Impact of Lockdown Measures on Health Outcomes of Adults with Type 2 Diabetes Mellitus in Bangladesh.

COVID-19 lockdown measures appreciably affected patients' lifestyles, negatively impacting on their health. This includes patients with Type 2 Diabetes Mellitus (T2DM). Care of these patients was also negatively impacted due to a priority to treat patients with COVID-19, certainly initially, within hospitals and clinics in Bangladesh, combined with a lack of access to clinics and physicians due to lockdown and other measures. This is a concern in Bangladesh with growing rates of T2DM and subsequent complications. Consequently, we sought to critically analyze the situation among patients with T2DM in Bangladesh during the initial stages of the pandemic to address this information gap and provide future direction. Overall, 731 patients were recruited by a simple random sampling method among patients attending hospitals in Bangladesh, with data collected over 3 timescales: before lockdown, during the pandemic, and after lockdown. Data extracted from patients' notes included current prescribed medicines and key parameters, including blood sugar levels, blood pressure, and comorbidities. In addition, the extent of record keeping. The glycemic status of patients deteriorated during lockdown, and comorbidities as well as complications related to T2DM increased during this period. Overall, a significant proportion of key datasets were not recorded in patients' notes by their physician before and during lockdown. This started to change after lockdown measures eased. In conclusion, lockdown measures critically affected the management of patients with T2DM in Bangladesh, building on previous concerns. Extending internet coverage for telemedicine, introduction of structured guidelines, and appreciably increasing data recording during consultations is of the utmost priority to improve the care of T2DM patients in Bangladesh.

COVID-19-Associated Rhino-orbital mucormycosis: Presentation and outcome.

We are reporting four accounts of rhino-orbital mucormycosis in patients during and after recovery from SARS-CoV-2 infection. The patients were diagnosed and treated for COVID-19 according to the current treatment protocols, following which they presented with sudden proptosis, ophthalmoplegia, and conjunctival injection, confirmed by magnetic resonance imaging and histopathological examination. The patients were treated with intravenous liposomal amphotericin B, and the outcome was observed. Early diagnosis and prompt intervention can substantially reduce the morbidity and mortality rates in these patients.

Mediating Effect of Challenges on Demographics and Coping Strategies of Indian Healthcare Workers during COVID-19.

Healthcare workers (HCWs) including doctors, nurses and allied workers struggled to cope up with the stressful situation as the COVID-19 pandemic unsettled healthcare systems, including India's. Many factors (commonly called as stressors) acted as major sources of stress and resulted in poor mental health of HCWs. Therefore, this study predicted and explained the mediating effect of challenges on demographic characteristics and coping strategies of HCWs. Data from a cross-sectional study was collected from the district hospital of Rajasthan, India, during the period of August 2022-October 2022. HCW's experience level, shift type and distance of greenspaces from their accommodation were significantly correlated with the challenges they faced at work, specifically societal challenges. Thus, HCWs were more inclined to adopt a meaning-focused coping strategy to retain good mental health during the pandemic. Therefore, these findings call for interventions requiring a layered response, comprising strategies and actions that are structural. At the organizational level, these actions may provide supportive workplace environments.

Therapeutic implications of current Janus kinase inhibitors as anti-COVID agents: A review.

Severe cases of COVID-19 are characterized by hyperinflammation induced by cytokine storm, ARDS leading to multiorgan failure and death. JAK-STAT signaling has been implicated in immunopathogenesis of COVID-19 infection under different stages such as viral entry, escaping innate immunity, replication, and subsequent inflammatory processes. Prompted by this fact and prior utilization as an immunomodulatory agent for several autoimmune, allergic, and inflammatory conditions, Jakinibs have been recognized as validated small molecules targeting the rapid release of proinflammatory cytokines, primarily IL-6, and GM-CSF. Various clinical trials are under investigation to evaluate Jakinibs as potential candidates for treating COVID-19. Till date, there is only one small molecule Jakinib known as baricitinib has received FDA-approval as a standalone immunomodulatory agent in treating critical COVID-19 patients. Though various meta-analyses have confirmed and validated the safety and efficacy of Jakinibs, further studies are required to understand the elaborated pathogenesis of COVID-19, duration of Jakinib treatment, and assess the combination therapeutic strategies. In this review, we highlighted JAK-STAT signalling in the pathogenesis of COVID-19 and clinically approved Jakinibs. Moreover, this review described substantially the promising use of Jakinibs and discussed their limitations in the context of COVID-19 therapy. Hence, this review article provides a concise, yet significant insight into the therapeutic implications of Jakinibs as potential anti-COVID agents which opens up a new horizon in the treatment of COVID-19, effectively.

Indian Healthcare Workers' Issues, Challenges, and Coping Strategies during the COVID-19 Pandemic: A Cross-Sectional Study.

India faced the maximum number of mortalities and morbidities during the second wave of COVID-19. Healthcare workers (HCWs) worked in high-pressure and stressful environments. Therefore, this study aimed to assess the common issues, challenges, and coping strategies of HCWs, as well as the statistical association between demographical characteristics and coping strategies. A cross-sectional study was conducted with 759 HCWs, involving simple, random sampling in Rajasthan, India, between August 2022 and October 2022. Participants responded to a self-administered questionnaire that included a Brief-COPE inventory. The statistical association between commonly adopted coping strategies and demographic characteristics was tested using the chi-square test and Fisher's exact test. A total of 669 (88%) respondents agreed that they faced issues during the COVID-19 pandemic: 721 (95%) participants experienced challenges at the personal level, 716 (94%) at the organizational level, and 557 (74%) at the societal level. Problem-focused coping strategies were frequently adopted by the participants. Gender, marital status, education, hours of work per day, and residential area were significantly associated with a problem-focused coping strategy (p < 0.05). The findings of this study reported a limited use of coping strategies by the participants during the public health crisis, despite facing issues and challenges at work. These findings highlight the need to assist HCWs in developing coping mechanisms to maintain good mental health at work.

A cross-sectional study to determine the psychological distress among pulmonary tuberculosis patients during COVID-19 pandemic.

COVID-19 pandemic had adversely affected the services of the National Tuberculosis (TB) Elimination Programme, resulting in psychological distress among pulmonary tuberculosis patients (PTB). This cross-sectional, hospital-based study included 361 PTB patients. Three pre-defined questionnaires were used for the analysis, a questionnaire to evaluate anxiety related to COVID-19, a patient health questionnaire (PHQ-9) for depression, and a fear of COVID-19 scale (FCV-19S) questionnaire. Among 361 PTB patients, 13% (n=47) had COVID-19 infection. Out of the total patients, 69% (n=250) were DR-TB (drug resistance-tuberculosis) cases. Proportion of anxiety, fear and depression due to COVID-19 was found in 49% (n=177), 23% (n=83), 67% (n=247) respectively. Delay in the initiation of anti-tubercular treatment was found in 58% (n=210) of the cases, among which the majority, i.e., 69% (n=172, p=0.011), were DR-TB. This pandemic has led to a sudden step-down of PTB. Trend analysis of the psychological distress showed a peak following the COVID-19 pandemic. Most DR-TB patients had delayed initiation of the anti-tubercular treatment during the pandemic. The preponderance of the younger age group was seen in the pulmonary tuberculosis patients, and a majority of them had DR-TB. Depression was the predominant psychological distress among the study subjects during the pandemic.

Breast Cancer Management in the Era of Covid-19; Key Issues, Contemporary Strategies, and Future Implications.

During the COVID-19 pandemic, several priority diseases were not getting sufficient attention. Whilst breast cancer is a fatal disease affecting millions worldwide, identification and management of these patients did not initially attract critical attention to minimize the impact of lockdown, post-lockdown, and other measures. Breast cancer patients' conditions may not remain stable without proper care, worsening their prognosis. Proper care includes the timely instigation of surgery, systemic therapy, and psychological support. This includes low-and middle-income countries where there are already concerns with available personnel and medicines to adequately identify and treat these patients. Consequently, there was a need to summarize the current scenario regarding managing breast cancer care during COVID-19 across all countries, including any guidelines developed. We systematically searched three scientific databases and found 76 eligible articles covering the medical strategies of high-income countries versus LMICs. Typically, diagnostic facilities in hospitals were affected at the beginning of the pandemic following the lockdown and other measures. This resulted in more advanced-stage cancers being detected at initial presentation across countries, negatively impacting patient outcomes. Other than increased telemedicine, instigating neo-adjuvant endocrine therapy more often, reducing non-essential visits, and increasing the application of neo-adjuvant chemotherapy to meet the challenges, encouragingly, there was no other significant difference among patients in high-income versus LMICs. Numerous guidelines regarding patient management evolved during the pandemic to address the challenges posed by lockdowns and other measures, which were subsequently adopted by various high-income countries and LMICs to improve patient care. The psychological impact of COVID-19 and associated lockdown measures, especially during the peak of COVID-19 waves, and the subsequent effect on the patient's mental health must also be considered in this high-priority group. We will continue to monitor the situation to provide direction in future pandemics.

Evaluation of Short-Term Side Effects Following the First Dose of COVID-19 Vaccines Among Physicians and Dentists: A Cross-Sectional Study from India.

Background: Efficacy and safety are fundamental for the development of successful COVID-19 vaccines. Vaccine-associated side effects influence vaccine hesitancy. This study investigated the prevalence, severity, and onset of side effects following the first dose of COVID-19 vaccines among physicians and dentists working in various healthcare settings across India. Methods: A cross-sectional survey collected self-report data from April to June 2021 on side effects following the first dose of the vaccine. An online validated questionnaire using the Google Docs® platform was circulated via email and social media platforms. Results: More than 40% of participants experienced at least one side effect after the first dose of vaccination; the most common were mild and resolved within three days after vaccination. More than 91% of respondents received the Covishield (AstraZeneca) vaccine; the most prevalent adverse effects were soreness of the injected arm (78.9%), tiredness (71.1%), and fever (54.9%). Logistic regression showed that women were almost 60% less likely to report side effects. Conclusion: Findings supported the safety of the first dose of the COVID-19 vaccine based on relatively few self-limiting side effects, mainly soreness of the injected arm and tiredness. Further research is needed to determine the long-term safety of COVID-19 vaccines, especially after booster doses.

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. METHODS: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. RESULTS: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. CONCLUSION: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

COMPARISON OF CURB-65 SCORES IN NON-DIABETIC AND DIABETIC SUBJECTS HOSPITALIZED FOR COVID-19 INFECTION

Background: Diabetes mellitus has been established as a contributory factor for comorbidity in subjects with COVID-19 owing to diabetics being at high infection susceptibility from different bacteria and viruses including those of the respiratory tract. CURB 65 scores are an easier system among the various scoring systems developed to assess CAP risk. Aim: To record and comparatively analyze the CURB-65 scores in non-diabetic and diabetic subjects hospitalized for COVID-19 infection in an Indian health care center. Methods: In 280 subjects admitted for COVID-19 infection, glycemic state and CURB-65 scores were evaluated. The subjects were grouped as having mild, moderate, or severe illnesses based on the CURB-65 scoring. Also, ICU admission, the requirement of a ventilator, hospitalization duration, and mortality rates were assessed. All subjects were followed till discharge or death, whichever was early. Results: Mild CURB-65 was seen for 65.21% (n=90) diabetic subjects and 97.18% (n=138) non-diabetic subjects. 30.43% (n=42) diabetic subjects and 2.81% (n=4) non-diabetic subjects had CURB-65 scores as moderate. ICU admission was needed in 24.63% (n=34) diabetic subjects and in 5.63% (n=8) non-diabetic subjects (p=0.002). Ventilatory support was needed in 18.84% (n=26) diabetic subjects in the study and in 4.22% (n=6) non-diabetic subjects. This difference was statistically significant with p=0.007. 24.63% (n=34) diabetic subjects died and in non-diabetic (p<0.0001). The mean duration of hospital stay was 9.23±5.2 days in diabetic subjects and 7.03±4.28 days in nondiabetic subjects (p=0.005). Conclusion: Increased and higher values of CURB-65 scores were seen for subjects having diabetes mellitus and COVID-19 infection compared to non-diabetic subjects with COVID-19 infection. Also, the disease severity was more in subjects with diabetes mellitus and COVID-19 compared to non-diabetics. [ FROM AUTHOR]

Characteristics and Management of Children With Suspected COVID-19 Admitted to Hospitals in India: Implications for Future Care.

INTRODUCTION: There is a growing focus on researching the management of children with COVID-19 admitted to hospital, especially among developing countries with new variants alongside concerns with the overuse of antibiotics. Patient care can be improved with guidelines, but concerns with the continued imprudent prescribing of antimicrobials, including antibiotics, antivirals, and antimalarials. OBJECTIVE: Consequently, a need to document the current management of children with COVID-19 across India. Key outcome measures included the percentage of prescribed antimicrobials, adherence to current guidelines, and mortality. METHODOLOGY: A point prevalence study using specially developed report forms among 30 hospitals in India. RESULTS: The majority of admitted children were aged between 11 and 18 years (70%) and boys (65.8%). Reasons for admission included respiratory distress, breathing difficulties, and prolonged fever. 75.3% were prescribed antibiotics typically empirically (68.3% overall), with most on the Watch list (76.7%). There were no differences in antibiotic prescribing whether hospitals followed guidelines or not. There was also appreciable prescribing of antimalarials (21.4% of children), antivirals (15.2%), and antiparasitic medicines (27.2%) despite limited evidence. The majority of children (92.2%) made a full recovery. CONCLUSION: It was encouraging to see low hospitalization rates. However, concerns about high empiric use of antibiotics and high use of antimalarials, antivirals, and antiparasitic medicines exist. These can be addressed by instigating appropriate stewardship programs.

Impact of Social Media on Student Life

The Internet has changed everyone's life. Social networks may have only been initiated with the help of the Internet. Today's generation is using the internet, which means social media like Snap-chat, Twitter, Facebook, etc., is increasing. Hence, the purpose of this paper is to understand the positive and negative outcomes of social media. In addition, this paper discusses the current situation, the previous situation, and the situation where people do not have social networks. Finally, the influence of social networks on different stages of a person's life is described.

Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%.

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19☆

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19☆

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswra cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67 percentage. Graphical

A Novel Multimodal Fusion Framework for Early Diagnosis and Accurate Classification of COVID-19 Patients Using X-ray Images and Speech Signal Processing Techniques

Background and objective: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. Methods: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. Results: the proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. Conclusion: this paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

Hepatic, Extrahepatic and Extracellular Vesicle Cytochrome P450 2E1 in Alcohol and Acetaminophen-Mediated Adverse Interactions and Potential Treatment Options.

Alcohol and several therapeutic drugs, including acetaminophen, are metabolized by cytochrome P450 2E1 (CYP2E1) into toxic compounds. At low levels, these compounds are not detrimental, but higher sustained levels of these compounds can lead to life-long problems such as cytotoxicity, organ damage, and cancer. Furthermore, CYP2E1 can facilitate or enhance the effects of alcohol-drug and drug-drug interactions. In this review, we discuss the role of CYP2E1 in the metabolism of alcohol and drugs (with emphasis on acetaminophen), mediating injury/toxicities, and drug-drug/alcohol-drug interactions. Next, we discuss various compounds and various nutraceuticals that can reduce or prevent alcohol/drug-induced toxicity. Additionally, we highlight experimental outcomes of alcohol/drug-induced toxicity and potential treatment strategies. Finally, we cover the role and implications of extracellular vesicles (EVs) containing CYP2E1 in hepatic and extrahepatic cells and provide perspectives on the clinical relevance of EVs containing CYP2E1 in intracellular and intercellular communications leading to drug-drug and alcohol-drug interactions. Furthermore, we provide our perspectives on CYP2E1 as a druggable target using nutraceuticals and the use of EVs for targeted drug delivery in extrahepatic and hepatic cells, especially to treat cellular toxicity.

Benchmarking the assessment of barriers to the admission of management education in India during the COVID-19 pandemic

Purpose>This paper proposes a decision-making framework for assessing various dimensions and barriers that have affected the admission process in management educational institutions during the ongoing pandemic. The framework considers the interrelationship between the obstacles and highlights the importance of each barrier.Design/methodology/approach>An integrated method based on decision-making trial and evaluation laboratory and analytical network process is proposed to structure the barrier assessment framework. Results obtained from the study are validated by comparing them against the conventional analytical hierarchy process.Findings>The results obtained from this study indicate four significant dimensions that hinder admission in Indian management institutes, namely, governmental, financial, sectoral, institutional and market. The top five barriers are demand shift towards technical (alternative) skills, acceptance of the graduated students, lack of industry–institute collaboration, lack of long-term vision and opening new Indian Institute of Technologies (IITs) and Indian Institute of Managements (IIMs).Research limitations/implications>During this ongoing pandemic, many educational institutes have been forced to shift from the traditional classroom to a virtual teaching model. In this regard, this study helps identify and assess the barriers to admission in Indian management institutes during this epidemic and thus, contribute to the literature. The findings will assist all stakeholders and policymakers of management institutions design and develop appropriate managerial strategies. The study is conducted in the Indian management educational institute context and can be extended to technical education institutions for deeper insights.Originality/value>The paper develops an assessment framework for analysing the barriers to admission in Indian management institutes during the ongoing COVID-19 pandemic. Research implications are discussed in the context of a developing country.

Influence of Webinar-Based Learning on Practice of Percutaneous Nephrolithotomy: Outcomes of a Global Survey.

Background: With webinars looking to be the mainstay post-pandemic, it is important to demonstrate whether webinars are, indeed, effective educational tools for professional training and skill acquisition. We aim at demonstrating, via a global survey, the efficacy of webinars on percutaneous nephrolithotomy (PCNL) and how this knowledge transforms clinical practice. Methods: A structured online survey covering the following sections: (1) Demographics, (2) PCNL techniques, and (3) PCNL equipment was circulated. The target study population were practicing urologists and residents. Categorical data were presented with counts and percentages, and they were compared by using Chi-square test. Continuous data were analyzed with non-parametric methods. Respondents were dichotomized according to attendance of webinar type, attendees of dedicated PCNL webinars (Group A), or attendees of endourological webinars that discussed some aspects of PCNL (Group B). Results: A total of 303 respondents from 38 countries participated. Overall, 91.7% (n = 278) were in Group A and 8.3% (n = 25) were in Group B; 77.9% were younger than 50 years, whereas 51.8% had more than 10 years of urology experience. In group A, urologists of all ages, in academic institutions and private practitioners, significantly benefited in gaining knowledge about the merits of newer devices and the role of suction-assisted devices in modern PCNL. The majority of group A also reflected that by attending a dedicated PCNL-based webinar they benefited in learning newer positions for PCNL access, especially supine, and how to effectively use laser as energy devices for lithotripsy. In Group B, the only area of benefit was in lasing techniques and the use of newer lasers such as the thulium fibre laser. Conclusion: Our survey positively validates the two proposed hypothesis, that is, webinars as a medium of education do benefit practicing urologists in knowledge and the clinical practice domains. Age, experience, or place of practice is no barrier to adopting newer mediums of education such as webinars.

SPR Based Biosensing Chip for COVID-19 Diagnosis-A Review.

Surface Plasmon Resonance (SPR) techniques are highly accurate in detecting biomolecular like blood group measurement, food adulteration, milk adulteration and recently developing as a rapid detection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. In order to validate the clinical diagnosis, Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) of nasopharyngeal swabs has been utilized, which is time consuming and expensive. For fast and accurate detection of the SARS-CoV-2 virus, SPR based biosensing chips are described in this review article. SPR sensors have the potential to be employed for fast, accurate, and portable SARS-CoV-2 virus diagnosis. To combat the SARS-CoV-2 pandemic, there is considerable interest in creating innovative biosensors that are quick, reliable, and sensitive for COVID-19 diagnosis.