Definition and clinical variability of SHANK3-related Phelan-McDermid syndrome.

Phelan-McDermid syndrome (PMS) is an infrequently described syndrome that presents with a disturbed development, neurological and psychiatric characteristics, and sometimes other comorbidities. As part of the development of European medical guidelines we studied the definition, phenotype, genotype-phenotype characteristics, and natural history of the syndrome. The number of confirmed diagnoses of PMS in different European countries was also assessed and it could be concluded that PMS is underdiagnosed. The incidence of PMS in European countries is estimated to be at least 1 in 30,000. Next generation sequencing, including analysis of copy number variations, as first tier in diagnostics of individuals with intellectual disability will likely yield a larger number of individuals with PMS than presently known. A definition of PMS by its phenotype is at the present not possible, and therefore PMS-SHANK3 related is defined by the presence of SHANK3 haploinsufficiency, either by a deletion involving region 22q13.2-33 or a pathogenic/likely pathogenic variant in SHANK3. In summarizing the phenotype, we subdivided it into that of individuals with a 22q13 deletion and that of those with a pathogenic/likely pathogenic SHANK3 variant. The phenotype of individuals with PMS is variable, depending in part on the deletion size or whether only a variant of SHANK3 is present. The core phenotype in the domains development, neurology, and senses are similar in those with deletions and SHANK3 variants, but individuals with a SHANK3 variant more often are reported to have behavioural disorders and less often urogenital malformations and lymphedema. The behavioural disorders may, however, be a less outstanding feature in individuals with deletions accompanied by more severe intellectual disability. Data available on the natural history are limited. Results of clinical trials using IGF-1, intranasal insulin, and oxytocin are available, other trials are in progress. The present guidelines for PMS aim at offering tools to caregivers and families to provide optimal care to individuals with PMS.

HybridFaceMaskNet: A Novel Face-Mask Detection Framework Using Hybrid Approach (preprint)

Coronavirus disease 2019 (covid-19 ) is a contiguous disease which is caused by severe acute respiratory syndrome coronavirus2(SARAS-2) started from Wuhan, china, and spread all over the world within a few months in 2019. Government of all countries had to apply lockdown to decrease the number of affected patient as mortality rate of many countries became very high at that time. In the awake of 2nd wave of COVID 19 WHO has made mandatory to use mask in largely crowded areas, health centers, communities and in different places to prevent spread of virus. Many countries have invented the vacancies but it will firstly available for corona front line warriors only, not for general people, So, people have to wear mask when they are going out from home. But In recent days it can be followed that people are reluctant to wear mask when they are entering in offices, departmental stores or local shops where, gathering might happen anytime. This could lead to spread of COVID-19 among the communities. With the help of computer-vision, people who are not wearing mask can be detected by generating an alarm signal. To achieve this challenging task, a face mask detector ‘HybridFaceMaskNet’ is proposed, which is a combination of classical Machine Learning and deep learning algorithm. ‘HybridFaceMaskNet’ can achieve state-of-art accuracy on public faces. The real challenges are the low-quality images, different distances of people from camera and dynamic lighting on the faces at daylight or in artificial light.This problem can be overcome by using different noise removal techniques. HybridFaceMaskNet is trained with three different classification of images ‘proper-mask’, ‘incorrect-mask’ and ‘no-mask’ which are collected from real life images and some synthetic data , to generate alarm for different scenario .This HybridFaceMaskNet is trained on Google Colab and is compared with different existing face mask detector model. There is a possibility of deploy the model in IOT devices as it is light weight compare to other existing models.