Background Hearing loss affects access to spoken language, which can affect cognition and development, and can negatively affect social wellbeing. We present updated estimates from the Global Burden of Disease (GBD) study on the prevalence of hearing loss in 2019, as well as the condition's associated disability. Methods We did systematic reviews of population-representative surveys on hearing loss prevalence from 1990 to 2019. We fitted nested meta-regression models for severity-specific prevalence, accounting for hearing aid coverage, cause, and the presence of tinnitus. We also forecasted the prevalence of hearing loss until 2050. Findings An estimated 1•57 billion (95% uncertainty interval 1•51-1•64) people globally had hearing loss in 2019, accounting for one in five people (20•3% [19•5-21•1]). Of these, 403•3 million (357•3-449•5) people had hearing loss that was moderate or higher in severity after adjusting for hearing aid use, and 430•4 million (381•7-479•6) without adjustment. The largest number of people with moderate-to-complete hearing loss resided in the Western Pacific region (127•1 million people [112•3-142•6]). Of all people with a hearing impairment, 62•1% (60•2-63•9) were older than 50 years. The Healthcare Access and Quality (HAQ) Index explained 65•8% of the variation in national agestandardised rates of years lived with disability, because countries with a low HAQ Index had higher rates of years lived with disability. By 2050, a projected 2•45 billion (2•35-2•56) people will have hearing loss, a 56•1% (47•3-65•2) increase from 2019, despite stable age-standardised prevalence. Interpretation As populations age, the number of people with hearing loss will increase. Interventions such as childhood screening, hearing aids, effective management of otitis media and meningitis, and cochlear implants have the potential to ameliorate this burden. Because the burden of moderate-to-complete hearing loss is concentrated in countries with low health-care quality and access, stronger health-care provision mechanisms are needed to reduce the burden of unaddressed hearing loss in these settings. Funding Bill & Melinda Gates Foundation and WHO.

Purpose During this forced down-time of COVID-19 pandemic, shift to virtual anatomy education is the solitary solution to support the learning of students. The purpose of this study was to understand the visible and invisible potential challenges being faced by the 1st year medical and dental students while attending digital anatomy classes. Methods The present study was conducted on 81st year medical and dental students who were admitted to their respective college in August 2019 and were willing to participate in the study. A multiple choice close-ended questionnaire regarding their opinion on virtual classes was designed and feedback was taken from the students. Results Majority (65%) of the students agreed that they missed their traditional anatomy learning i.e., dissection courses, face to face lectures and interaction with mentors. The students strongly felt the lack of confidence and difficulty in the topics completed without dissections, models, microscopic slides and other modalities. 83% felt lack of proper gadgets, high-band width and strong internet connections, a potential barrier in their digital learning. Lack of self-motivation was felt by 69% students. Conclusions The current situation of anatomy education is not intentional, and is not the long term silver bullet solution for a visual subject like anatomy. Though learners face a lot of challenges, however, a shift to online must be supported at this time of health crisis. As the digital learning may go for indefinite period, the feedback of students may be helpful for relevant and timely modifications in digital anatomy education.

Cadavre sans anatomie : l'obscurité au temps de la pandémie Covid-19

The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.

Background: The human paracentral lobule (PCL) is the medial continuation of the precentral and postcentral gyri. It has important functional area related to the lower limb and perineum. Its visible surface that corresponds to magnetic resonance imaging scout images varies in morphology, so it requires exact data. Studies related to such data are rare. With such a facile, we studied the morphology and morphometry of PCL. Materials and Methods: Fifty formalin-fixed adult human brains dissected in the midsagittal plane were used in this study. First, the morphological types of PCL and its boundary were determined, followed by morphometry of its extrasulcal surface using digital vernier calipers. Measurements were done along the anteroposterior axis (length) and vertical axis (height). In addition to that, the extent of motor and sensory area into PCL was also measured. Results: Three distinct morphological types of PCL were found: continuous (2%), partially segmented (91%), and completely segmented type (7%). In completely segmented type, a short transitional lobulolimbic gyrus was also found in three cases. The mean extrasulcal surface of the left PCL was significantly larger, both in males (left 10.67 cm 2 vs. right 8.80 cm 2 ) and in females (left 8.80 cm 2 vs. right 6.99 cm 2 ). Irrespective of gender and sidedness, motor area was significantly larger than the sensory area. Conclusion: Reported data will be useful in diagnosis and treatment of diseases affecting the human PCL. Variations in the distribution of sensorimotor cortex over PCL may help further assessment of hemispheric lateralization and the location of central sulcus as a reliable indicator of cytoarchitectonic borders.