Clinician satisfaction and experience using teleconsultation during the COVID-19 pandemic in Pakistan: A cross-sectional study

Aims: During the pandemic of COVID-19, the sudden change in traditional health-care providing systems, clinicians experience some positive and negative aspects of the approach. This study evaluates the clinician's satisfaction and experience with the use of teleconsultation provided during the pandemic of novel coronavirus and their willingness to continue telehealth after the pandemic. Materials and Methods: A cross-sectional survey was conducted online during the peak pandemic of COVID-19 in Pakistan through Google Forms questionnaire from 115 health consultants on different disciplines and recruited through social media. The questionnaire contains 15 questions regarding clinician's satisfaction, quality of treatment, and intention to continue providing telehealth services after the pandemic. Descriptive and inferential statistics were obtained by analyzing the data using SPSS software version 20, USA. Results: One hundred and fifteen consultants, 28 males and 87 females participated in the study, in which 62% were found to have an average and 34% at a high level of satisfaction. The Kruskal-Wallis test showed a significant difference among different medical specialists in the continuation of telehealth services after the pandemic of COVID-19 (P = 0.003) and its recommendation to friends and family (P = 0.02) with high mean rank in endocrinologist and dermatologist. Conclusions: A great number of participants reported a good response for the continuation in telemedicine services in their daily routine even after the pandemic situation. However, there is an urgent need to find the solution for the difficulties and drawbacks faced by health-care providers.

COVID-19- A Challenge for Urology Subspecialty in an overwhelmed public sector hospital: A Descriptive Review of Urology Department, Lady Reading Hospital

Background: COVID-19 has brought unprecedented changes in every aspect of life throughout the world including the healthcare delivery system. After a grinding halt in surgical practice due to this pandemic, the conventional protocols needed a thorough overhaul before kick-starting formal services. This study discusses ways and procedure changes adopted at the Urology department to navigate this crisis and extend adequate urological care to patients at the same time. Aim(s): To share our experience of patient management in the era of the COVID-19 pandemic. Method(s): It's a descriptive review article based on patient management protocols and clinical audit in the era of COVID-19 pandemic at the Departmentof Urology, MTI, Lady Reading Hospital from 20th Marchto 20th June 2020. Clinical implication the benefit of this study is how to organize things and continue health care provision in a deadly pandemic. Furthermore, it will set a precedence that how to cope with such a pandemic in the future. Conclusion(s): All surgical patients should be screened for COVID-19, with preference given to PCR tests. All elective surgeries should be put on hold as a result of the limited availability of ventilators, manpower, and hospital beds. Only semi-elective, lifesaving and oncologic surgeries that cannot be delayed should be done with full PPEs provided to every personnel frequenting operating theaters during the procedure. Furthermore, more efforts are needed to lift the infrastructure of hospitals and make them capable to face problems of such proportions in the future.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

New Control Chart Based On Neutrosophic Maxwell Distribution with Decision Making Applications

The neutrosophic approach is a potential area to provide a novel framework for dealing with uncertain data. This study aims to introduce the neutrosophic Maxwell distribution (M̃D) for dealing with imprecise data. The proposed notions are presented in such a manner that the proposed model may be used in a variety of circumstances involving indeterminate, ambiguous, and fuzzy data. The suggested distribution is particularly useful in statistical process control (SPC) for processing uncertain values in data collection. The existing formation of VSQ-chart is incapable of addressing uncertainty on the quality variables being investigated. The notion of neutrosophic VSQchart (Ṽ SQ) is developed based on suggested neutrosophic distribution. The parameters of the suggested Ṽ SQ-chart and other performance indicators, such as neutrosophic power curve (P̃C), neutrosophic characteristic curve (C̃C) and neutrosophic run length (R̃L) are established. The performance of the Ṽ SQ-chart under uncertain environment is also compared to the performance of the conventional model. The comparative findings depict that the proposed Ṽ SQ-chart outperforms in consideration of neutrosophic indicators. Finally, the implementation procedure for real data on the COVID-19 incubation period is explored to support the theoretical part of the proposed model © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

Authentication of Tinospora cordifolia derived herbal supplements using high resolution mass spectrometry-based metabolomics approach – A pilot study

Tinospora cordifolia herbal supplements have recently gained prominence due to their promising immunomodulatory and anti-viral effects against SARS-CoV-2. Mislabelling or diluting Tinospora supplements for profit may harm public health. Thus, validating the label claim of these supplements in markets is critical. This study investigated how high resolution mass spectrometry-based metabolomics and chemometrics can be used to distinguish Tinospora cordifolia from two other closely related species (T. crispa and T. sinensis). The Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) and PLS-DA based chemometric models predicted the species identity of Tinospora with 94.44% accuracy. These classification models were trained using 54 T. cordifolia, 21 T. crispa, and 21 T. sinensis samples. We identified 7 biomarkers, including corydine, malabarolide, ecdysterone, and reticuline, which discriminated Tinospora cordifolia from the two other species. The label claim of 25 commercial Tinospora samples collected from different parts of India was verified based on the relative abundance of the biomarker compounds, of which 20 were found authentic. The relative abundance of biomarkers significantly varied in the 5 suspicious market samples. This pilot study demonstrates a robust metabolomic approach for authenticating Tinospora species, which can further be used in other herbal matrices for product authentication and securing quality. © 2023 Elsevier B.V.

Assessment of Vitamin D Level as a Risk Factor and Illness Severity Indicator in COVID 19 Cases

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-COV2) viruses using angiotensin-converting enzyme 2 (ACE2) receptors, to enter human cells, these receptors are highly expressed in the lung alveolar cells, vascular endothelium, cardiac myocytes, and other cells. Inadequate vitamin D levels in the blood have been linked to a higher risk of COVID-19 severity. Objective: To determine the association between Vitamin D level and severity of COVID-19 infection. Materials and Method: A cross sectional study was conducted at Thumbay Hospital, Ajman, UAE. Enrolled 70 COVID-19 positive hospitalized patients with age group ≥ 18 years old of both genders. Patients taking vitamin D supplements were excluded from the study. The biochemical analysis for the collected blood samples was performed on the automated analyzer and assessed for significance analysis. Result: There is a statistically significant correlation between Vitamin D levels and disease severity (p < 0.05) as determined by Pearson's Chi-square test. Independent t-test shown that there is a statistically significant difference with regards to gender, age groups, and co morbidity (p < 0.05). Pearson's correlation revealed a moderate, positive correlation between Vitamin D levels and the severity of COVID-19 infection, which was statistically significant. Conclusion: Vitamin D levels affect COVID-19 severity, with more severe cases showing vitamin D levels lower than normal when compared to severe cases with sufficient vitamin D levels. Furthermore, blood vitamin D levels are linked to age groups in COVID-19 patients. Vitamin D insufficiency and deficiency, on the other hand, was not linked to a higher risk of death prognosis, and co morbidities. © Sabir SM, Ismail MK, Ibrahim EH, Khan ZA.

Guillain-Barre syndrome: An autoimmune disorder post-COVID-19 vaccination?

SARS-CoV-2 causes Coronavirus Disease 2019 (COVID-19), an infectious condition that can present none or one or more of these symptoms: fever, cough, headache, sore throat, loss of taste and smell, aches, fatigue and musculoskeletal pain. For the prevention of COVID-19, there are vaccines available including those developed by Pfizer, Moderna, Sinovac, Janssen, and AstraZeneca. Recent evidence has shown that some COVID-19-vaccinated individuals can occasionally develop as a potential side effect Guillain-Barre syndrome (GBS), a severe neurological autoimmune condition in which the immune response against the peripheral nerve system (PNS) can result in significant morbidity. GBS had been linked previously to several viral or bacterial infections, and the finding of GBS after vaccination with certain COVID-19, while rare, should alert medical practitioners for an early diagnosis and targeted treatment. Here we review five cases of GBS that developed in different countries after COVID-19 vaccination.Copyright © 2021

On Using Piecewise Fractional Differential Operator to Study a Dynamical System

This research work is devoted to undertaking a dynamical system representing SARS-CoV-19 disease under the concept of piecewise fractional-order derivative using the Caputo concept since long-memory and short-memory terms are not well explained by ordinary fractional differential equations. It has been found that for such disruption, piecewise operators of fractional derivatives have been found useful in many cases. Therefore, we study a compartmental model of susceptible and infected individuals under the concept of piecewise derivative. We establish the existence theory of the considered model by using some Banach and Schauder fixed-point theorems. Keeping the importance of stability, a pertinent result related to the said area is also developed. The said concept of stability is based on the concept given by Ulam and Hyers. Further, to derive the numerical results, we use the Euler method to develop a numerical scheme for the considered model. Using real available data, we have presented various graphical presentations of two compartments against different fractional orders and various values of isolation parameters. The crossover behaviors in the dynamics can be clearly observed, which is explained by the piecewise operators, not the usual fractional-order derivative. © 2023 by the authors.

Phages and SARS-CoV-2

The emerging human pathogenic viruses, including the recently emerged severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), have markedly affected the human health and have become a challenge for researchers worldwide. Antibiotic therapy and existing vaccines have reduced the pandemic burden to some extent. However, there is still need for efficient treatment, vaccination, and antiviral agents to control the pandemic. This chapter illustrates the role of bacteriophage in bacterial infections, SARS-COV-2 infected patients, biological activities of phages, phage display method, phages as potential inducers of antiviral immunity, phage-based vaccines, CRISPR and phage-based SARS-CoV-2 vaccines, and possible advantages of phage-based vaccines. It is concluded that phages have considerable breadth in the SARS-CoV-2 pandemic and offer many substantial advantages, such as clearing respiratory bacterial infections, which significantly reduce the burden of mortalities. Phage plays a vital role in triggering antiviral immunity by inducing cytokines such as IFN-α and IL-12. It suggests the role in driving antiviral immunity, triggering TLR3-dependent pattern recognition receptors, inhibiting TNF-driving type I IFN, inducing antiviral immunity through upregulation of the expression of defensin in IL-2, and encouraging a marked upregulation of gene hBD2 that induces virucidal effects, thus playing a key role in anti-SARS-COV-2 immunity. Moreover, phages have been presented as an alternative universal adjuvant-free nano-vaccine platform in which single-phage scaffolds are used to incorporate multiple targets. © 2023 Elsevier Inc. All rights reserved.

Natural products and SARS-CoV-2

Natural products have been proven to be the source of many antiviral drugs in the past. History has a bunch of natural products used as traditional medicine, therapies, mixtures, and oils. However, there are many bioactive natural products that need to be evaluated against severe acute respiratory syndrome (SARS-CoV-2) to curb the ongoing pandemic. Several plants and fungal-derived natural products are extensively reported with antiviral activities against SARS-CoV-2. In vitro, preliminary study assays and computational studies revealed several antiviral drugs from natural fungal compounds, including cordycepin isolated from Cordyceps militaris fungi. Polyphenolic compounds isolated from the Broussonetia papyrifera plant showed promising antiviral activity against SARS CoV-2 in in silico studies. Two alkaloid compounds, 10-hydroxyusambarensine and cryptoquindoline isolated from African medicinal plants, inhibited the main protease (Mpro) of SARS CoV-2. At the start of the COVID-19 pandemic, FDA approved the emergency use of chloroquine against SARS CoV-2;chloroquine is a derivative of alkaloid. The development of modern technologies has streamlined the discovery of new drugs from natural products. Gas chromatography–mass spectrometry, infrared radiation, nuclear magnetic resonance, high-performance thin-layer chromatography, and high-performance liquid chromatography and other high output technologies should be available for the structural interpretation and distinguishability of prudent lead molecules © 2023 Elsevier Inc. All rights reserved.

Computational Study Reveals the Inhibitory Effects of Chemical Constituents from Azadirachta indica (Indian Neem) Against Delta and Omicron Variants of SARS-CoV-2

Background: The newly emerged delta and omicron variants of severe acute respiratory syn-drome coronavirus (SARS-CoV-2) have affected millions of individuals globally with increased transmis-sible and infectivity rates. Although, numerous vaccines are available or under clinical trials to combat the SARS-CoV-2 and its variant, still, a therapeutic agent is awaited. Objective(s): The present work is focused on rigorous screening of chemical constituents of Azadirachta indica (A. indica) against delta and omicron variants of SARS-CoV-2 via inhibition of S-glycoprotein. Method(s): Total, 10 compounds of A. indica were subjected to molecular docking and pharmacophore modeling studies against the S-glycoprotein of delta and omicron variants of SARS-CoV-2. Furthermore, homology modeling was performed for omicron S-glycoprotein with the help of SWISS-MODEL and aligned by PyMOL software. Later on, the residues of protein were verified in the allowed region via Ramachandran plot. In addition, our docking results have also been validated by MMGBSA binding free energy calculations. Result(s): Our computed study demonstrated that nimbolinin B12-methyl ether and nimbidinin showed promising docking scores (>-6.0) as compared to docking scores (< 6.0) of reference drug 'camostat' against S-glycoproteins of both delta and omicron variants. Redocking by using MMGBSA calculation also reveals that both these compounds can effectively bind within the pockets of said protein receptors Conclusion(s): Nimbolinin B12-methyl ether and nimbidinin have potent anti-SARS-CoV activity against delta and omicron variants and thus, A. indica might be a useful source for developing novel anti-SARS-CoV-2 therapeutic agents.Copyright © 2022 Bentham Science Publishers.

Evaluation of Academic Procrastination Tendencies in Sophomore Medical Students

This paper describes the effectiveness of learning analytics on various academic endeavors. Two scenarios are discussed. The first one considers the academic performance while the second one considers diversity-related issues on the studies in order to ensure the evaluation of online classes during COVID-19 pandemic. Though the impact of learning analytics in academic decision making is vast, nevertheless, it can be used to improve learning environment and address key issues as experienced by new entrants in a medical school. The present work details the key academic and non-academic factors and suggests some strategies to minimize the impact of academic procrastination and hence improve the retention rate of sophomore medical students. © 2022 IEEE.

Performance evaluation of CNN architectures for COVID-19 detection from X-ray images

Early detection of the COVID-19 infection is the key to avoiding fatalities. Chest radiography has proven to be an effective and low-cost solution for detecting the virus. It is important to evaluate the potential of deep learning models for COVID-19 detection from the x-ray images for quick and early detection of COVID-19 with high accuracy. We conducted a study that evaluates the potential and performance of various Convolutional Neural Networks (CNN) architectures for detecting the COVID-19 on a dataset consisting of 5902 chest X-ray images having 2276 instances of X-ray images of COVID-19 patients and 3626 images of healthy and non-COVID-19 pneumonia X-rays. The performance of the models is assessed using metrics like accuracy, specificity, sensitivity, F1 Score, ROC curve, etc. The results suggest that the DenseNet-121 model proved to be the better choice among evaluated architectures for COVID-19 detection from X-ray images in terms of overall performance with an accuracy of 98.2%, sensitivity of 97.6%, and specificity of 98.4%. We conclude that there is a need for further evaluation of the CNN architectures on large, real-world, and diverse datasets for obtaining generalizable results for a reliable diagnosis.Copyright © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Soft computing techniques for forecasting of COVID-19 in Pakistan

Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reasonable estimates over the recursive Bayesian updates. Kalman Filters, used for controlling epidemics, are valuable in knowing contagious infections. Artificial Neural Networks (ANN) have generally been used for classification and forecasting problems. ANN models show an essential role in several successful applications of neural networks and are commonly used in economic and business studies. Long short-term memory (LSTM) model is one of the most popular technique used in time series analysis. This paper aims to forecast COVID-19 on the basis of ANN, KF, LSTM and SVM methods. We applied ANN, KF, LSTM and SVM for the COVID-19 data in Pakistan to find the number of deaths, confirm cases, and cases of recovery. The three methods were used for prediction, and the results showed the performance of LSTM to be better than that of ANN and KF method. ANN, KF, LSTM and SVM endorsed the COVID-19 data in closely all three scenarios. LSTM, ANN and KF followed the fluctuations of the original data and made close COVID-19 predictions. The results of the three methods helped significantly in the decision-making direction for short term strategies and in the control of the COVID-19 outbreak. © 2022 Faculty of Engineering, Alexandria University

Analysing the impact of COVID-19 pandemic on the psychological health of people using fuzzy MCDM methods

Recently, a large portion of the world's population has experienced an unprecedented devastating effect of the COVID-19 pandemic. At the time of its outbreak, not much was known about this disease and therefore, quarantine and social distancing were the only ways suggested to prevent its spread among humans. Although the current situation is much better than before however, strict social distancing norms as well as frequent long-lasting lockdowns with stringent guidelines and actions to control the spread in the early days have affected the physical and psychological health of the people. Consequently, this study was carried out to attain the following major objectives: (i) to identify the potential psychological problems/factors that might have been caused due to COVID-19 led social distancing and lockdowns, and (ii) to determine the ranks of the identified psychological factors to reflect their degree of criticality. The first objective was achieved by gathering information about the potential psychological factors from the experts. Data, in terms of linguistic variables, was collected from the experts and analyzed using two fuzzy-based multi-criteria decision-making (MCDM) methods i.e. Fuzzy Best Worst Method (F-BWM) and Fuzzy TOPSIS (F-TOPSIS) which led to the accomplishment of the second objective. The results of this study revealed that anxiety, stress, panic attacks, frustration, and insomnia were the top five critical psychological factors that might have affected people due to this pandemic. Consistency of the results was ensured by comparing the obtained ranks with the ranks found using the Fuzzy WSM and Fuzzy MABAC methods. In addition, the robustness of the results was ascertained by conducting the sensitivity analysis. Based on the findings of the study, the identified factors were categorized into most, average, and least critical psychological factors. This research might help the relevant authorities to understand the extent of the seriousness of the various psychological factors caused by this pandemic, so that an effective strategy may be developed for better management, control, and safety. © 2022 The Authors

Challenges of Online Learning Environment Faced by Undergraduate Medical Students during Covid 19 Pandemic

Objective: This study aimed to define the challenges faced by medical students rotating in the orthopedics department and their suggestions regarding improvement during covid-19 pandemic. Study Design: A mixed method cross sectional study design. Place and Duration of Study: It was conducted on 4th and 5th year MBBS students at Shifa college of Medicine with clerkship rotation in the department of orthopedics from 16th March 2020 to 23rd August 2021. Materials and Methods: Students were enquired about their comfort levels while using the internet and computer for online sessions. Data was collected through an online questionnaire and analyzed using Google forms. Frequencies, percentages, and standard deviations were calculated for qualitative variables. Results: Out of 147 study participants, 64(43.4%) students strongly agreed that they had no difficulty and were extremely comfortable using internet and computer during covid-19 pandemic. Eighty-five (58%) students used online available reading material shared on Google classrooms and what's app groups. While only 23(16%) agreed to concentrate during online sessions. One hundred and eighteen (80%) agreed with a lesser desire to study for online classes as compared to on campus. Major problems faced by the students during the pandemic included very limited patient centered learning, limited hands-on experience, less interactive sessions, problems with internet connections, technology handling and class timing issues due to time zone differences. Conclusion: We conclude that our students faced lot of challenges during Covid-19 pandemic including internet issues, lack of awareness of technology, distractions because of family, siblings and homely environment and lack of conducive learning environment like learning at bedside. Flexible class timings, multiple breaks, recorded lectures and online interaction of real patients can improve online clinical learning. © 2022 by the Author(s).

Attention-based Automated Chest CT Image Segmentation Method of COVID-19 Lung Infection

According to the World Health Organization, Artificial Intelligence (AI) technology may assist in COVID-19 management. However, existing image segmentation using AI suffers from a lack of accuracy and explainability, which prevents its adoption in actual clinical practice. In this paper, we investigated an attention-based image segmentation method for COVID-19 CT imaging with enhanced interpretation capabilities. Specifically, we developed U-Net architecture-based for segmentation with attention coefficients to produce a salient feature map. We use the DICE score and accuracy to perform a comprehensive model evaluation. We compared to other well-known methods such as Light U-Net, COPLE-Net, and Res U-Net and demonstrated that attention U-Net is superior for COVID-19 segmentation tasks in terms of performance and explainability. We also developed the tool as a web-application with a graphic user interface with the goal to translate this AI-driven clinical decision-support system for real-world clinical use. © 2022 IEEE.

iWHELD, A Novel Digital Non-Pharmacological Intervention with Live Virtual Coaching Reduces Antipsychotic Use in Nursing Home Residents with Dementia Compared to Usual Care in a 16-week RCT

Background: Implementation of evidence-based training into real world practice in nursing home settings is a major challenge. Digital approaches provide real potential to addressing the barriers, particularly over the difficult period of the COVID-19 pandemic. Method(s): iWHELD is a first-of-its-kind digital programme evolving the principles of the WHELD intervention combining person centred care, social interaction, and antipsychotic review, combining virtual coaching with a digital resource for nursing homes. The intervention utilising a Dementia Champion model supported by live virtual coaching set within a digital resource hub and peer networking platform was compared to usual care in a 16-week randomised control cluster study of 741 people with dementia across 149 nursing homes in the UK. The initial outcomes evaluated are the use of antipsychotic drugs and neuropsychiatric symptoms (using the Neuropsychiatric Inventory NH). Result(s): 36/72 (53%) of nursing homes allocated to the active treatment arm engaged successfully with the digital intervention, with 563 residents completing the treatment period. The average age of residents was 84.5 years, 71% were female, and there were no significant differences between treatment arms. There was a significant reduction in antipsychotic use in the digital iWHELD treatment group from 49% to 31% compared to no change in the group receiving usual care (p = 0.046). Initial analysis of neuropsychiatric symptoms indicates a non-significant improvement in the intervention group compared to those receiving usual care. Conclusion(s): The current RCT using an online programme with live virtual coaching delivered through a Dementia Champion achieved better than 50% engagement, which compares favourably with previous studies of digital interventions in other therapeutic areas. The intervention also achieved significant reductions in antipsychotic use without any worsening of neuropsychiatric symptoms, and provides an important potential approach to safely reducing the rise in antipsychotic use in nursing home residents with dementia that has become a major challenge during the COVID-19 pandemic. Copyright © 2022 the Alzheimer's Association.