A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic

Human gait recognition is a biometric technique that has been utilized for security purposes for the last decade. Gait recognition is an appealing biometric modality that aims to identify individuals based on the way they walk. The outbreak of the novel coronavirus (COVID-19), has spread across the world. The number of people infected with COVID-19 is rising rapidly throughout the world. Even though some vaccines for this pandemic have been developed to minimize the effects of COVID-19, deep learning-based gait recognition techniques have shown themselves to be an effective tool for identifying the individuals wearing face mask in COVID-19 pandemic. These techniques play an important part in reducing the rate of COVID-19 spreading throughout the world in the context of the COVID-19 pandemic. Deep learning methods are currently dominating the state-of-the-art in gait recognition and have fostered real-world applications. The main objective of this paper is to provide a comprehensive overview of recent advancements in gait recognition with deep learning, including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. The purpose of this discussion is to identify current challenges that need to be addressed as well as to suggest some directions for future research that could be explored. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

A multicentre study reveals dysbiosis in the microbial co-infection and antimicrobial resistance gene profile in the nasopharynx of COVID-19 patients.

The impact of SARS-CoV-2 infection on the nasopharyngeal microbiome has not been well characterised. We sequenced genetic material extracted from nasopharyngeal swabs of SARS-CoV-2-positive individuals who were asymptomatic (n = 14), had mild (n = 64) or severe symptoms (n = 11), as well as from SARS-CoV-2-negative individuals who had never-been infected (n = 5) or had recovered from infection (n = 7). Using robust filters, we identified 1345 taxa with approximately 0.1% or greater read abundance. Overall, the severe cohort microbiome was least diverse. Bacterial pathogens were found in all cohorts, but fungal species identifications were rare. Few taxa were common between cohorts suggesting a limited human nasopharynx core microbiome. Genes encoding resistance mechanisms to 10 antimicrobial classes (> 25% sequence coverages, 315 genes, 63 non-redundant) were identified, with ß-lactam resistance genes near ubiquitous. Patients infected with SARS-CoV-2 (asymptomatic and mild) had a greater incidence of antibiotic resistance genes and a greater microbial burden than the SARS-CoV-2-negative individuals. This should be considered when deciding how to treat COVID-19 related bacterial infections.

Comparison of Different COVID Waves during COVID-19 Pandemic: A Retrospective Study from a Dedicated COVID-19 Facility of Karachi Pakistan

Background. In Pakistan, the first case of COVID-19 was reported in February2020. To date Pakistan has experienced 5 waves of COVID-19 and each COVID-19 wave is driven by a unique SARS CoV2 strain. In this study we have compared the clinical spectrum and outcome of different COVID-19 waves in a dedicated COVID-19 facility of Karachi. Methods. We conducted a retrospective study at the Sindh Infectious Diseases Hospital and Research Centre (SIDH &RC), a 175 bedded dedicated first Infectious Diseases Hospital of Karachi, Pakistan. The hospital was established in July 2020. All patients >16 years of age who were admitted with a positive COVID PCR from July 2020-Mar 2022 were included. First COVID wave in Pakistan lasted from Mar-July 2020, second wave from Oct 2020 to Jan 2021, third wave from Mar-May 2021, fourth from July to Sept 2021 while fifth from Jan 2022 to Mar 2022. Demographic and clinical data was collected of patients from each wave and analyzed by SPSS to compare the difference amongst different COVID waves. Chi-square and t-test were used for analysis. Results. From July 2020-Mar 2022, 2900 COVID-19 patients were admitted. The total number of patients in each wave (from second to fifth waves) were 1217,783,590 and 310 respectively. The mean age in years was 60 in 2nd and 3rdwaves while it was 58 in 4th and 66 in 5th wave with 64.5%, 62.5%, 51.8%and 54% of males in each wave. On comparison majority 42% had moderate COVID in 2nd wave, 37% and 48.5% had severe COVID in 3rd and 4th waves respectively while the disease was mild (31%) in 5th wave (p-value of < 0.001).Cytokine releasing syndrome (CRS) was common in 3rd wave (33%). Most intubations were done in 4thwave (21%). Compared to other waves mortality rate was also highest in 4thwave (35%, with p-valve of 0.03). Conclusion. Based on our data fourth wave which was mostly driven by delta variant was the deadliest in terms of disease category and outcome.

Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic

Gait recognition is the behavioral biometric trait that tracks humans based on their walking motion. It has gained attention because of its non-invasive and unobtrusive behaviors and applicable to the different application area. In this paper, we target model-free gait recognition with the deep learning approach for the Muslim community in the COVID-19 pandemic. The different convolutional neural network architectures (CNN) are examined by using the spatio-temporal gait representation called Gait Energy Images (GEI). We explored both the identification and verification problems to determine the suitability of the proposed CNN frameworks. In gait recognition, the intraclass variation is larger than the inter-class variation because of the shooting view, the walking speed, the wearing condition, and so on. To tackle this challenge, the verification framework is more suitable for the 1:1 association of gait recognition. As for the verification problem, we implemented the Siamese network with the parallel CNN architecture. All the proposed methods are tested against the public gait datasets called OUISIR-LP and OUISIR-MVLP to determine the identification and verification performance in terms of recognition accuracy and error rate. © 2022 by the authors. Licensee ESJ, Italy.

Gastrointestinal and Hepatobiliary Manifestations of COVID-19 in Children: A Review

Background: Severe acute respiratory syndrome-coronavirus-2 (SARS-COV-2) is shaking the world heavily. SARS-COV-2 (COVID-19) infection has a wide variety of presentations as it affects almost every system of body. Gastrointestinal and hepatobiliary symptoms are frequently overlooked especially in children. Objectives: The purpose of this review was to discuss the gastrointestinal and hepatobiliary presentations of COVID-19 in children and compare with non-gastrointestinal presentations. Methods: This study was a narrative review. Recent available literature was searched by keywords. The most recent information from relevant articles were collected and reviewed. This write up was compiled after the review of articles from the last one and half year. Results: About 50.0% symptomatic children with COVID-19 had gastrointestinal manifestations. COVID-19 with gastrointestinal symptoms had delayed diagnosis, delayed hospitalization and worse outcome in compare with Covid-19 with non-gastrointestinal symptoms. Conclusion: Vomiting, diarrhoea, abdominal pain, anorexia, nausea are common gastrointestinal manifestations in children with COVID-19. Elevated transaminasemia is not uncommon.

A comparative study on the knowledge and attitude of COVID-19 among urban and rural populations of Bangladesh

Aim: This study is aimed to identify the awareness and behavioral perspective on COVID-19 between urban and rural people of Bangladesh during the period of outbreak.

Accelerated diagnosis and reporting of patients using analysis of bulk chest X-ray images to aid impacted healthcare system during COVID19

This paper proposes Machine Learning based Methodology to assist Health staff to perform bulk reporting of patients on Chest X-ray images into Normal or Pneumonia diseased clusters which will be of assistance to currently overburdened health workers and possibly detect potential covid19 infected patients as pneumonia is known symptom of covid19. Also this paper demonstrates creating high accuracy models trained on existing clustered data capable of accurately predicting pneumonia in patients.