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1.
Emerg Infect Dis ; 28(13):26-33, 2022.
Article in English | PubMed | ID: covidwho-2162885

ABSTRACT

A network of global respiratory disease surveillance systems and partnerships has been built over decades as a direct response to the persistent threat of seasonal, zoonotic, and pandemic influenza. These efforts have been spearheaded by the World Health Organization, country ministries of health, the US Centers for Disease Control and Prevention, nongovernmental organizations, academic groups, and others. During the COVID-19 pandemic, the US Centers for Disease Control and Prevention worked closely with ministries of health in partner countries and the World Health Organization to leverage influenza surveillance systems and programs to respond to SARS-CoV-2 transmission. Countries used existing surveillance systems for severe acute respiratory infection and influenza-like illness, respiratory virus laboratory resources, pandemic influenza preparedness plans, and ongoing population-based influenza studies to track, study, and respond to SARS-CoV-2 infections. The incorporation of COVID-19 surveillance into existing influenza sentinel surveillance systems can support continued global surveillance for respiratory viruses with pandemic potential.

2.
2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2161373

ABSTRACT

The fast proliferation of the coronavirus around the globe has put several countries' healthcare systems in danger of collapsing. As a result, locating and separating COVID-19-positive patients is a critical task. Deep Learning approaches were used in several computer-aided automated systems that utilized chest computed tomography (CT-scan) or X-ray images to create diagnostic tools. However, current Convolutional Neural Network (CNN) based approaches cannot capture the global context because of inherent image-specific inductive bias. These techniques also require large and labeled datasets to train the algorithm, but not many labeled COVID-19 datasets exist publicly. To mitigate the problem, we have developed a self-attention-based Vision Transformer (ViT) architecture using CT-scan. The proposed ViT model achieves an accuracy of 98.39% on the popular SARS-CoV-2 datasets, outperforming the existing state-of-the-art CNN-based models by 1%. We also provide the characteristics of CT scan images of the COVID-19-affected patients and an error analysis of the model's outcome. Our findings show that the proposed ViT-based model can be an alternative option for medical professionals for effective COVID-19 screening. The implementation details of the proposed model can be accessed at https://github.com/Pranabiitp/ViT. © 2022 IEEE.

3.
Indian J Dermatol Venereol Leprol ; : 1-19, 2022.
Article in English | PubMed | ID: covidwho-2156074

ABSTRACT

Human skin is continually exposed to internal and external forces, dynamic as well as static. The skin is normally flexible and can resist mechanical trauma due to friction, pressure, vibration, suction and laceration to a considerable degree. However, an excess of these forces can abnormally affect the structure and function of the skin, setting the stage for the development of a skin disorder. Repetitive trauma can cause lichenification, hyperpigmentation, erythema, scaling, fissuring, blisters, ulceration and chronic alterations. Frictional dermatoses is an under-recognised entity with no clear-cut definition and encompasses a variety of terms such as frictional dermatitis, frictional melanosis, frictional pigmentary dermatoses and certain other named entities, many of which are confusing. The authors propose to define frictional dermatoses as 'a group of disorders caused by repetitive trauma to the skin as a result of friction of varied aetiology which can have a wide range of cutaneous manifestations depending on the type of insult.' The exact prevalence of frictional dermatoses as a separate entity is unknown. Authors who conducted this review include a group of dermatologists and post graduate students from various institutions. Literature was reviewed through PubMed, Medscape, Medline, ResearchGate and Google Scholar using the terms 'frictional dermatitis,' 'friction and skin,' 'dermatoses and culture,' 'clothing dermatitis,' 'friction melanosis,' 'PPE induced dermatoses in COVID-19 era,' etc. A total of 122 articles were reviewed and 100 articles among them were shortlisted and included in the study, after removing duplications. The review was followed up with further deliberation which resulted in the formulation of a new definition and classification of frictional dermatoses taking into account the morphology, histopathological characteristics, anatomical region affected and the major predisposing factors. The rising incidence of mechanical dermatoses in the COVID-19 era was also emphasised.

4.
Journal of Datta Meghe Institute of Medical Sciences University ; 17(3):693-698, 2022.
Article in English | Scopus | ID: covidwho-2155522

ABSTRACT

Background: Coronavirus pandemic has dealt a severe blow to India’s poor and socioeconomically disadvantaged group. Among a nationwide lockdown to contain the spread of the infection. This study assessed the availability of food and access to healthcare among the vulnerable population of Gujarat– pregnant women and severe acute malnourished children. Specifically, we assessed events of starvation or skipped meal, availability of healthcare services, and self-reported psychological distress during the lock down. Materials and Methods: A cross-sectional study across 252 talukas and 33 districts of Gujarat was undertaken using a structured questionnaire. A telephonic survey was carried out and positive responses were received from 161 households (HHs) with severe acute malnourished children, 328 pregnant women with severe maternal anemia, and 402 lactating women. Results: We found 79.7% of surveyed HHs received ration where major reliance was on public distribution system (51.7%). Less than half of the beneficiaries (48.6%) received take-home ration under the ICDS program. Despite efforts of the State as well as voluntary agencies, 7.3% of HHs experienced episode of hunger, mostly from Devbhumi Dwarka, and Navsari district. A third of the respondents showed signs of psychological distress associated with lockdown. Conclusion: Food insecurity may lead to malnutrition impairing the immunity of the individuals to cope with the disease. Thus, given the uncertainty around the emergency situation, preparedness measures should not only focus on the availability of healthcare commodities but also to ensure the availability of other essentials, especially to the socioeconomic disadvantaged group. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

5.
2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 ; 2022-October:409-414, 2022.
Article in English | Scopus | ID: covidwho-2152536

ABSTRACT

The three times increase of SonyLiv viewers during the Tokyo Olympic, the 10% hike of YouTube users during the isolation era of covid-pandemic, and the 19% growth in Netflix user count due to the fastest growth of OTT, etc. have made the digital platform's mode all-time active and specific. The hourly increase of users' interactions and the e-commerce platform's desire of letting users engage on their sites are pushing researchers to shape the virtual digital web as user specific and revenue-oriented. This paper develops a deep learning-based approach for building a movie recommendation system with three main aspects: (a) using a knowledge graph to embed text and meta information of movies, (b) using multi-modal information of movies like audio, visual frames, text summary, meta data information to generate movie/user representations without directly using rating information;this multi-modal representation can help in coping up with cold-start problem of recommendation system (c) a graph attention network based approach for developing regression system. For meta encoding, we have built knowledge graph from the meta information of the movies directly. For movie-summary embedding, we extracted nouns, verbs, and object to build a knowledge graph with head-relation-tail relationships. A deep neural network, as well as Graph attention networks, are utilized for measuring performance in terms of RMSE score. The proposed system is tested on an extended MovieLens-100K data-set having multi-modal information. Experimental results establish that only rating-based embeddings in the current setup outperform the state-of-the-art techniques but usage of multi-modal information in embedding generation performs better than its single-modal counterparts. 1. © 2022 IEEE.

6.
12th Annual IEEE Global Humanitarian Technology Conference, GHTC 2022 ; : 137-142, 2022.
Article in English | Scopus | ID: covidwho-2136174

ABSTRACT

This work addresses the vital need of keeping people informed with relevant, correct and essential information during the pandemic. Advanced NLP and machine learning mechanisms have been leveraged to generate responses to user queries through contextual conversation. In order to help people be discerning about what information they receive, a conversational system is proposed that identifies the correct intent of the query and a reinforcement Learning based generation model is used to proceed with conversation. We propose an end-to-end real-time text generation model that can respond to users queries on covid19. We created a new dataset with 1200+ covid-related questions from various sources and pre-processed them for a brief and direct answer. The dataset has also been manually observed to identify depressed questions and the responses are converted to be more empathetic. The dataset has been used to fine-tune DailoGPT, a GPT2-based transformer model to generate the responses related to COVID. COVID-related queries are bucketed into 15 categories to identify the exact intent of people. Our model generated both contextual and empathetic responses and achieved a human evaluation score of 3.48 (on a scale of 5) in terms of contextual relevance and a score of 2.12 (on a scale of 3). © 2022 IEEE.

7.
International Conference on Nonlinear Dynamics and Applications, ICNDA 2022 ; : 1435-1447, 2022.
Article in English | Scopus | ID: covidwho-2128343

ABSTRACT

The current work describes the scenario of Covid-19 wave by SEIR model with the aid of mathematical analysis. The SEIR model describes the present scenario using a stability point of view, namely Disease-free equilibrium (DFE) and endemic (EE) equilibrium with the aid of the next-generation matrix, to predict the possible outcomes of recovery rate, infectious growth rate, and death rate and reproduction number. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

8.
2022 International Joint Conference on Neural Networks, IJCNN 2022 ; 2022-July, 2022.
Article in English | Scopus | ID: covidwho-2097615

ABSTRACT

The worldwide breakout of the novel COVID-19 has resulted in one of the worst epidemics in modern times since World War II. Although various vaccinations are being produced, their efficacy remains a considerable hurdle. This is especially true when new virus strains emerge. The main challenge to combating this pandemic is diagnosing and isolating COVID-19 positive cases as early as possible. As a result, COVID-19 needs to be detected early and accurately to prevent its spread. This paper proposes a computer-aided automated COVID-19 detection tool based on Computed Tomography (CT-scan) images of lungs. The proposed approach applies an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. The lack of COVID-19 data makes it challenging to train a standard CNN from scratch, so we use a transfer learning approach instead of training the base classifiers, VGG-16, InceptionResnetV2, and Xception. We apply the gained knowledge in the target domain of small CT-scan data, considering ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework achieves 98.99% accuracy on a publicly available dataset and outperforms the existing state-of-the-art methods. Experimental results and comparative analysis with baselines establish the need and effectiveness of our proposed model. © 2022 IEEE.

9.
Chaos Solitons & Fractals ; 164, 2022.
Article in English | Web of Science | ID: covidwho-2068759

ABSTRACT

In the present article, global characteristics of a generalized SIRS (susceptible-infected-recovered-susceptible) epidemic model have been investigated incorporating government policy, public response and social behavioral reaction. The effects of environmental fluctuations and time-dependent control strategies on the disease dynamics have also been analyzed. In the case of deterministic model, it is shown that the disease invades in this system when the basic reproduction number (R-0) is greater than 1, whereas the dynamics of the stochastic model can be controlled by its associated basic reproduction number R-s. Specifically, this work emphasizes the importance of nonlinear dynamic analysis of epidemic modeling, as well as the significant impact of social and government actions on disease dynamics. Numerical figure depicts that the governmental action plays a crucial role to control an epidemic situation, and the system turns out to be disease-free sooner if the government takes action at an early stage during a disease outbreak. Furthermore, one of the most key developments is that random fluctuations can prevent disease outbreaks, which can lead to the development of useful control techniques to restrict disease dynamics. The governmental actions and the clinical treatment are considered to be the effective control pair in this model, and it can be observed that the simultaneous implementation of the control strategies significantly reduces the disease burden.

10.
Strategies for Student Support during a Global Crisis ; : 28-47, 2021.
Article in English | Web of Science | ID: covidwho-2068195

ABSTRACT

Online internet-based education and virtual teaching and learning have been forced upon the world due to coronavirus global pandemic healthcare crisis. Various internet and communication technology-assisted virtual delivery platforms are used, such as Zoom, Microsoft Teams, Google Hangouts, Skype, etc., to conduct lectures, tutorials, workshops, and provide online support to students. The main objective of this chapter is to reflect and compare the teaching and learning strategies in normal situation in contrast with the practice during COVID-19 environment. The chapter formalises an analysis of the challenges faced by lecturers in teaching and delivering first-year economics unit to the students, at the two institutions, and its impact on their learning of the economics core unit offered at the undergraduate Bachelor of Business program.

11.
Journal of Clinical and Diagnostic Research ; 16(9):DC24-DC27, 2022.
Article in English | EMBASE | ID: covidwho-2067201

ABSTRACT

Introduction: Hybrid Problem-Based Learning (h-PBL) is a type of teaching-learning technique that incorporates both in-person learning and virtual learning via hybrid classroom tools. It reportedly increases student engagement, positively impact their learning process and improve communication skills. During Coronavirus Disease 2019 (COVID-19) times, its applicability was further enhanced as it allowed the flexibility of teaching as well as learning from home to both teachers and students. Aim: To assess the perception and experience of 2nd phase MBBS students after undergoing training by the h-PBL method. Materials and Methods: A cross-sectional study was conducted on 2nd phase MBBS students in the Department of Microbiology at College of Medicine and Sagore Dutta Hospital, Kolkata from 15th March to 14th April 2022. A total of 111 students of 2nd phase MBBS of the college gave an informed consent to be part of the study. All inductees underwent a structured training by h-PBL technique following which their perception and experience about the exercise was sought via questionnaire. Data were presented in frequency and percentage. Association between mean scores of male and female participants was calculated by Chi-square test. Results: Out of the 111 participants, 58 (52.2%) were male and 53 (47.8%) were female with mean age of 19.5±0.5 years (range 18-22 years). The h-PBL technique was perceived to be motivating for self-directed learning by 97 (85.6%) of the respondents. A total of 107 (96.4%) students agreed that h-PBL is more effective than traditional teaching for acquiring both theoretical and practical knowledge, learning and understanding topics correctly and also identifying and rectifying their deficiencies in knowledge and skills. More than 90% participants (102 of 111) felt that h-PBL has more potential than traditional teaching to establish fruitful student-teacher interaction and provide better feedback opportunities. Overall student satisfaction in our study showed 96.4% agreement (107 of 111). Conclusion: The students considered h-PBL model to be better than traditional teaching to help them acquire theoretical knowledge and practical skills. They also felt that it improved their communication skills, teamwork ethics and motivated them to undertake self-directed learning.

12.
Cyber-Physical Systems: AI and COVID-19 ; : 139-160, 2022.
Article in English | Scopus | ID: covidwho-2048754

ABSTRACT

In COVID-19, most of the patients have been diagnosed with pneumonia in their early stages. Most of the symptoms that have been in the display or have evolved in the last couple of months like fever, cough, and shortness of breath have been predominant. Moreover, based on the studies and reports of the infected patients, symptoms like heart disease, hypertension, chest pain, diarrhea, and nasal congestion have shown a significant impact in the sustenance of COVID-19. Taking all these symptoms into consideration along with the person’s age, a prediction process has been developed in this chapter to check whether the person is infected with COVID-19 or not. Based on the significance of these attributes, we have applied artificial neural network to classify the patient’s condition into three classes, which include no infection, mild infection, and serious infection. We have achieved an accuracy of 84.7% in predicting the cases. © 2022 Elsevier Inc. All rights reserved.

13.
Journal of the Intensive Care Society ; 23(1):67-68, 2022.
Article in English | EMBASE | ID: covidwho-2043035

ABSTRACT

Introduction: Intensive Care Unit (ICU) design impacts staff well-being1 with relocation to a different ICU layout causing staff stress.2,3 During the COVID-19 pandemic our new critical care centre was opened expediently allowing increased patient capacity and providing a purpose-built environment for ICU patients. The new single-bed room layout differed to other open plan multi-bed ICUs in the hospital. New design features included large floor-to-ceiling windows with park views, modernised equipment such as computer screens on movable pendants and noise reduction features. The pandemic accelerated the opening of the new unit and practice was adapted to address surge conditions (e.g., there were two patients in each 'single' room, and PPE could only be worn in specific areas of the unit, restricting movement). Objectives: We sought to understand the impact of the ICU design on staff experiences during pandemic conditions. Methods: Following ethical approval, staff who had worked on the new unit were invited to participate in a semi-structured interview. The interview guide was based on the Theoretical Domains Framework (TDF),4 a framework to identify the determinants of behaviour change. Interviews were audio recorded, anonymised and transcribed verbatim. We used line-by-line coding and analysed data informed by the TDF. Results: 21 participants captured experiences of a wide range of multi-disciplinary staff members. The most common domain identified within the data was 'Environmental context and resources', including data pertaining to barriers and facilitators of the new unit to effective working: Having large bed spaces is perfect for getting people out [of bed]. They are soundproofed as well, so patients were sleeping really well at night. Also, 'social/professional role and identity' (including group identity, leadership), 'skills' (including competence, skills development), and 'beliefs about consequences' (perception of the effects of the new units) were frequently identified in positive and negative ways: .because of where it [the patient's room] is located you do not get to see people often. I got forgotten for rolls.It was a constant struggle Medical staff and allied health professionals described advantages over the old unit design including improved team-working, oversight of patients, and mood from the design features. Participants perceived patient benefits from improved lighting and views and stimulation due to access to social media. Conversely, nurse participants perceived less support, less team-working and increased levels of anxiety due to the single rooms. Nurse experiences improved as patient numbers reduced. However, changes in how nurse teams worked was an ongoing challenge: staffing breaks and things is quite tricky. You need a permanent floater that is never allocated to patients, to try and help people, because they cannot leave their bays. Conclusions: Our findings support previous research2 demonstrating increased nurses stress when transitioning to a single-bed room ICU layout. Providing systems to alleviate nurse isolation, promote teamworking and reduce stress in future relocations may significantly improve staff well-being (e.g., video-calling and messaging between patient rooms). A multidisciplinary awareness of the impact on nurses is vital to support strategies to ameliorate the impact of changes during relocation.

14.
Journal of Family Medicine and Primary Care ; 11(5):2231-2233, 2022.
Article in English | Web of Science | ID: covidwho-2033310

ABSTRACT

Vaccination is supposed to be the most reliable means to end the COVID 19 pandemic, but recently there have been reports of thrombosis and thrombocytopenia in patients receiving the vaccine especially ChAdOx1 nCoV-19 (AstraZeneca University of Oxford and Serum Institute of India). This has been termed as vaccine-induced immune thrombotic thrombocytopenia (VITT), thrombosis with thrombocytopenia syndrome (ITS) and vaccine-induced prothrombotic immune thrombocytopenia (VIPIT). This is a challenging situation and patients are treated with Fondaparinaux and Rivaroxaban after thrombocytopenia is corrected. Herewith, we report a case of VITT who presented to our hospital and was successfully treated over a weeks' time.

15.
2021 International Conference on Simulation, Automation and Smart Manufacturing, SASM 2021 ; 2021.
Article in English | Scopus | ID: covidwho-2018980

ABSTRACT

Recently, COVID-19 disease carried out by the SARS-CoV-2 virus appeared as a pandemic across the world. The traditional diagnostic techniques are facing a hard time detecting the virus efficiently at an early stage. In this context, chest x-ray scans can be useful for diagnostic prediction. Therefore, in this paper, a deep multi-layered convolution neural network has been proposed to analyze the chest x-ray scans effectively for detecting COVID-19 and pneumonia accurately. The proposed approach has been applied on multiple benchmark datasets and the experimental results define the effectiveness of the proposed approach. © 2021 IEEE.

16.
2022 International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2018774

ABSTRACT

COVID-19 is an infectious illness concerning coronavirus that is transmitted through droplets propagated by an infected person exhales, coughs, or sneezes. People affected by coronavirus have a risk to occur respiratory diseases (RDs). The longevity of COVID-19 may appear a vital risk of manifesting RDs. To address these issues, we explored transcriptomic data to identify the genetic effects of COVID-19 on the development of RDs such as Bronchitis (BC), Asthma (AT), Lung cancer (LC), and Pulmonary Edema (PE). We explored GEO datasets from NCBI for COVID-19, BC, AT, LC, PE case, and control subjects. We identified COVID-19 is associated with RDs by sharing 16, 19, 27, and 59 commonly DEGs accordingly. By using these genes we performed some bioinformatics analysis and constructed diseasome networks, identified functional and ontological pathways. We formed PPIs networks and PDIs network. On the basis of PPIs and PDIs, we have identified hub proteins and constructed hub proteins network. We have successfully developed a quantitative model to identify the genetic effects of COVID-19 on the progression of RDs. We also validated our investigations through gold-benchmark datasets. Our results are an effective resource to mark out the most important influences on the development of RDs for COVID-19. © 2022 IEEE.

17.
ACS Sustainable Chemistry and Engineering ; 10(30):9811-9819, 2022.
Article in English | Scopus | ID: covidwho-2016557

ABSTRACT

For the past two years, doxycycline has been employed hugely for the treatment of COVID 19 over the globe. Excessive use of doxycycline can result in bacteria and gene resistance, which affects the future treatment of infectious diseases. Furthermore, unused doxycycline left from the hospital and pharmaceutical industries may have an adverse effect on the environment, posing a significant menace to modern society. As a result, doxycycline detection is required. Herein, we developed blue luminous nitrogen-doped carbon quantum dots (N-CQDs) using ascorbic acid and diethylenetriamine (DETA) as carbon and nitrogen sources via a microwave-assisted technique for the differential detection of doxycycline (DC) via a fluorescence quenching mechanism, even when other tetracycline derivatives interfere. The quenching mechanism has been elaborately explained by using a Stern-Volmer plot, UV-vis and fluorescence spectroscopy, and TCSPC to attribute the static quenching and inner filter effect. In addition, the limit of detection of our suggested sensor is 0.25 μM. To confirm the structural properties and the size of the N-CQDs, FT-IR, Raman spectroscopy, HRTEM, DLS, and EDX have been performed. Moreover, this approach was used to identify doxycycline in pharmaceutical waste and bacterial cells. Because of its great sensitivity and selectivity, N-CQDs are ideal for measuring DC in environmental applications. © 2022 American Chemical Society. All rights reserved.

18.
Lecture Notes on Data Engineering and Communications Technologies ; 132:595-608, 2022.
Article in English | Scopus | ID: covidwho-1990589

ABSTRACT

COVID-19 is caused by the SARS-CoV-2 virus, which has infected millions of people worldwide and claimed many lives. This highly contagious virus can infect people of all ages, but the symptoms and fatality are higher in elderly and comorbid patients. Many COVID-19 survivors have experienced a number of clinical consequences following their recovery. In order to have better knowledge about the long-COVID effects, we focused on the immediate and post-COVID-19 consequences in healthy and comorbid individuals and developed a statistical model based on comorbidity in Bangladesh. The dataset was gathered through a phone conversation with patients who had been infected with COVID-19 and had recovered. The results demonstrated that out of 705 patients, 66.3% were comorbid individuals prior to COVID-19 infection. Exploratory data analysis showed that the clinical complications are higher in the comorbid patients following COVID-19 recovery. Comorbidity-based analysis of long-COVID neurological consequences was investigated and risk of mental confusion was predicted using a variety of machine learning algorithms. On the basis of the accuracy evaluation metrics, decision trees provide the most accurate prediction. The findings of the study revealed that individuals with comorbidity have a greater likelihood of experiencing mental confusion after COVID-19 recovery. Furthermore, this study is likely to assist individuals dealing with immediate and post-COVID-19 complications and its management. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

19.
Gastroenterology ; 162(7):S-596-S-597, 2022.
Article in English | EMBASE | ID: covidwho-1967341

ABSTRACT

Background: Initial studies have shown that patients with inflammatory bowel disease (IBD) have a humoral immune response rate of 95–99% to a two-dose SARS-CoV-2 mRNA vaccine series. A third mRNA vaccine dose has been recommended for the IBD population. The aim of our study was to evaluate the humoral immunogenicity a third SARS-CoV-2 mRNA vaccine dose in patients with IBD. Methods: This was a multicenter, prospective, nonrandomized study comprised of patients with IBD and healthy controls (HC) in the HERCULES cohort. IBD subject eligibility criteria included a diagnosis of IBD, stable doses of maintenance therapy (≥ 2 months), and completion of a two-dose mRNA vaccines series. IBD subjects may have received a third mRNA vaccine dose. HC eligibility criteria included absence of immunosuppressive therapy and completion of a two-dose mRNA vaccine series. HC did not receive a third dose. Those with prior COVID-19 infection were excluded. The primary outcome was total serum SARS-CoV-2 anti-spike IgG antibody concentrations following a third dose compared to antibody concentrations following the two-dose series in IBD subjects. In IBD subjects, we measured antibody concentrations at 28–35 days following completion of the two-dose series and 28–65 days following the third dose. In HC, we measured antibody concentrations at 180 days following completion of the twodose series. Antibody concentrations between groups were compared using Mann-Whitney U tests. Results: One hundred thirty-nine IBD subjects and 46 HC were enrolled. Eightyfive IBD subjects received a third dose (Table 1). One hundred thirty-five IBD subjects (97.1%) had detectable antibody concentrations post-two-dose series, while 85 IBD subjects (100%) had detectable antibody concentrations post-third dose. For IBD subjects that received a third dose, antibody concentrations were significantly higher post-third dose compared to post-two-dose series (median 68 (IQR 32–147) vs 31 (IQR 16–61), p<0.001) (Figure 1). Post-third dose, IBD subjects on systemic corticosteroids or anti-TNF combination therapy had significantly lower antibody concentrations than IBD subjects that were not (median 29 (IQR 10–39) vs 72 (IQR 37–164), p<0.001). For HC, antibody concentrations were significantly lower 180 days compared to 30 days post-two-dose series (median 17 (IQR 11–22) vs 120 (IQR 88–190), p<0.001). HC had lower antibody concentrations 180 days post-two-dose series compared to IBD subjects post-third dose (median 17 (IQR 11– 22) vs 68 (IQR 32–147), p<0.001). Conclusion: All patients with IBD receiving a third SARS-CoV-2 mRNA vaccine dose were seropositive, and median antibody concentrations were higher than those measured after the two-dose series. Patients on corticosteroids and anti-TNF combination therapy had lower antibody concentrations than patients not on such therapy following a third dose. (Table Presented) (Figure Presented)

20.
Journal of Population and Social Studies ; 30:866-876, 2022.
Article in English | Scopus | ID: covidwho-1964997

ABSTRACT

Coronavirus disease 2019 (COVID-19) became a global pandemic within a few months. Even though Bangladesh has been badly affected by COVID-19, the pandemic is still a concern across the country. This study was conducted to explore regional variations in preventive health practices of rural adults during the COVID-19 pandemic, and to determine the predictors regarding COVID-19 prevention. A cross-sectional survey was conducted in rural Bangladesh in 2020 among 810 respondents selected by multi-stage random sampling. Data collection was done by face-to-face interviews using a questionnaire. The results showed that almost half of the rural adults (48.1%) had poor health practices regarding COVID-19 prevention. Rural adults of the Mymensingh district showed relatively better health practices during the pandemic (U = 58,747.5, p < 0.001). Not only the background issues but also information, attitude, motivation, and intention in COVID-19 prevention were significant in predicting the health practices of rural adults in COVID-19 prevention. The significant regional effect was determined in COVID-19 prevention behavior of rural adults (p < 0.001) in hierarchical regression, explained through a modified reasoned action approach. Health programs should be strengthened more, not only to improve preventive health practices of rural adults but also to establish regional equity in COVID-19 prevention, ensuring region-specific initiatives on behavioral changes. © 2022. Journal of Population and Social Studies. All Rights Reserved.

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