Local governments and community-based rehabilitation for developmental disabilities: leaving no one behind.

SETTING: The BUDS (not an acronym) institutions comprise a community-based rehabilitation initiative for children and families affected by developmental disabilities in Kerala, India. OBJECTIVE: To explore the role of local governments in the establishment and functioning of BUDS institutions. DESIGN: We used qualitative approaches comprising document review and in-depth interviews with trainers, parents of children with developmental disabilities and elected representatives. RESULTS: BUDS was created by Kudumbasree, a decentralised women empowerment and poverty alleviation initiative. Our findings illustrate the role of local governments in facilitating expansion through the establishment of infrastructure, therapy equipment, transportation and financial allocation for these, as well as through the development of human resources, assistance with enrolment for financial assistance and insurance programmes, and coordination with education and health sectors. Programme implementation varied considerably regarding available infrastructure, staffing and services among the institutions studied. The institutions were physically closed during the COVID-19 pandemic but continued to function in alternative ways. CONCLUSION: Despite variable implementation, local governments have supported the expansion of BUDS institutions, thereby creating more spaces for inclusive and integrated education and rehabilitation of persons with disabilities in Kerala. The expansion over the past two decades and measures during the COVID-19 pandemic suggest resilience and sustainability of the model.

CONTEXTE: Les institutions BUDS (ceci n'est pas un acronyme) ont mis en place une initiative communautaire pour la réhabilitation des enfants et familles touchés par des troubles du développement au Kérala, Inde. OBJECTIF: Analyser le rôle des gouvernements locaux dans la fondation et le fonctionnement des institutions BUDS. MÉTHODES: Nous avons utilisé des approches qualitatives fondées sur une analyse documentaire et sur des entretiens approfondis avec des formateurs, des parents d'enfants atteints de troubles du développement et des représentants élus. RÉSULTATS: BUDS a été créé dans le cadre d'une initiative décentralisée de réduction de la pauvreté et d'autonomisation des femmes, dénommée Kudumbasree. Nos résultats illustrent le rôle des gouvernements locaux dans la facilitation de l'expansion par la mise à disposition d'infrastructures, d'équipements thérapeutiques, de transports et l'allocation de fonds pour ceux-ci, ainsi que par le développement des ressources humaines, l'inclusion dans des programmes d'assistance financière et d'assurances, et la coordination avec les secteurs de l'éducation et de la santé. De grandes différences de mise en œuvre du programme ont été observées entre les institutions à l'étude, en matière d'infrastructures disponibles, de personnel et de services. Les institutions ont fermé leurs portes pendant la pandémie de COVID-19, mais elles continuaient de fonctionner de manière alternative. CONCLUSION: En dépit d'une mise en œuvre variable, les gouvernements locaux ont soutenu le développement des institutions BUDS et ainsi élargi l'espace pour une éducation et une réhabilitation inclusives et intégrées des personnes porteuses de handicaps au Kérala. Le développement de ces institutions au cours des 20 dernières années et les mesures instaurées pendant la pandémie de COVID-19 laissent transparaître la résilience et le caractère durable du modèle.

Diagnosis of Medical Conditions in Patients Using Machine Learning Models

A variety of patient care and intelligent health systems can benefit from the implementation of artificial intelligence as a tool to aid caregivers. Machine learning and deep learning are two types of AI that are increasingly being used in the medical industry. Artificial intelligence methods require a large amount of clinical data from a range of imaging modalities for correct disease diagnosis. In addition, AI has greatly enhanced the quality of hospital stays, allowing patients to be released sooner and complete their recoveries at home. This article aims to provide the information on the field of AI subset i.e., machine learning-based disease detection with information that will aid them in making better decision making. This helps the researchers to classify the medical conditions in patients with a prominent dataset. Copyright © 2022, Anka Publishers. All rights reserved.

Deep Learning Based NLP Embedding Approach for Biosequence Classification

Biological sequence analysis involves the study of structural characteristics and chemical composition of a sequence. From a computational perspective, the goal is to represent sequences using vectors which bring out the essential features of the virus and enable efficient classification. Methods such as one-hot encoding, Word2Vec models, etc. have been explored for embedding sequences into the Euclidean plane. But these methods either fail to capture similarity information between k-mers or face the challenge of handling Out-of-Vocabulary (OOV) k-mers. In order to overcome these challenges, in this paper we aim explore the possibility of embedding Biosequences of MERS, SARS and SARS-CoV-2 using Global Vectors (GloVe) model and FastText n-gram representation. We conduct an extensive study to evaluate their performance using classical Machine Learning algorithms and Deep Learning methods. We compare our results with dna2vec, which is an existing Word2Vec approach. Experimental results show that FastText n-gram based sequence embeddings enable deeper insights into understanding the composition of each virus and thus give a classification accuracy close to 1. We also provide a study regarding the patterns in the viruses and support our results using various visualization techniques. © 2022, Springer Nature Switzerland AG.

Biological Sequence Classification Using Deep Learning Architectures

Finding similar biological sequences to categorize into respective families is an important task. The present works attempt to use machine learning-based approaches to find the family of a given sequence. The first task in this direction is to convert the sequences to vector representations and then train a model using a suitable machine learning architecture. The second task is to find which family the sequence belongs to. In this work, deep learning-based architectures are proposed to do the task. A comparative study on how effective various deep learning architectures for this problem is also discussed in this work.

Material Design for Next-Generation mRNA Vaccines Using Lipid Nanoparticles

Vaccine development is among the critical issues for ceasing the COVID-19 pandemic. This review discusses the current usage of biomaterials in vaccine development and provides brief descriptions of the vaccine types and their working mechanisms. New types of vaccine platforms (next-generation vaccines and DNA- or mRNA-based vaccines) are discussed in detail. The mRNA vaccine encoding the spike protein viral antigen can be produced in a cell-free system, suggesting that mRNA vaccines are safer than “classic vaccines” using live or inactivated virus. The mRNA vaccine efficacy is typically high at approximately 95%. However, most mRNA vaccines need to be maintained at −20 or −70 degrees for storage for long periods (half a year) and their transportation because of mRNA vaccine instability in general, although mRNA vaccines with unmodified and self-amplifying RNA (ARCT-154, Arcturus), which have a lyophilized form, have recently been reported to be kept at room temperature. mRNA vaccines are typically entrapped in lipid nanoparticles composed of ionizable lipids, polyethylene glycol (PEG)-lipids, phospholipids, and cholesterol. These components and their composition affect mRNA vaccine stability and efficacy and the size of the mRNA vaccine. The development of an improved mRNA vaccine entrapped in sophisticated biomaterials, such as novel lipid nanoparticles, using new types of biopolymers or lipids is necessary for high efficacy, safe transportation and long-term storage of the next generation of mRNA vaccines under mild conditions. © 2022 Taylor & Francis Group, LLC.

Identification of Non-Vaccinated People Using Face Recognition Based on CNN

Preventive medical care relies on vaccinations to provide significant health benefits. Vaccination is an important and effective preventive health measure. There is no better way to reduce the risk of pandemic spread of SARS-CoV-2/COVID-19 than vaccination. As a preventive measure, the government has begun vaccinating Indians against Corona infection. It is therefore important, in addition to developing and supplying vaccines, that enough people are willing to obtain vaccines. However, of the populations worldwide, there are concerning proportions that are reluctant to get vaccinated. In order to end the pandemic, it is highly essential to deal with another omnipresent issue: outright rejection of vaccinations. To achieve population immunity first we have to find the non-vaccinated population should be detected and to this end, this project proposed an Aadhaar-based facial recognition system is used to find non-vaccinated citizen and alert them using Artificial Intelligence. Deep learning which is in the form of Convolutional Neural Networks (CNNs) are used to carry out the face recognition process and it is also proven to be an efficient method to carry out face recognition due to its high fidelity. A CNN is a Deep Neural Network (DNN), which is designed to perform challenging tasks like image processing, which is crucial for facial recognition. The CNN structure is composed of numerous layers of neurons that connect the neurons: an input layer, an output layer, and layers between these two layers. In the midst of the epidemic coronavirus outbreak (COVID-19), a person's current inoculation status will be updated based on face recognition to safeguard him/her from COVID-19 and it may also serve as proof of vaccination for other purposes. Facial recognition technology (FRT) along with the Aadhaar helps to authenticate people before entering into any types of service. This project provides COVID-19 immunization status, which is determined by observing at their face, and certify that they have been vaccinated. © 2022 IEEE.

Malaysia's Experience Navigating Colorectal Cancer Screening in the COVID-19 Pandemic and Opportunities to Build Back Better

The downstream effect of the pandemic on global cancer prevention and control efforts is wide-ranging, especially for lower and middle-income countries (LMICs), including Malaysia. This paper explores the performance of the colorectal cancer screening programme in Malaysia for the years 2019 and 2020, This is followed by evidence-based recommendations for building back a better cancer control programme in Malaysia. Malaysia screened a total of 31,529 eligible candidates in 2019 and 42,554 in 2020. A total of 2,668 (8.46%) and 2767 (6.50%) individuals tested positive for the immunochemical faecal occult blood test (iFOBT) in 2019 and 2020 respectively. Of these numbers, only 1454 (54.49%) of those who tested positive underwent colonoscopy in 2019 and this proportion reduced to 1148 (41.48%) in 2020. This analysis also shows a drop in the number of screenings in the second quarter of 2020. This drop coincides with the announcement of Malaysia's first Movement Control Order. Existing challenges exacerbated by pandemic restrictions have possibly led to a decreased colonoscopy attendance rate in 2020. To build back a better cancer control programme, better governance, and political will, coupled with improved financing, sustainable partnerships, improved service delivery, and a robust monitoring and evaluation mechanism is vital. © 2022 UPM Press. All rights reserved.

Small fiber neuropathy in a patient with coronavirus disease 2019 (COVID-19)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel beta-coronavirus that causes a variety of symptoms in patients known as coronavirus disease (COVID-19). COVID-19 infection may cause complications involving the central nervous system (CNS), peripheral nervous system (PNS), muscles, or autonomic system (ANS). Compared with central and peripheral nervous system involvement in COVID-19 infection reports, the bibliography describing ANS manifestations is more limited. We report a patient with confirmed SARS-CoV-2, admitted to one of the tertiary hospitals in Singapore with small fiber neuropathy. Small fiber neuropathy as a neurological manifestation in COVID-19 infection is rare. Our case report adds to and supports this observation and also highlights how SFN in COVID-19 infection can be self-limited without requiring immunosuppressive treatment.

The Challenges in Implementing Wearable Antennas for Large-Scale Health Monitoring

The rise of antenna technology, smartphones, and the Internet-of-things (IoT) has enabled wearable antennas for wireless communication between implantable devices such as pacemakers, infusion pumps, etc., and external devices for health monitoring. This work describes the key challenges that need to be addressed for such wireless body area network (WBAN) technologies to be integrated into large-scale health monitoring programs. These include the miniaturization of antennas, fabrication techniques to enable mass production, and methods to protect patients from data infringement and hackers. Furthermore, the role of wearable and implantable antennas is pivotal to realize devices for continuous healthcare monitoring especially during Pandemic situations such as Coronavirus Disease-2019 (COVID-19). © 2021 IEEE.

Face Mask Detection and Temperature Scanning for the Covid-19 Surveillance System

Face mask recognition has grown significantly in recent years as a result of Corona's insistence on its numerous applications in law enforcement, security, and other commercial applications. A unique technique for performing face new line detection and face mask identification is presented. The proposed method employs a YoLo technique to recognise the objects like face masks in pictures and videos as a measure for COVID-19 precaution. Extensive testing on datasets and performance assessment of the suggested approaches are demonstrated. Furthermore, we used a symbolic method to successfully maintain inter and intra class differences in face mask detection. The proposed work is being created as a prototype to monitor temperature and identify masks for individuals. The first technique employs a temperature sensor to detect the body's current temperature. In the second way, the work is aimed at offering a safety mechanism for individuals in order to avoid COVID-19. Extensive experimentation on 50 different image datasets was carried out to assess the performance of the suggested technique. For ten random trials, we experimented with different training and testing percentages. Based on the data, we conclude that the symbolic method produces better outcomes than the conventional one. © 2021 IEEE.

A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Interactive Workbook for Effective Virtual Laboratories

The COVID-19 pandemic has forced academic institutions to switch from traditional teaching-learning to fully digital mode. The traditional programming lab sessions are replaced by video lectures, notes, and assignment submission through LMS. Manual grading and debugging of the program results in delayed feedback. The existing auto-graders are designed to check the programs’ correctness, and they cannot enhance learning. The interactive workbooks we propose are similar to the popular Jupyter notebooks but oriented more towards enhancing the teaching-learning process and providing immediate feedback. The survey results showed that 70% of the students believed that interactive workbooks enabled them to understand the problem and made them capable of solving it in incremental steps. 65% of the instructors added that interactive workbooks could supplement the physical lab sessions’ teaching-learning process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Clinical Significance of Total Radiation Dose for Patients With Treatment Delays in Stage III Non-Small Cell Lung Cancer

Delays during definitive radiation treatment are not uncommon, as highlighted by the ongoing COVID-19 pandemic, and are associated with worse survival. We aim to utilize the National Cancer Database (NCDB) to determine whether prolonged chemoradiation (CRT) treatment time in stage III non-small cell lung cancer (NSCLC) can be compensated for by an increase in total radiation dose. We identified 26,101 patients who were treated curatively with CRT using standard doses (59.4-66.6 Gy) and fractionation for stage III NSCLC between 2004-2017. Treatments were classified as non-prolonged or prolonged. The total number of days allowed for non-prolonged treatment for each dose/fractionation was calculated by adding number of fractions, weekend days (to accommodate any weekday start), and 2 additional days. Any treatment exceeding this number of days was prolonged. Multivariable Cox proportional regression was used to assess the association between specific doses and treatment durations and OS while adjusting for age, gender, race, comorbidity score, insurance status, facility type, urban/rural location, education, clinical T and N category. Of 26,101 patients, 57% were male and the median age was 67. The most common T and N stage were T2 (31%) and N2 (64%), respectively. The majority of patients (62%) did not have prolonged treatment. For those who had prolonged treatment, the median prolongation was 4 days and survival was worse (HR 1.256, P = < 0.0001). Comparison of non-prolonged and prolonged treatment was then limited to two dose levels: 60 Gy and 66 Gy, both delivered in 2 Gy fractions (13,189 patients). Both doses resulted in similar survival for non-prolonged treatment (HR for 60 Gy non-prolonged: reference;HR for 66 Gy non-prolonged: 1.01, P = 0.633) and worse survival if treatment was prolonged (HR for 60 Gy prolonged: 1.294, P = < 0.0001;HR for 66 Gy prolonged: 1.216, P = < 0.0001). Direct comparison of the prolonged groups for 60 Gy and 66 Gy is tabulated below. For prolongation of 1-3 days, 66 Gy and 60 Gy were equivalent. If treatment was prolonged for 4 days or longer, survival was improved for higher total radiation dose. Prolonged CRT in stage III NSCLC is associated with worse survival. Alteration of total dose should be considered for prolonged treatment time, especially as the number of missed days increases. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Radiation Oncology, Biology, Physics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep optimal VGG16 based COVID-19 diagnosis model

Coronavirus (COVID-19) outbreak was first identified in Wuhan, China in December 2019. It was tagged as a pandemic soon by the WHO being a serious public medical condition worldwide. In spite of the fact that the virus can be diagnosed by qRT-PCR, COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray (CXR) and Computed Tomography (CT) images. In this paper, the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features. Impressive features like Speeded-Up Robust Features (SURF), Features from Accelerated Segment Test (FAST) and Scale-Invariant Feature Transform (SIFT) are used in the test images to detect the presence of virus. The optimal features are extracted from the images utilizing DeVGGCovNet (Deep optimal VGG16) model through optimal learning rate. This task is accomplished by exceptional mating conduct of Black Widow spiders. In this strategy, cannibalism is incorporated. During this phase, fitness outcomes are rejected and are not satisfied by the proposed model. The results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing spaces. VGG 16 model identifies the image which has a place with which it is dependent on the distinctions in images. The impact of the distinctions on labels during training stage is studied and predicted for test images. The proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen, Spec, Accuracy and F1 score were promising. © 2021 Tech Science Press. All rights reserved.

A study on the issues and challenges of food sector with special reference to restaurant industry during the COVID -19 pandemic periods in Trivandrum Dist-Kerala

A Pandemic is not a new event that encountered in the history of humanity, because mankind has failed various pandemic in history, The Covid -19 has impacts negative effects on the global economy .Considering food chain supply especially Restaurant Industry on of the most important sectors of the economy, Covid -19 has an impact on the whole process in the field of restaurant industry, it concerns about food productions, processing and distribution as demands. Therefore the central government issued order for Trans movement of Agri-food products in India under covid-19 pandemic situation,. Government announces the lock down is slightly impact the movements of food products in India like transportation facilities, people lock down in home etc. But government allowed to maintain the safety precautions and prevent an increase in food safety is a primary important criterion for fighting against this type of pandemic spreads across the world. COVID-19 outbreak has presented unprecedented circumstances before the fragile tourism and Restaurant industry. The highly infectious novel coronavirus continues to thwart the sector and raises serious questions about the present and future survival of the food sector. This pandemic situation created the entire industry has been down economically, socially, and mentally, The research addresses two important concerns, first the Challenges and Issues faced by the restaurant industry and the suggestions and recommendations for survival of business.

Emerging Technologies and Sensors That Can Be Used during the COVID-19 Pandemic

The spread of COVID-19, which has infected over 10 million people worldwide, entails the need for fast and aggressive testing never like before. As countries look to expanding testing, such test solutions must not only be technically sound, but should also be feasible and convenient for the user. The aim of this paper is to review the emerging tests and technology which can be potentially used to detect and assess the condition of those infected with the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the challenges in their development and use. The paper deals with 1) In vitro diagnostic tests(IVDs),i.e tests that use biological samples like blood and consist of 2 types: nucleic acid tests, which detect the RNA of the virus, and antibody tests which antibodies created by the body in response to the virus. 2) Chest X-Ray and CT scan devices, associated Deep Learning based detection methods and portable devices. 3) Wearable sensors, IoT and telemedicine for remote monitoring of COVID-19 patients to assess their condition, and also of Non-COVID-19 ones to reduce risks of cross-infection. © 2020 IEEE.