Use of metolazone as an adjunct therapy to loop diuretics in diuretic resistant acute decompensation of heart failure: A systematic review and meta-analysis: Metolazone in diuretic resistant AHF: A Meta-Analysis

Introduction: Loop diuretics are the first-line treatment for volume overload in acute decompensation of congestive heart failure (AHF). Loop diuretic resistance is common due to pharmacologic tachyphylaxis. Therefore, thiazide and thiazide-like diuretics are often used as add-on therapy to combine two different pharmacologic mechanisms. This systemic review and meta-analysis aimed to synthesize the current evidence on the efficacy and safety of metolazone and other thiazide-like diuretics in AHF. Method(s): PRISMA guidelines were followed in conducting this systematic review. PubMed, Scopus, PubMed Central, and Embase databases were searched using relevant keywords for studies published before 5 Jan 2022. and title screening was performed, followed by full-text screening using the Covidence software. Data were extracted, and analysis was done using Cochrane Review Manager (RevMan v5.1). The results were reported in odds ratio and mean difference with 95% confidence intervals. Result(s): Out of 2999 studies identified by database search, eight studies met the inclusion criteria (2 RCTs and 6 cohort studies). Pooled analysis using a random-effects model showed no difference in mean difference among the metolazone group and control group for 24 hours total urine output (MD 69.32, 95% CI -638.29 to 776.94;n = 551;I2 = 84%), change in urine output in 24 hours (MD -284.09, 95% CI -583.99 to 15.81;n = 345;I2 = 0%), 48 hours total urine output (MD -465.62, 95% CI -1302.22 to 370.99;n = 242;I2 = 0%) and urine output at 72 hours (MD -13.24, 95% CI -90.88 to 64.40;n = 205;I2 = 0%). However, studies with furosemide only in the comparator arm, 24 hours of total urine outcome favored metolazone (MD 692.70, 95% CI 386.59 to 998.82;n = 334;I2 = 0%). There was no difference between the two groups in the rate of adverse events, loss of weight, mortality, or readmission rates. Conclusion(s): Metolazone therapy in diuretic resistant AHF may improves urine output and facilitates achieving a net negative balance. Thus, metolazone and thiazide-like diuretics can be used as add-on therapy in acute decompensation of heart failure, especially in diuretic resistance.Copyright © 2023 The Author(s)

Demographic and clinical characteristics of COVID-19 patients in the least developed province Balochistan of Pakistan – an observational descriptive study

This study was designed to determine the epidemiological and clinical attributes of COVID-19 patients in the least developed province of Balochistan, Pakistan. The information was obtained from the daily situation report by the Health Department, Government of Balochistan, Pakistan. We investigated the reports of 4177 patients confirmed by RT-PCR tests. Demographic, epidemiological and risk factors data along with comorbidities and clinical signs were recorded. Out of 4500 suspected cases, 4177 cases were directed for the confirmation of COVID-19. A sum of 2177 patients was confirmed to have COVID-19 and 2000 individuals tested negative for the illness. Out of 4177 patients, 2000 patients recovered but 177 patients died because of COVID-19. In current statistics, most males were affected by COVID-19 as 3243 (77.69%) were males and 934 (22.36%) were females. A total of 90.81% of individuals had fever, 88.97% had a cough, 81% had body throbs, and 89.66% had a sore throat. Shortness of breath was observed in 97.06% and 44.09 % had comorbidity. Multiple logistic regression analysis showed that the outcome of patients was associated with gender and symptoms. The district Quetta had the maximum number of COVID-19 cases and deaths. COVID-19 cases and case casualty proportion are low in Balochistan. Whether this is because of failure to do more tests is still to be discovered. Males and individuals of older age are more impacted, and fatalities were higher in cases with co-morbid conditions. Balochistan has a feeble medical care framework and many asymptomatic cases, and needs more rigid screening activities. © 2023, Mahidol University - ASEAN Institute for Health Development. All rights reserved.

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.

Impact of Covid-19 on Mental Health among Medical Students

Background: The COVID-19 pandemic is a major health crisis affecting several nations. Such widespread outbreaks are associated with adverse mental health consequences. Objective(s): To conduct a survey-based assessment of mental health among medical students during the COVID-19 pandemic. Aimed at identifying severity levels of depression and anxiety, stressors related to the pandemic, and barriers students experienced in handling the pandemic-related stress. Method(s): An analytical cross-sectional study was chosen as the study design for this research to study the association between demographic social and mental health among medical students during the pandemic COVID-19. Result(s): The results of this study were collected by respondents through questionnaires as the respondents were needed to answer about 16 questions and the main question was asked mostly about their mental health condition during the pandemic COVID-19. 101 respondents participated in the study. Discussion(s): the impact of COVID-19 on mental health among medical students has been studied. Due to the long-lasting pandemic situation and numerous measures such as lockdown and stay-at-home orders, COVID-19 brings negative impacts on higher education of medical students, self and social isolation, disconnection from friends and teachers resulting in more medical students than ever experiencing feelings of helplessness, isolation, grief, anxiety and depression. The issue of mental health is not only relevant but crucial. Demand for health support services has increased exponentially as a result. Conclusion(s): In this study, severity levels of depression and anxiety, stressors related to the pandemic, and barriers students experienced in handling the pandemic-related stress have increased due to many factors such as social isolation, own health and the health of loved ones, financial difficulties, suicidal thoughts, depressive thoughts, class workload, changes in living environment, eating patterns and sleeping habits.Copyright © RJPT. All right reserved.

Performance Evaluation of State-of-The-Art 2D Face Recognition Algorithms on Real and Synthetic Masked Face Datasets

Face recognition systems based on Convolutional neural networks have recorded unprecedented performance for multiple benchmark face datasets. Due to the Covid-19 outbreak, people are now compelled to wear face masks to reduce the virus's transmissibility. Recent research shows that when given the masked face recognition scenario, which imposes up to 70% occlusion of the face area, the performance of the FR algorithms degrades by a significant margin. This paper presents an experimental evaluation of a subset of the MFD-Kaggle and Masked-LFW (MLFW) datasets to explore the effects of face mask occlusion against implementing seven state-of-the-art FR models. Experiments on MFD-Kaggle show that the accuracy of the best-performing model, VGGFace degraded by almost 40%, from 82.1% (unmasked) to 40.4% (masked). On a larger-scale dataset MLFW, the impact of mask-wearing on FR models was also up to 50%. We trained and evaluated a proposed Mask Face Recognition (MFR) model whose performance is much better than the SOTA algorithms. The SOTA algorithms studied are unusable in the presence of face masks, and MFR performance is slightly degraded without face masks. This show that more robust FR models are required for real masked face applications while having a large-scale masked face dataset. © 2023, Penerbit Akademia Baru. All rights reserved.

A Physics Learning System Using Gamification for High-School Students

With rapid proliferation of digitalization and compulsion by COVID-19 pandemic, learning formats have been changing from face-To-face to online. Online education enables learners to take courses from anywhere, anytime, but it can also cause some problems for learners who struggle to maintain motivation. In addition, for STEAM education, it is important to engage in hands-on activities, but the ongoing pandemic has made it difficult for students to gather in one place to perform such activities. Incorporating gamification into online education can potentially motivate students and make STEAM education more interactive. On this premise, we have developed PhyGame as a learning system to help high-school students learn Physics. The system includes common game elements such as badges and leaderboards, and interactive simulation of Physics concepts embodying game-like charm. It also includes three modes of learning that allow students to adjust the difficulty according to their own learning levels, and a function that automatically saves learning log. For evaluation, PhyGame was used by students (N=23) at a high school in central Tokyo. The students rated the system on a scale of 1 to 10, and the main results are as follows: (1) Using PhyGame made learning enjoyable (mean score: 7.74);(2) PhyGame provided a good UI/UX (mean score: 7.83);(3) The overall experience with PhyGame was satisfactory (mean: 7.00). Our evaluation results show that interactive and gamified learning systems like PhyGame have a positive impact on user engagement and motivation. © 2023 IEEE.

Applications of Machine Learning in Viral Disease Diagnosis

Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.

Estimating Prevalence of Bereavement, Its Contribution to Risk for Binge Drinking, and Other High-Risk Health States in a State Population Survey, 2019 Georgia Behavioral Risk Factor Surveillance Survey.

BACKGROUND: Binge drinking is a pattern of alcohol abuse. Its prevalence and associated risk factors are not well documented. Heavy drinking, on the other hand, has a well-documented association with bereavement. This report uses a cross-sectional, population-based survey to estimate prevalence of bingeing and its association with new bereavement. Bingeing is defined as 4 or more drinks (women) or 5 or more drinks (men) in a 2-4-h setting. For the first time in 2019, the Georgia Behavioral Risk Factor Surveillance Survey (BRFSS) included a bereavement item: 'Have you experienced the death of a family member or close friend in the years 2018 or 2019?' METHODS: Georgia BRFSS is a complex sampling survey administered annually. It is designed to represent the 8.1 million people aged 18 years and older in the U.S. state of Georgia. Alcohol consumption patterns are routinely measured in the common core. In 2019, the state added a new item probing for bereavement in the prior 24 months predating the COVID-19 pandemic. Imputation and weighting techniques were applied to yield the population prevalence rates of new bereavement, bingeing, and their co-occurrence with other high-risk health behaviors and outcomes. Multivariate models, adjusted for age, gender, and race, were used to estimate the risk for other unhealthy behaviors posed by the co-occurrence of bereavement and bingeing. RESULTS: In Georgia, bereavement (45.8%), and alcohol consumption (48.8%) are common. Bereavement and alcohol use co-occurred among 1,796,817 people (45% of all drinkers) with a subset of 608,282 persons reporting bereavement combined with bingeing. The most common types of bereavement were death of a friend/neighbor (30.7%) or three plus deaths (31.8%). CONCLUSIONS: While bingeing is a known risk to public health, its co-occurrence with recent bereavement is a new observation. Public health surveillance systems need to monitor this co-occurrence to protect both individual and societal health. In a time of global bereavement, documenting its influence on binge drinking can support the work towards Sustainable Development Goal #3-Good health and Well-Being.

Block Toeplitz Matrices: Multiplicative Properties

Given A, B, C, and D, block Toeplitz matrices, we will prove the necessary and sufficient condition for AB - CD = 0, and AB - CD to be a block Toeplitz matrix. In addition, with respect to change of basis, the characterization of normal block Toeplitz matrices with entries from the algebra of diagonal matrices is also obtained.

Review of the Literature on Ramadan Fasting and Health in 2022

Objectives The literature on health and disease during Ramadan fasting (RF) is widely spread in many journals making it not readily accessible to those interested in the subject. Here, we provide an overview of the research on the interplay of RF with various aspects of well-being published in 2022.Materials and Methods A narrative, nonsystematic review of the international literature from a single major medical online database, PubMed, in one calendar year (2022) was conducted. The search term "Ramadan fasting" was used to retrieve the appropriate records. The relevant literature with substantial data-based content was presented in a concise thematic account, excluding those concerned with diabetes.Results Themes that emerged from the review included the pathophysiology of metabolic changes during RF, nutritional aspects including body composition and energy metabolism, cardiovascular disease and risk factors, renal function and structure, endocrinology (mainly thyroid), neurological disorders, mental health, pregnancy and fetal life, and infections (including COVID). Some miscellaneous clinical themes were identified, such as patients' and professional perspectives.Conclusions In 2022, the medical interest in RF was again widely spread across specialties. Cardiovascular disease and risk factors attract the most interest in terms of original articles and professional guidelines. We hope with this review to present a concise summary of the scholarly work on the subject in this year.

Prevalence of COVID-19 Vaccine Hesitancy in South Asia: A Systematic Review and Meta-Analysis

Vaccine uptake and coverage in susceptible populations are needed through effective vaccination campaigns to address the COVID-19 pandemic in South Asian countries. We aimed to measure the pooled proportion of COVID-19 vaccine hesitancy in this regard. Research articles published between January 1, 2020, to December 31, 2021, were searched through Medline, PubMed, Cochrane, Google Scholar, and the WHO COVID-19 database. The Joanna Briggs Institute (2014) tool for prevalence studies was used to assess data quality. We performed a meta-regression test and a sensitive analysis among the studies and used the DerSimonian and Laird random-effects model to measure the pooled effect estimates. Subgroup analyses were performed concerning vaccine hesitancy, countries, study population, study level, and the time since the first outbreak of the pandemic. A total of 43 studies out of 598 published articles across the eight countries in South Asia were included. The pooled proportion of COVID-19 vaccine hesitancy was 26.5% (95% CI [22, 31], I2 = 99.59%). Vaccine hesitancy was higher in Afghanistan (37%), Pakistan (33%), and Bangladesh (28.9%);among the general population (29%);at community levels (27.9%);and the duration of time of 1–12 months since the first outbreak in each country (27.5%). Vaccine hesitancy exists in South Asia with different rates among countries, population sub-groups, communities, study-levels, duration of time since the first outbreak, and study population. Therefore, enhancing public awareness of vaccination and vaccine hesitancy is required to prevent future pandemics. © 2023,Journal of Population and Social Studies. All Rights Reserved.

Relationship of clinical symptoms, comorbidities and treatment options along with disease outcomes in individuals tested for COVID-19 in Pakistan: a national survey

Background: This web-based survey is done to collect and assess data from people tested for COVID-19 with PCR in Pakistan. Methods: This 3-month study is a cross-sectional online survey, conducted by Pakistan Islamic Medical Association (PIMA), Health Research Advisory Board (HealthRAB) and National Institute of Health (NIH). Data collection was done using Google Forms. People who were tested for COVID-19 using Polymerase Chain Reaction (PCR) were included in the study. The sample size of the study was 1,537. SPSS version 22 was used for data analysis. Results: Majority of the respondents belonged to the age group 20 - 39 years. The most common symptoms found were fever 633 (41%), cough 534 (34%), generalized body aches 432 (28%) and sore throat 392 (25%). The mean COVID-19 mental health score was 3.59 (SD: 5.808, range: 0-18). Treatment with antibiotics and painkillers had a strong correlation (p-value < 0.05) with the disease outcomes. The disease outcomes had moderate correlation (p-value < 0.05) with anti-allergy, steroids, plasma and oxygen therapy, and weak correlation (p-value < 0.05) with Antiviral and Antimalarial therapy. Out of the total respondents, 561 (36.1%) were cured from COVID-19, 14 (0.9%) were expired during/after hospitalization, 15 (1%) were still infected and 962 (62%) were not infected. Conclusion: Pakistani population has a better cure rate than some of its neighboring countries. However, further research in this area is required to draw a definite conclusion.

Efficacy of Paracetamol or Ibuprofen in The Management of COVID-19 Fever: A SystematicReview

The Global outbreak of COVID-19 pandemic affected almost all countries and territories worldwide. The outbreak was first identified from Wuhan, China, in December 2019 and was declared a pandemic in March 2020. Virus incubation time is usually 7 days and initial symptoms includes fever, cough, flu, muscle fatigue and difficulty in breathing. Ibuprofen and paracetamol are the two most commonly used over the counter (OTC) drugs to treat fever due to COVID-19. Some researchers discouraged the use of ibuprofen initially due to possible adverse effects related with longevity of infection, increased morbidity, and mortality rate. This study aimed to compare the effectiveness of paracetamol and ibuprofen as anti-pyretic drugs to treat fever caused in COVID-19 infection. A systematic review of major databases i.e., PubMed, Cochrane library, Web of Science, Google scholar and ClinicalTrials.gov was performed, to screen the studies conducted on managing fever using paracetamol and ibuprofen. Review of the selected articles based on the inclusion/exclusion criteria was performed by two independent researchers. The titles of selected publications were screened for relevance to the preset criteria followed by review of the s. Finally, the full-length articles were evaluated for the final selection of studies to be included. Outcomes of use of ibuprofen and paracetamol were estimated by analyzing selected case control and cohort studies. Overall, eleven observational studies were selected for the compilation of systematic review, based upon the preset inclusion/exclusion criteria. All studies included adult COVID-19 patients both male and female from different age groups. Paracetamol users were compared with ibuprofen users and no adverse effects of ibuprofen were found related to longevity of infection, complications, increased mortality rate and ventilation support requirement, when treating fever or pain caused by COVID-19. However, further studies and randomized control trials need to be conducted to assess and compare the effectiveness of these drugs to manage fever caused by coronavirus disease. © The Author(s) 2023.

Increased Levels of Inflammatory and Endothelial Biomarkers in Blood of Long COVID Patients Point to Thrombotic Endothelialitis.

The prevailing hypotheses for the persistent symptoms of Long COVID have been narrowed down to immune dysregulation and autoantibodies, widespread organ damage, viral persistence, and fibrinaloid microclots (entrapping numerous inflammatory molecules) together with platelet hyperactivation. Here we demonstrate significantly increased concentrations of von Willebrand factor (VWF), platelet factor 4 (PF4), serum amyloid A (SAA), α-2 antiplasmin (α-2AP), endothelial-leukocyte adhesion molecule 1 (E-selectin), and platelet endothelial cell adhesion molecule (PECAM-1) in the soluble part of the blood. It was noteworthy that the mean level of α-2 antiplasmin exceeded the upper limit of the laboratory reference range in Long COVID patients, and the other 5 were significantly elevated in Long COVID patients as compared to the controls. This is alarming if we take into consideration that a significant amount of the total burden of these inflammatory molecules has previously been shown to be entrapped inside fibrinolysis-resistant microclots (thus decreasing the apparent level of the soluble molecules). We conclude that presence of microclotting, together with relatively high levels of six biomarkers known to be key drivers of endothelial and clotting pathology, points to thrombotic endothelialitis as a key pathological process in Long COVID.

Analysis of Cardiovascular Complications during Delivery Admissions Associated with COVID-19

BACKGROUND: Persons with Coronavirus Disease 2019 (COVID-19) infection have an increased risk of pregnancy-related complications. However, data on acute cardiovascular complications during delivery admissions remain limited. OBJECTIVE(S): To determine whether birthing individuals with COVID-19 have an increased risk of acute peripartum cardiovascular complications during their delivery admission. METHOD(S): This population-based retrospective cohort study used the National Inpatient Sample (2020) by utilizing ICD-10 codes to identify delivery admissions with a diagnosis of COVID-19. A multivariable logistic regression model was developed to report an adjusted odds ratio for the association between COVID-19 and acute peripartum cardiovascular complications. RESULT(S): A total of 3,458,691 weighted delivery admissions were identified, of which 1.3% were among persons with COVID-19 (n=46,375). Persons with COVID-19 were younger (median 28 vs. 29 years, p<0.01) and had a higher prevalence of gestational diabetes mellitus (GDM), preterm births and Cesarean delivery (p<0.01). After adjustment for age, race/ethnicity, comorbidities, insurance, and income, COVID-19 remained an independent predictor of peripartum cardiovascular complications including preeclampsia (aOR 1.33 [1.29-1.37]), peripartum cardiomyopathy (aOR 2.09 [1.54-2,84]), acute coronary syndrome (ACS) (aOR 12.94 [8.85-18.90]), and cardiac arrhythmias (aOR 1.55 [1.45-1.67]) compared with no COVID-19. Likewise, the risk of in-hospital mortality, AKI, stroke, pulmonary edema, and VTE was higher with COVID-19. For resource utilization, cost of hospitalization ($5,374 vs. $4,837, p<0.01) was higher for deliveries among persons with COVID-19. CONCLUSION(S): Persons with COVID-19 had a higher risk of preeclampsia, peripartum cardiomyopathy, ACS, arrhythmias, in-hospital mortality, pulmonary edema, AKI, stroke, and VTE during delivery hospitalizations. This was associated with an increased cost of hospitalization. Keywords: COVID-19, Pregnancy, GDM, PCOS, Preeclampsia, CVD, Cardiovascular Disease Abbreviations: COVID-19: Coronavirus disease-2019, GDM: Gestational Diabetes Mellitus, PCOS: Polycystic Ovary Syndrome, National Inpatient Sample: NIS, AHRQ: Agency for Healthcare Research and Quality, HCUP: the Healthcare Cost and Utilization Project Funding and Conflicts of Interest Dr. Michos reports Advisory Board participation for Amgen, AstraZeneca, Amarin, Bayer, Boehringer Ingelheim, Esperion, Novartis, Novo Nordisk, and Pfizer. The remaining authors have nothing to disclose.Copyright © 2023

Evaluation of Factors Causing a Delay in Management and Hospital Stay in Diabetic Foot Amputation Patients: A Prospective Cohort Study

OBJECTIVE: To determine the rate of different amputation levels in diabetic foot patients and the incidence of repetitive foot surgeries and evaluate the factors causing a delay in hospital stay and amputation of patients. METHODOLOGY: This prospective cohort study was conducted in Dr. Ruth K.M. Pfau, Civil Hospital Karachi, Pakistan. The study selected 375 participants from the clinic's daily patient inflow from October 2021 to March 2022 using a non-probability consecutive sampling technique. Those who had a delay in hospital stay and amputation were further followed up from May-October 2022. The chi-square test and Kruskal Wallis test (p-value <0.05) were used to correlate the effect of the level of lower limb amputation and the cause of delay in amputation using SPSS version 24.0. RESULT(S): Total 246(65.60%) were males and 129(34.40%) were females. Toe amputation was the most commonly seen amputation in 173(46.1%) participants. About 168(44.8%) patients had some in-hospital delay stay during their treatment. Preoperative hurdles (Uncontrolled RBS, Osteomyelitis, etc.) were the most common factor causing an in-hospital delay in 92(24.5%) patients. The level of amputation performed was found to be statistically significant with factors causing a delay in hospital stay through chi-square (p=0.003*) and Kruskal Wallis test H (2) statistic= 13.3, df = 3, H (2), P=0.004*). CONCLUSION(S): Diabetic foot is a frequent cause of amputation globally, majorly in developing countries like Pakistan. On-time provision of treatment to these patients can decline the global amputation rate due to diabetic foot ulcers.Copyright © 2023 Syeda Anjala Tahir.

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes' Response against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error. © 2023 Tech Science Press. All rights reserved.

INSIGHT OF PANDEMIC COVID-19: DEVELOPMENTS AND CHALLENGES

This review focuses on the characteristics of coronavirus disease-19 (COVID-19) including virus structure, ecoepidemiology and pathophysiology, signs and symptoms in infected people, and data on virus pathogenicity, severity, and survivability in COVID-19 infected patients. The emphasis is on immunological reactions, diagnosis, prophylactic methods, and the zoonotic significance of COVID-19. The authors feel that the review's contents will be valuable to epidemiologists, virologists, public health officials, diagnosticians, laboratory workers, environmentalists, and socioeconomic experts. It has information on the many types of coronavirus variants, the disease situation in Pakistan and the WHO criteria for COVID-19 prevention is given. Moreover, lessons learned from the COVID-19 pandemic are also outlined.

A Quality Improvement Project to Improve the Efficiency of Pulmonary Nodule Service at a Tertiary Centre

Introduction: With the increasing detection of incidental pulmonary nodules (IPNs), there is a clinical need for a dedicated IPN service to ensure that growing PNs are managed in a timely manner. Pre COVID-19, our centre ran a virtual nodule service, delivered on an ad-hoc basis by the lung cancer physicians. We hypothesised that efficiency of the service would improve with a dedicated nodule team. We were awarded a pump priming grant by the Thames Valley Cancer Alliance to implement a nodule navigator run service. We report the initial outcomes of this project here. Objective(s): To evaluate the PN navigator service. Method(s): Retrospective data pre-service development was collected from patients presenting to the PN service between April and June 2022. The service was established in October 2022 and data from October and November 2022 collected. Student t-test was used to compare means. [Table presented] Results: 107 patients were included pre-service and 92 patients in the post-service development cohorts. Data for time to CT report and patient contact are summarised in Table 1. There was no reduction in mean time from CT scan date to CT report (Table 1;31vs 27;p=0.143) but a reduction was seen between CT report and patient contact (Table 1, 45 vs 20;p<0.001). Conclusion(s): This small cohort study shows an improvement in the time between CT scan and patient contact following the introduction of a dedicated PN service. This may reduce delays in the diagnosis of early-stage lung cancer. Whilst there was no significant difference between the CT scan date and CT report, these data highlight an area in the pathway that can improve. Further aims of the project are to collect patient satisfaction and IPN discharge. Disclosure: No significant relationships.Copyright © 2023 Elsevier B.V.

A Cybersecurity Evaluation Model (CSEM) for Indian SMEs Working in a Virtual Team Environment

During COVID-19 pandemic, there has been unprecedented increase in the number of employees working outside an organisations IT infrastructure due to the use of personal devices. The scale and sophistication of cyberattacks also continue to increase post-COVID-19 and it has become critical for SMEs (Small and Medium Sized Enterprises) to safeguard their information and IT assets. COVID19 proved to be a major catalyst for the adoption of digital approaches to remote working that many organisations did not previously believe to be feasible. The systems are becoming increasingly exposed to cyber-attacks as a result of remote access technology and cloud networks. The literature points to a gap in the existing knowledge to address the cybersecurity requirements for SMEs in India working in a virtual setup. The purpose of this paper is to develop a cybersecurity evaluation model (CSEM) that can be leveraged by SMEs which will eventually help them assess their cyber-risk portfolio. Based on the research project and the methodology used in the past for similar research, a quantitative approach will be chosen for this research. This research requires the researcher to roll out an online survey, which will enable the participants to evaluate cybersecurity risks by responding to the survey questionnaire. Analysing and implementing a CSEM will not only assist SMEs in identifying their strengths and weaknesses but will also include simple best practice guidelines for effectively plugging their cybersecurity flaws while working remotely. © 2022 IEEE.