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.

RECENT DEVELOPMENTS IN SOFT COMPUTING BASED TECHNIQUES FOR FEATURE SELECTION AND DISEASE CLASSIFICATION

Computational prediction of diseases is vital in medical research that contributes to computer-aided diagnostics and helps doctors and medical practitioners in critical decision-making for various diseases such as bacterial and viral kinds of disease, including COVID-19 of the current pandemic situation. Feature selection techniques function as a preprocessing phase for classification and prediction algorithms. For disease prediction, these features may be the patient's clinical profiles or genomic features such as gene expression profiles from microarray and read counts from RNA-Seq. The performance of a classifier depends primarily on the selected features. In addition, genomic features are too large in numbers, resulting in the curse of dimensionality problem. In the last few years, several feature selection algorithms have been developed to overcome the existing problems to get rid of eliminating chronic diseases, such as various cancers, Zika virus, Ebola virus, and the COVID-19 pandemic. In this review article, we systematically associate soft computing-based approaches for feature selection and disease prediction by applying three data types: patients' clinical profiles, microarray gene expression profiles, and RNA-Seq sample profiles. According to related work, when the discussion took place, the percentage of medical data types highlighted through pictorial representation and the respective ratio of percentages mentioned were 52%, 27%, 9% and 12% for clinical symptoms, gene expression, MRI-Image and other data types such as signal or text-based utilized, respectively. We also highlight the significant challenges and future directions in this research domain © 2023, Suranaree Journal of Science and Technology.All Rights Reserved.

Medicinal plants as COVID-19 remedy

COVID-19 (coronavirus) is an infectious disease which disturbs the modern world socially and economically. SARS-CoV-2, a causative agent of COVID-19, is a global pandemic. It is a fast-spreading disease which is largest ever pandemic in human history. The origin of this disease is still not known, and vaccine or medicine for the cure of infection has not been discovered so far. But various medicinal plants are used in different countries as a therapeutic treatment for COVID infection to help the immune system fight against COVID-19 disease. Various plant products contain high amount of vitamin C, boost up human immune system, and help to cure the disease. The medicinal and herbal plants act as effective therapeutic agents against coronavirus infection. The SARS-CoV-2 infection can also be relieved by a combined therapy of medicinal plants based on their properties. As COVID-19 can cause multiple organ disease, the use of natural products and medicinal plants may inhibit the SARS-CoV-2 life cycle linked proteins, i.e., papain-like or chymotrypsin-like proteases. In this article, different medicinal plants and herbs and their bioactive components that help in enhancing our immune system and play a role in fighting microbial infections as well as COVID-19 infection are discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Patterns of unconventional monetary policy spillovers during a systemic crisis

We examine whether the COVID-19 pandemic-induced systemic shocks cause a change in the dynamics of monetary policy spillovers among developed economies. Results from our analysis under the time-varying parameter vector autoregressive model indicate that: (i) variations in monetary policy actions are explained by monetary policy spillovers;(ii) shocks from the COVID-19 pandemic rocketed monetary policy spillovers;(iii) the Euro area and the US chiefly propagate monetary policy shocks to their counterpart developed economies;and (iv) New Zealand and Japan endure the highest monetary policy shocks. Our results evidence the need for synchronized monetary policy actions during systemic crises. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Current pandemic COVID-19 vaccine strategies and development: a comprehensive review

The coronavirus disease-19 pandemic with the characteristics of asymptomatic condition, long incubation period and poor treatment has influenced the entire globe. Coronaviruses are important emergent pathogens, specifically, the recently emerged sever acute respiratory syndrome coronavirus 2, the causative virus of the current COVID-19 pandemic. To mitigate the virus and curtail the infection risk, vaccines are the most hopeful solution. The protein structure and genome sequence of SARS-CoV-2 were processed and provided in record time;providing feasibility to the development of COVID-19 vaccines. In an unprecedented scientific and technological effort, vaccines against SARS-CoV-2 have been developed in less than one year. This review addresses the approaches adopted for SARS-CoV-2 vaccine development and the effectiveness of the currently approved vaccines.Copyright © 2023, Finlay Ediciones. All rights reserved.

Volatility spillovers during normal and high volatility states and their driving factors: A cross-country and cross-asset analysis

Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile-based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011–10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward-looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision-making process of risk managers and policymakers. © 2022 John Wiley & Sons Ltd.

Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data

Using 5-minute high-frequency data, we study realized volatility spillovers in major crypto-currencies, employing generalized forecast error variance decomposition. We also include COVID19 period observations and report time-varying and asymmetric connectedness across various cryptocurrencies using realized volatilities and semi-variances. Our study provides diverse connections after distinctly considering good-and bad volatilities, which is unique in the related literature. Bitcoin and Ethereum are central to the system and dominant transmitters of positive shocks, while Litecoin propagates negative shocks abundantly. Ripple and Stellar are the least connected currencies with others, whereas Cardano and EOS are isolated in the network. This feature makes these currencies suitable diversifiers in a portfolio with other cryptocurren-cies. Further, the majority of these connections are asymmetric in the long-and short-run. The time-varying and asymmetric nature of connections offers potentially unique opportunities for diversification and portfolios strategies. Total volatility connectedness is not only significantly enhanced but also changed in its nature during the COVID19 period. We observe no significant changes in results after the robustness check through varying lengths of the rolling-window. The findings are important to crypto investors and regulatory authorities for better diversification strategies and effective market oversight, respectively.

Sustainable financial dimensions of managing poverty in the era of the COVID-19 pandemic: A developing country perspective

The COVID-19 pandemic is dangerous to people’s lives and livelihoods, creating immediate obstacles for organizations that support impacted populations. This research concentrates on the consequences for local microfinance institutions in Pakistan, which is a well-developed sector that has pulled many households out of the poverty trap. Microfinance programs in Pakistan provide financial resources to vulnerable and deprived people to engage in income-generating practices on more favorable terms. As a result, this study addressed and assessed the financial dimensions of managing poverty reduction in rural Pakistan through the microfinance segment and its effectiveness on poverty-reduction programs in Pakistan during the COVID-19 pandemic. The primary data were collected through a questionnaire survey to determine the views of the households, beneficiaries, and non-beneficiaries on the outcome and efficacy of poverty-reduction programs during the pandemic to meet the study objectives. The Mann-Whitney U test of the non-parametric method and Cronbach’s alpha of the data reliability test have been applied for the empirical analysis. According to the non-parametric findings, programs, marital status, working women members, and resources such as land, livestock, business assets, shares, and loans have all been affected during the COVID-19 pandemic. Education, wages, gender, size, child dependency, and district variables are significant factors related to poverty, but they fell into second position during COVID-19. These findings suggests that the small loan system must be improved and made efficient during the pandemic. This could be a practical tool to maintain poor people’s current economic and poverty position. Copyright © 2022 Lu, Dai, Ali, Al-Faryan and Iqbal.

Pattern formation induced by fractional-order diffusive model of COVID-19

Differential equations with complex order fractional derivatives enable the regulation of complicated fractional systems. Within this scale, fractional calculus unfolds the fundamental mechanisms and multi-scale dynamic phenomena in biological tissues. It is viable that weakly nonlinear analysis represents a system that includes amplitude equations and the analysis that corresponds with it, which allows the prediction of the various patterns of parameter regimes likely to coexist in complicated dynamical and transient circumstances. A weakly nonlinear analysis creates a system comprising amplitude equations in dynamical contexts with fractional-order system characteristics, and its associated analyses are useful for predicting parameter regimes of several patterns that are expected to coexist. The development of patterns as a result of Turing instability in the homogeneous steady state is known to be a Turing process addressing unpredictability in numerous contexts. In a COVID-19 model, we investigate the Turing instability produced by fractional diffusion. To that purpose, positive equilibrium points are first specified, and then the Routh-Hurwitz criteria are used to assess the positive equilibrium point's stability. Local equilibrium points and stability analysis are employed to find the conditions for Turing instability. The amplitude equations near the Turing bifurcation point are deduced using weakly nonlinear analysis. After the application of amplitude equations, this structure has manifested highly rich dynamical properties. Regarding the amplitude equations, the dynamic analysis determines the conditions for the development of patterns such as spot, hexagon, stripe, and mixed patterns. Moreover, the theoretical effects are confirmed using numerical simulations. Within this context, this analysis, which looks into the system's dynamical behavior and the bifurcation point centered on the death rate, will serve as a leverage for further studies in different disciplines concerning COVID-19 model through the lenses of distinct viewpoints. Based on modeling as regards complex and heterogeneous materials, fractional system ensures the formation of patterns by identifying the specific and required significant attributes of complexity that convey information in terms of dynamical behavior. As a result of the analyses which reveal the highly complex connection between COVID-19 and fractional-order diffusion, the Turing bifurcation point and weakly nonlinear analysis used in the fractional-order dynamics discussed in this study are critically important on a quantitative basis owing to the fact that the results can be applied and extended to a variety of statistical, physical, engineering, biological and other related further models. © 2022 Elsevier Inc. All rights reserved.

Evolution of dry powder inhaled colistimethate sodium (Colobreathe®) in a large adult cystic fibrosis centre

Objectives: Colobreathe® is a dry powder formulation of colistimethate sodium developed to reduce treatment burden for people with cystic fibrosis. In our centre initial experience revealed 45% discontinued this therapy within 12 months, of which 83% were due to tolerance or device issues. Capsuleswere reformulated in 2017 to address some of these issues. We aimed to assess the prescription rates of Colobreathe® over 3 time periods to assess whether prescription practices and tolerance changed. Methods: A retrospective review of antibiotic challenges in the one-year periods from Dec 2013, 2016 and 2020 was conducted. Key end points included tolerance of test dose and continued use at 1 and 3 months. The proportion of antibiotic challenges that were Colobreathe® at each time point was compared. Results: Therewas a significant difference in the proportion of all antibiotic challenges whichwere for Colobreathe® across the 3 periods (2013–65/186 (35%), 2016–8/136 (6%), 2020–22/55 (40%), p < 0.001). The majority of patients at all 3 time points had previously nebulised colistimethate sodium (98%, 88% and 100%, respectively). All patients had a successful test dose during each time period. Therewas no difference in the proportion of patients who commenced long-term prescription following a 1-month review at the 3 time periods (75%, 75% and 73% respectively, p = 0.97). Of those who received a long-term prescription, continuation rates were similar at 3 months (82%, 100% and 93%, respectively). Conclusions: There was a marked reduction in inhaled antibiotic challenges in 2020, likely due to COVID. There was a significant change in prescription of Colobreathe® over the 3 time frames. Colobreathe®waswell tolerated at initial challenge and continuation rates after a month appear to be consistent. A number of factors likely influenced prescription practices, including early experience and potentially changing airway physiology following CFTR modulation introduction.

COVID-19, Irregular Migration and Asymmetrical Dependency

This study explores the process of irregular migration that drives people to opt for illegal channels to migrate. The study further examines the impact of the COVID-19 pandemic on the socio-economic vulnerabilities of irregular migrant workers. We find that due to COVID-19, irregular migrants suffered job losses, with only a few cases of job restoration. Their predicament is compounded given their questionable legal status, economic vulnerabilities, the stance of governments of the host and origin countries, vulnerability to poverty, and resort only to social capital as social security. We suggest that the governments should intervene to facilitate irregular migrants during pandemics. © 2021

Spotlight on the Adolescent's Mental Health: Distress and Coping amid the COVID-19 Pandemic

Background: COVID-19 pandemic globally challenged the healthcare sector as well as posed a serious threat to mental health among both young and adults rendering people with a sense of uncertainty and loss. Objective Aim: To assess the psychological burden among the adolescent population during the pandemic and lockdown. Methodology: A cross sectional study was moderated by the research team at the Department of Psychiatry and Behavioral Sciences, Jinnah Postgraduate Medical Center between April 2020 to October 2021. All individuals between the ages of 13 to 17 years were included. The proforma was circulated among residents of the province of Sindh that assessed the emotional symptoms, conduct problems, hyperactivity-inattention, peer relationship problems, and prosocial behaviors among participants. Results: The mean SDQ score was 24.97 with a standard deviation of 6.62. The mean scores for emotional symptoms, conduct problem, hyperactivity, peer problem, and prosocial Scale were 5.47, 6.82, 5.85, 6.82, and 3.02, respectively. A significant relationship was revealed between mental health stability and witnessing a death of a known person due to COVID- 19 infection (p=0.003). Furthermore, the study found that Sindhi individuals had significantly higher scores as compared to other ethnic groups (p=0.002). Conclusion: The young population is as equally stressed as adults and may suffer from substantial anxiety during the pandemic. Therefore, parents should be encouraged to create an atmosphere of support and goodwill.

Do you know when the inhaler is empty?

BACKGROUND: Medication review is recommended at asthma appointments. The presence of propellant in the metered dose inhalers (MDIs) makes it challenging to identify when the inhaler is empty. The COVID-19 pandemic has resulted in move towards more virtual monitoring of care. We aimed to evaluate if patients identify when the inhaler is empty and the method of inhaler disposal. METHODS: Prospective, multicentre quality improvement project. Data collected from children with asthma and other respiratory conditions. OUTCOME MEASURES: Children/carers attending hospital were asked how they identify an empty salbutamol inhaler; dose counters in the preventer inhalers and disposal practices were reviewed. RESULTS: 157 patients recruited. 125 (73.5%) patients deemed an empty inhaler as either full/partially full. 12 of 66 (18.2%) preventer inhalers with a dose counter were empty. 83% disposed their inhalers in a dustbin. CONCLUSIONS: Patients cannot reliably identify when their MDI is empty. There is an urgent need for improving inhaler technology and providing appropriate guidance on how to identify when an MDI is empty.

The evolution of community peer support values: reflections from three UK mental health project teams The McPin peer support evaluation writing collaborative

Objective: To explore emergent values for community-based peer support in three projects and use of peer research methodology. Background: Peer support refers to the support people with shared lived experiences provide to each other. Its roots are in the civil rights movement, providing alternatives to clinical treatments. This method of support is delivered in different settings, with varying degrees of structure. In this paper, it includes shared experience of mental health issues. Methods: We reviewed interview data from two evaluations and one development project - mental health (n = 69), women-only (n = 40), and maternal mental health (n = 24), respectively. Each project used peer research methods. Peer support values from each project were compared, along with reflections from mostly peer researchers who worked on them (n = 11). Results: Six peer support values emerged and were found to be identifiable and applicable in different contexts. Decisions on facilitation and leadership varied across projects and generated some concerns over professionalisation, including non-peer leadership. Frameworks were viewed as broadly useful, but peer support is heterogenous, and peer researchers were concerned about over-rigid application of guidance. Discussion: We propose caution applying frameworks for peer support. Values must remain flexible and peer-led, evolving in new contexts such as COVID-19. Evaluators have a responsibility to consider any potentially negative consequences of their work and mitigate them. This means ensuring research outputs are useful to the peer support community, and knowledge production is based upon methodologies, such as peer research, that complement and are consistent with the values of peer support itself.

Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak

The cryptocurrency markets are perceived as being dominated by Bitcoin leading the overall system dynamics. Although the previous empirical evidence points towards strong connections among selected cryptocurrencies or, from the other side, weak dependence between Bitcoin and traditional financial assets, a focused study on the dynamics of return and volatility connectedness among a wider range of cryptocurrencies is lacking, and more so, one directed towards the very first actual critical period of the global economy coinciding with relevant crypto-markets. Using data for the 10 most capitalized cryptocurrencies between 1st October 2017 and 5th January 2021, we examine how cryptocurrencies interact and whether they have a clear leader, with a special focus on differences with respect to investment horizons and how the relationship structure evolves in time. We uncover a structural change in the connectedness evolving in 2020 as the market restructures in reaction to the unprecedented monetary injections as a counter to the COVID-19-induced economic standstill. The structural change is shown not only for cryptocurrencies considered separately but also when we jointly examine them with traditional assets. © 2022 Elsevier B.V.

Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19

In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period from September 23, 2008, to June 1, 2020. Using high-frequency (5-min) price data on commodity futures, we compute daily realized volatility and then apply quantile-based connectedness measures. The results show that the connectedness measures estimated at the lower and upper quantiles are much higher than those estimated at the median, implying that realized volatility shocks circulate more intensely during extreme events relative to normal periods, which endangers the stability of the system of volatility connectedness under extreme events such as the COVID19 outbreak. There is evidence of a strong asymmetry between the behaviour of volatility spillovers in lower and upper quantiles, given that the connectedness measures estimated at the upper quantile are the highest. The main results are robust to rolling window size and other alternative choices. Our analyses matter to investors and policy makers who are concerned with the stability of commodity markets.

Coronavirus Disease Predictor: An RNA-Seq based pipeline for dimension reduction and prediction of COVID-19

SARS CoV-2, the novel coronavirus behind the COVID-19 infection, has caused destruction around the world with human life, detecting a range of complexity which has knocked medical care specialists to investigate new innovative solutions and diagnosis strategies. The soft computing-based approach has assumed a significant role in resolving complex issues, and numerous societies have been shifted to implement and convert these innovations in response to the encounters created by the COVID-19 pandemic. To perform genome-wide association studies using RNA-Seq of COVID-19 and identify gene biomarkers, classification, and prediction using soft computing techniques of Coronavirus disease studies to fight this emergency pandemic in the epidemiological domain, and disease prognosis. The RNA-Seq profiles of both healthy and COVID-19 positive patients’ samples were considered. We have proposed an integrated pipeline from bioinformatics in-silico phase for -omic profile data processing to dimension reduction using various prominent techniques such as formal concept analysis and principal component analysis followed by machine learning phase for prediction of the disease. In this experimental research, we have applied different eminent machine learning techniques to implement an effective integrated model using Classifier Subset Evaluator (CSE) followed by principal component analysis (PCA) for dimension reduction to select the highly significant features and then to do the classification and prediction of Coronavirus disease, different eminent classifiers have been applied on the selected features. In this analysis, the Hoeffding Tree model found the topmost performance classifier with a classification accuracy of 99.21% as well as sensitivity and specificity of 99% and 100% respectively. © 2021 Institute of Physics Publishing. All rights reserved.

Does green finance matter for sustainable entrepreneurship and environmental corporate social responsibility during COVID-19?

Purpose The discourse aimed to investigate green finance practices under the assumptions of several notable climate advisors and speculators in Asia and particularly in Southeast Asia. The study intrigues by considering financial specialists to vent government spending on green restoration plans leading toward green bankable venture openings for the public and private sector. This section distinguishes a few of the green fund components and approaches that can be joined by national and neighborhood governments, essentially in Southeast Asia, into their post-COVID-19 techniques, but are too valuable inputs for domestic commercial banks and private corporates. Design/methodology/approach It can be defined as a functional type for Cobb Douglas development. ARDL technology is a way of calculating complex forces at the classification level at long-term and short-term stages. This ARDL approach has many advantages and can be implemented when incorporated in level I (0) and level I first (1) with the original variable. Still, it offers robust ability to the outcomes and standardizes the lag, considering the number and sample size used. Pooled mean group (PMG) method is becoming a convenient technique for monitoring data over the period and a good approach for energy impact panels - growth ties for creating links between energy emissions and environmental sustainability and businesses in the nation. Findings There is a positive partnership between creativity and a sustainable world. Corporations are recommended to uphold the principles of CSR in the development process by introducing environmentally friendly advanced technologies. The main objectives of corporate social responsibility (CSR) are economic growth, environmental sustainability and social justice. Several programs have been established to expand businesses' responsibilities to improve their confessions in sustainable growth. SMEs are a primary source of production of innovative products and technologies. The key concerns of stakeholders and politicians in the new competitive business climate are the protection of environmental sustainability and social responsibility, recognizing factors driving economic development for SMEs. Originality/value During the COVID-19 era, the prime responsibility of pandemic confronting governments is to spend on help activities (that have been started in earlier phase) and recovery endeavors (yet to start in the situation). Therefore, the governments may devise policies to pool resources from commercial, private, public-private partnerships and other capital market sources. With rising hazard recognitions particularly emerging from at-threat income projections, governments ought to make the correct mechanisms and instruments that can perform this catalytic part of derisking and drawing in such capital. This too can be an opportunity for governments to enhance and execute such financial instruments that offer assistance, quicken their commitments to climate alter beneath the Paris Agreement and the sustainable development goals (SDGs), and thus "build back better" is being progressively voiced over the world.