machine learning models. We choose a standard dataset of British Broadcasting Corporation (BBC) news consisting of five categories: business, sports, technology, politics, and entertainment. Four multi-class news category predictors have been developed and trained on the same dataset: Naïve Bayes, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Each category predictor's performance was evaluated by analyzing the confusion matrix and quantifying the test dataset's precision, recall, and overall accuracy. In the end, the performance of all category predictors was studied and compared. The results show that all category predictors have achieved satisfactory accuracy grades. However, the SVM model performed better than the four supervised learning models, categorizing news articles with 98.3% accuracy. In contrast, the lowest accuracy was obtained by the KNN model. However, the KNN model's performance can be enhanced by investigating the optimal number of neighbors (K) value.