Abundance, spatial distribution, and chemical characterization of face masks on the beaches of SE Kanyakumari, India.

Personal Protective Equipment (PPE) is a new world of waste during the COVID-19 pandemic. In this baseline study, the occurrence of PPE faces masks were assessed on the eleven beaches of Kanyakumari, India concerning the abundance, spatial distribution, and chemical characterization (ATR-FTIR spectroscopy). A total of 1593 items/m2 of PPE face masks and their mean density of 0.16 PPE/m2, ranging from 0.02 to 0.54 PPE/m2 were determined in the study area. Kanyakumari beach (n = 430 items/m2) has the highest concentration of masks (26.99 %), with a mean density of 0.54 m2 due to recreational, sewage disposal, and tourism activities. This is perhaps the most important study describing the scientific data that focuses on the significant effects of communal activities and accessibility on COVID-19 PPE face mask pollution. It also highlights the need for sufficient management facilities to optimize PPE disposal.

COVID -19 Predictions using Transfer Learning based Deep Learning Model with Medical Internet of Things

Early detection of COVID-19 may help medical expert for proper treatment plan and infection control. Internet of Things (IoT) has vital improvement in many areas including medical field. Deep learning also provide tremendous improvement in the field of medical. We have proposed a Transfer learning based deep learning model with medical Internet of Things for predicting COVID-19 from X-ray images. In the proposed method, the X ray images of patient are stored in cloud storage using internet for wide access. Then, the images are retrieved from cloud and Transfer learning based deep learning models namely VGG-16, Inception, Alexnet, Googlenet and Convolution neural Network models are applied on the X-rays images for predicting COVID 19, Normal and pneumonia classes. The pre-trained models helps to the effectiveness of deep learning accuracy and reduced the training time. The experimental analysis show that VGG -16 model gives accuracy of 99% for detecting COVID19 than other models. © 2023 Sunarno Basuki and Perdinanto.

Efficient COVID-19 Identification Using Deep Learning for IoT

Internet of Things (IoT) has made it possible to diagnose and treat patients remotely, as well as to expedite the transportation of essential drugs and medical equipment to locations that are geographically separated. This has occurred at a time when society has become more socially distant. During the Ebola and COVID-19 outbreaks, the Internet of Things (IoT) technology was put to use in remote patient monitoring and the management of the vaccine cold chain. Concurrently, this study reflects on the variables that are required for IoT to scale. Since December 2019, the COVID-19 outbreak on a worldwide scale has developed into a significant problem. In order for medical treatment to be successful, it is essential to make a prompt and accurate diagnosis of persons who may be infected with the COVID-19 virus. In order to put a halt to the spread of COVID-19, it is important to construct an automated system that is based on deep transfer learning and is capable of detecting the virus based on chest X-rays. The authors of this study present an internet-of-things (IoT) system that makes use of ensemble deep transfer learning to diagnose COVID-19 patients at an earlier stage. It is feasible to keep an eye on potentially hazardous COVID-19 incidents as they occur so long as suitable procedures are adhered to. Inceptions A variety of different deep learning models are included into the framework that has been proposed for the Internet of Things. According to the findings of the study, the method that was suggested assisted radiologists in accurately and quickly identifying patients who could have COVID-19. The proposed effort focuses on developing an effective identification system based on the COVID-19 standard for use in an IoT setting. © 2022 IEEE.

Measuring Emotional Intelligence Competency of Undergraduate Students during Online Class learning

Emotional quotient is highly important in the current education system. The COVID-19 pandemic period has mandated the online classes among the students. The aim of this work is to analyze the characteristics of Emotional Intelligence (EI) of students during the online class learning. Totally 130 students have participated in this empirical study with interest. There are 50 questions in a self-report questionnaire with a 5-point likert scale that covers the different aspects of self-awareness, self-management, motivation, empathy and social skills. The students' responses are collected through Google form. The scores of each participant with respect to self-awareness, self-management, motivation, empathy and social skills are calculated. The descriptive statistics are applied and then Confirmatory Factor Analysis (CFA) is performed to find the important factors. Further Kaiser-Meyer-Olkin (KMO) and Bartlette' test are also made to measure the sample adequacy. From the results of the CFA, it is concluded that social skill component of EI played a major role among the students. © 2022, Rajarambapu Institute Of Technology. All rights reserved.

Treating Children with Hcv Close to Home through a Virtual National Multidisciplinary Network

Background and Aims Hepatitis C virus (HCV) infection is a major global health problem in adults & children. The recent efficacy of Direct Acting Anti-viral therapy (DAA) has cure rates of 99% in adults and adolescents. These drugs were licensed for children 3-12 yrs during the recent coronavirus pandemic. To ensure equitable access, safe & convenient supply during lockdown, we established a virtual national treatment pathway for children with HCV in England & evaluated its feasibility, efficacy & treatment outcomes. Method A paediatric Multidisciplinary Team Operational Delivery Network (pMDT ODN), supported by NHS England (NHSE), was established with relevant paediatric specialists to provide a single point of contact for referrals & information. Referral & treatment protocols were agreed for HCV therapy approved by MHRA & EMA. On referral the pMDT ODN agreed the most appropriate DAA therapy based on clinical presentation & patient preferences, including ability to swallow tablets. Treatment was prescribed in association with the local paediatrician & pharmacist, without the need for children & families to travel to national centres. All children were eligible for NHS funded therapy;referral centres were approved by the pMDT ODN to dispense medication;funding was reimbursed via a national NHSE agreement. Demographic & clinical data, treatment outcomes & SVR 12 were collected. Feedback on feasibility & satisfaction on the pathway was sought from referrers. Results In the first 6 months, 34 children were referred;30- England;4 - Wales;median (range) age 10 (3.9 - 14.5) yrs;15M;19F: Most were genotype type 1 (17) & 3 (12);2 (1);4(4). Co-morbidities included: obesity (2);cardiac anomaly (1);Cystic Fibrosis (1);Juvenile Arthritis (1). No child had cirrhosis. DAA therapy prescribed: Harvoni (21);Epclusa (11);Maviret (2) .27/34 could swallow tablets;3/7 received training to swallow tablets;4/7 are awaiting release of granules.11/27 have completed treatment and cleared virus;of these 7/11 to date achieved SVR 12. 30 children requiring DAA granule formulation are awaiting referral and treatment. Referrers found the virtual process easy to access, valuing opportunity to discuss their patient's therapy with the MDT & many found it educational. There were difficulties in providing the medication through the local pharmacy. However there are manufacturing delays in providing granule formulations because suppliers focused on treatments for COVID, leading to delays in referring and treating children unable to swallow tablets. Conclusion The National HCV pMDT ODN delivers high quality treatment & equity of access for children & young people, 3- 18 yrs with HCV in England, ensuring they receive care close to home with 100% cure rates.

Evaluating Textural Changes of Lung in CT Images using GLCM in Comparison with GLRLM

The aim of this analysis is to identify the textural alterations due to incidence of COVID-19 in lung CT scan images using GLCM matrix in comparison with GLRLM. Materials and Methods: Sample size is calculated using G power analysis and a total of 176 sample sizes are acquired for this novel texture analysis using parameters like effect size (0.3), standard error rate (0.05), maximum rate (0.8) and allocation rate (N2/N1=1). For this analysis the required CT images are collected from Github. For group 1 a total of 94 sample images are taken and for group 2 a total of 82 sample images are taken. For analyzing the textural alterations of CT scan lung images, comparison between Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) is carried out for this analysis. In the process of evaluation of classifiers 10-fold cross validation is performed. Normal and COVID subjects are classified using Random forest, K-NN, Logistic regression classifiers for better classification. Results and Discussion: Due to incidence of COVID in lunge tissues it is observed that textural alterations are formed in lung CT scan images. From the acquired features values of GLCM and GLRLM it is observed that GLCM is statistically significant than the GLRLM. Contrast, homogeneity and sum of average features are statistically significant (0.0001) in identifying normal and COVID subjects. The mean value of homogeneity for healthy controls is (0.215) and for COVID subjects it is (0.327) such that normal subjects have a gentle surface of the lung and COVID subjects have rough surface and significance value is (p<0.05). GLCM has acquired precision (0.931), F1-score (0.928), Recall (0.929), AUC (0.981), Classification Accuracy (0.929) are obtained using random forest classifiers. From the above values it is observed that COVID subjects have textural variations than the normal subjects. Conclusion: From this analysis it is observed that GLCM provides significantly better classification in differentiating the COVID and normal subjects than GLRLM. © 2022 IEEE.

Evaluating the Performance of Hough based Moments to Classify Healthy and COVID Subjects in Comparison with Traditional Shape Measures

The aim of this analysis is to measure and analyse the shape changes in Lung CT scans using orthogonal Zernike moments in comparison with traditional shape measures. Materials and Methods: A total sample size of 176 scans are acquired for this analysis, by assigning parameters such as the effect size = 0.3, standard error rate = 0.05 and algorithm power = 0.80 are predefined in Gpower software. In this analysis, the comparison between traditional shape measures and Hough Transform algorithms in classifying normal and COVID-19 is performed. Results: It is observed that there is no shape change in the lungs of the normal subjects and in COVID subjects the shape of the lungs reduces due to tissue loss. The feature values obtained from Hough Transform are found to be statistically important (p<0.05). The statistical values (Mean ± standard deviation) of normal and COVID subjects are 0.18 ± 0.13 and 0.10 ± 0.13. The significant features for the Zernike moment were M13,9, M10,8. The extracted values from the Computed Tomography images are consistent in displaying a considerable difference between healthy subject and COVID CT- scan images. The proposed Hough Transform based Zernike Moments algorithm has significantly better accuracy (97%) than the Traditional shape measures with accuracy (78%). Conclusion: The Hough transform based Zernike moments algorithm gives a significantly better result oriented to extraction of shape changes and manifestation of a significant difference in the healthy subject and COVID subject CT scan images than Traditional shape measures algorithm. © 2022 IEEE.

Treating children with HCV close to home through a virtual national multidisciplinary network

Background and aims: Hepatitis C virus (HCV) infection is a major global health problem in adults & children. The recent efficacy of Direct Acting Anti-viral therapy (DAA) has cure rates of 99% in adults and adolescents. These drugs were licensed for children 3–12 yrs during the recent coronavirus pandemic. To ensure equitable access, safe & convenient supply during lockdown, we established a virtual national treatment pathway for children with HCV in England & evaluated its feasibility, efficacy & treatment outcomes. Method: A paediatric Multidisciplinary Team Operational Delivery Network (pMDT ODN), supported by NHS England (NHSE), was established with relevant paediatric specialists to provide a single point of contact for referrals & information. Referral & treatment protocolswere agreed for HCV therapy approved byMHRA& EMA. On referral the pMDT ODN agreed the most appropriate DAA therapy based on clinical presentation & patient preferences, including ability to swallow tablets. Treatment was prescribed in association with the local paediatrician & pharmacist, without the need for children & families to travel to national centres. All children were eligible for NHS funded therapy;referral centres were approved by the pMDT ODN to dispense medication;funding was reimbursed via a national NHSE agreement. Demographic & clinical data, treatment outcomes & SVR 12 were collected. Feedback on feasibility & satisfaction on the pathway was sought from referrers. Results: In the first 6 months, 34 childrenwere referred;30- England;4-Wales;median (range) age 10 (3.9–14.5) yrs;15M;19F: Most were genotype type 1 (17) & 3 (12);2 (1);4 (4). Co-morbidities included: obesity (2);cardiac anomaly (1);Cystic Fibrosis (1);Juvenile Arthritis (1). No child had cirrhosis. DAA therapy prescribed: Harvoni (21);Epclusa (11);Maviret (2). 27/34 could swallow tablets;3/7 received training to swallowtablets;4/7 are awaiting release of granules.11/27 have completed treatment and cleared virus;of these 7/11 to date achieved SVR 12. 30 children requiring DAA granule formulation are awaiting referral and treatment. Referrers found the virtual process easy to access, valuing opportunity to discuss their patient’s therapy with the MDT & many found it educational. There were difficulties in providing the medication through the local pharmacy. However there are manufacturing delays in providing granule formulations because suppliers focused on treatments for COVID, leading to delays in referring and treating children unable to swallow tablets. Conclusion: The National HCV pMDT ODN delivers high quality treatment & equity of access for children & young people, 3–18 yrs with HCV in England, ensuring they receive care close to home with 100% cure rates.

Cranial neuropathy in COVID-19: a case series and review of literature.

Neurological presentation of COVID-19 is increasingly being recognised. Cranial neuropathy in COVID-19 is an uncommon and under-diagnosed entity. We report a case series of 4 patients who presented with trigeminal neuropathy (two cases) and facial nerve palsy (two cases) which recovered with conservative treatment along with the review of the literature.

Virtual risk assessment pathway for deep venous thrombosis: a preliminary model.

BACKGROUND: COVID-19 has necessitated the reduction in conventional face-to-face patient consultation to reduce the risk of novel coronavirus SARS-CoV-2 transmission. Traditional pathways to risk assess for deep venous thrombosis (DVT) would involve face-to-face assessment to formulate an appropriate management plan following an initial presentation usually in secondary care or in-hospital settings. Appropriate antithrombotic measures can prevent complication of DVT such as pulmonary embolism with prompt early diagnosis and treatment. METHODS: This observational, pilot study evaluates the possibility of combining telemedicine technology and a virtual examination pathway for remote triage and assessment of patients with suspected DVT. RESULTS: Piloting and development of a virtual risk assessment pathway for DVT involves various challenges and multidisciplinary co-ordination. CONCLUSION: Advances in telecommunication technology can enable clinicians, specialist nurses and hospital departments to develop a virtual examination pathway for remote triage and assessment of patients with suspected DVT. This pathway is not a replacement for conventional 'face-to-face' evaluation, but we believe the template can be explored and refined to act as a blueprint for future applications even when the pandemic has stabilised.

Bibliometric analysis of COVID-19 related publications in Indian orthopaedic journals.

BACKGROUND: The COVID-19 pandemic has resulted in an infodemic about the novel coronavirus SARS-CoV-2 outbreak to build knowledge and develop mitigation strategies. In addition, scientific journals across the world have studied the impact of COVID-19 on trauma and orthopaedics. METHODS: A cross-sectional, bibliometric analysis of the literature was undertaken on COVID-19 related articles from three Pubmed and Scopus indexed orthopaedic journals from India, namely, Indian Journal of Orthopaedics(IJO),Journal of Clinical Orthopaedics and Trauma(JCOT), and Journal of Orthopaedics (JOO), in May 2021. All the article types and study designs were included for this review. The authors, institutions, countries, keywords, and co-authorship mapping were studied. RESULTS: A total of 112 COVID-19 related documents were retrieved. Period of these publications was from 2nd April 2020 to 31st May 2021. Vaishya R. (n = 16) was the most cited author, and Indraprastha Apollo Hospitals (n = 16) was the most cited research Institution. India led the list of countries in academic publication output. On keyword mapping, telemedicine was the most prominent Medical Subject Headings (MeSH) search word. CONCLUSION: The Indian orthopedic journals have addressed the impact of COVID-19 on orthopaedic practice in India and aborad whilst continuing to publish knowledge about basic science and clinical orthopaedic research studies. The JCOT has outperformed and become the most leading orthopaedic journal from India during the pandemic. COVID -19 articles have been fast tracked, open accessed and attracted more citations in reduced duration of time compared to non-COVID-19 papers.

Characteristics and clinical outcomes of hip fracture patients during the first lockdown of COVID-19, lessons learnt: A retrospective cohort analysis.

BACKGROUND: The management of hip fracture patients has been challenging across the UK in the wake of emergency coronavirus disease 2019 guidelines. AIMS: This retrospective, observational cohort study analyses the impact of the first lockdown during the early part of the coronavirus disease 2019 pandemic on the management of hip fracture patients at a district general hospital in the UK. METHODS: Comparative analysis to assess hip fracture patients treated at this Trust between 1 April to 31 May 2019 and 1 April to 31 May 2020 was undertaken. The primary outcome measures appraised were 30 and 60-day mortality and the secondary outcome measure included time to surgery. RESULTS: There was a higher 30 and 60-day mortality rate in the first lockdown period at 8.1% and 13.5%, respectively, compared to 1.96% and 5.88% in 2019. A significantly lower proportion of hip fracture patients at 59.46% were operated within the 36h target time frame during the first lockdown. CONCLUSION: In our Trust, hip fractures were treated as obligatory injuries. However, the mortality was higher in the 2020 cohort with a significant reduction in patients achieving the recommended '36 hours' time to surgery target and accruement of Best Practice Tariff. Enhanced infection control strategies have prepared us for the future.