Evolution-guided large language model is a predictor of virus mutation trends (preprint)

Emerging viral infections, especially the global pandemic COVID-19, have had catastrophic impacts on public health worldwide. The culprit of this pandemic, SARS-CoV-2, continues to evolve, giving rise to numerous sublineages with distinct characteristics. The traditional post-hoc wet-lab approach is lagging behind, and it cannot quickly predict the evolutionary trends of the virus while consuming high costs. Capturing the evolutionary drivers of virus and predicting potential high-risk mutations has become an urgent and critical problem to address. To tackle this challenge, we introduce ProtFound-V, an evolution-inspired deeplearning framework designed to explore the mutational trajectory of virus. Take SARS-CoV-2 as an example, ProtFound-V accurately identifies the evolutionary advantage of Omicron and proposes evolutionary trends consistent with wetlab experiments through in silico deep mutational scanning. This showcases the potential of deep learning predictions to replace traditional wet-lab experimental measurements. With the evolution-guided large language model, ProtFound-V presents a new state-of-the-art performance in key property predictions. Despite the challenge posed by epistasis to model generalization, ProtFound-V remains robust when extrapolating to lineages with different genetic backgrounds. Overall, this work paves the way for rapid responses to emerging viral infections, allowing for a plug-and-play approach to understanding and predicting virus evolution.

Wastewater Tiling Amplicon Sequencing Reveals Longitudinal Dynamics of SARS-CoV-2 Variants Prevalence in the Community (preprint)

SARS-CoV-2 continues to evolve, while the decline in clinical sequencing efforts hampers public health sectors to prepare for the menace of ongoing variant emergence and future COVID-19 surges.1 Wastewater-based epidemiology (WBE) has been proposed to provide complementary insights on the variants being transmitted in communities.2 However, limited research has been dedicated to the use sequencing methods for tracking disease prevalence and variant dynamics in wastewater, particularly on a large scale. Here, we employed a tiling amplicon sequencing to track the dynamics of variant of concern (VOC) in wastewater collected from Queensland, Australia from 2020 to 2022. RNA concentrations in wastewater measured by ATOPlex showed a stronger correlation and greater consistency with the number of daily new cases than a PCR-based method. The VOC dynamics observed in wastewater were largely in line with clinical reports. These findings support that WBE and sensitive sequencing methods can serve as a long-term approach for disease surveillance, thus aiding in disease outbreak prevention, control, and management.

Lung ultrasound score as a predictor of failure to wean COVID-19 patients off mechanical ventilation: a prospective observational study (preprint)

Background The lung ultrasound score was developed for rapidly assessing the extent of lung ventilation, and it can predict failure to wean various types of patients off mechanical ventilation. Whether it is also effective for COVID-19 patients is unclear.Methods This single-center, prospective, observational study was conducted to assess the ability of the 12-region lung ultrasound score to predict failure to wean COVID-19 patients off ventilation. In parallel, we assessed whether right hemidiaphragmatic excursion or previously published predictors of weaning failure can apply to these patients. Predictive ability was assessed in terms of the area under the receiver operating characteristic curve (AUC).Results Among the 35 patients in the study, 12 patients (37%) could not be weaned off mechanical ventilation. The lung ultrasound score predicted these failures with an AUC of 0.885 (95% CI 0.770–0.999, p < 0.001), and a threshold score of 10 provided specificity of 72.7% and sensitivity of 92.3%. AUCs were lower for previously published predictors of weaning failure, and right hemidiaphragmatic excursion did not differ significantly between the two groups.Conclusions The lung ultrasound score can accurately predict failure to wean critically ill COVID-19 patients off mechanical ventilation, whereas assessment of right hemidiaphragmatic excursion does not appear helpful in this regard.Trial registration: https://clinicaltrials.gov/ct2/show/NCT05706441

Study on prescription medication mode and mechanism of traditional Chinese medicine in the treatment of noncritical COVID-19 based on data mining

Background: As of 2023, coronavirus disease 2019 (COVID-19) is still spreading globally. Therefore, we aim to integrate non-critical COVID-19 high-frequency and high-targeting Chinese medicines to provide a reference for clinical prescriptions to improve COVID-19-related symptoms. Materials and methods: The information on non-critical COVID-19 high-frequency Chinese medicines in the diagnosis and treatment of COVID-19 was obtained by the TCM inheritance support platform. Using network pharmacology and molecular docking technology, high-targeting Chinese medicines with good docking activity with COVID-19 receptors angiotensin-converting enzyme-II (ACE2), 3CLpro and tyrosine-protein kinase receptor UFO (AXL) were obtained. A new prescription for non-critical COVID-19 was established by integrating high-frequency and high-targeting Chinese medicines. Rats with acute lung injury induced by lipopolysaccharide were used as the experimental model. The histopathological changes in the lungs of rats in each group were observed by hematoxylin-eosin staining. The lung coefficient of rats was measured. The levels of IL-6, TNF-a, and IL-1ß in serum were detected by enzyme-linked immunosorbent assay. The mRNA and protein levels of ACE2 and AXL in lung tissue were detected by real-time quantitative polymerase chain reaction and western blot. Results: Through data mining, it was found that there were 39 high-frequency traditional Chinese medicines for non-critical COVID-19 in the diagnosis and treatment guidelines. According to network pharmacology and molecular docking, 30 highly targeted traditional Chinese drugs for COVID-19 were found. The new prescriptions for non-critical COVID-19 were comprehensively obtained, including Glycyrrhizae Radix, Ephedra Herba, Amygdalus Communis Vas, Gypsum Fibrosum, Descurainiae Semen, Atractylodes Lancea, Scutellariae Radix, Amomum Tsao-Ko Crevostet, Forsythiae Fructus, Pogostemon cablin, Magnolia Officinalis. Compared with the LPS-induced lung injury model group, the medium dose of the new prescription group had significantly alleviated pathological changes in lung tissue, decreased lung coefficient, decreased contents of IL-6, TNF-a and IL-1ß, and increased mRNA and protein expression of ACE2 and AXL (P < 0.05). Conclusion: Based on data mining, network pharmacology and molecular docking technology, the new prescription for non-critical COVID-19 established by this method has an anti-inflammatory effect on rats with acute lung injury induced by lipopolysaccharide and can provide a reference for clinicians to alleviate the symptoms related to non-critical COVID-19. [ FROM AUTHOR] Copyright of Traditional Medicine Research is the property of TMR Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Voices from Service Providers Who Supported Young Caregivers throughout the COVID-19 Pandemic in the Canadian Context (preprint)

This empirical research is part of a larger project beginning in 2020 and ongoing until 2023 exploring the impact of the COVID-19 pandemic on young caregivers aged 5-25 years and their families in Canada. Utilizing the social determinants of health as a conceptual framework, this case study emphasizes the voices of professionals offering services to young caregiver clients during the pandemic, and explores their perspectives on the impact of the pandemic on young caregivers and their families. Across three (3) different organizations offering programs and services to young caregiver clients in Ontario, six (6) individual interviews were conducted with directors/program managers and four (4) group interviews were conducted with thirteen (13) staff members who worked directly with young caregivers and their families. Nineteen (19) service providers participated in total. The results of this study highlight five (5) primary themes that emerged through data analysis: i) the role of service providers, ii) the impact of the COVID-19 pandemic on organizations and service providers supporting young caregivers and their families, iii) barriers for service users, iv) helpful resources for service providers and organizations, and v) resources needed/preferred by service providers and organization. The pandemic significantly impacted young caregivers and their families, as reported by professionals, and organizations working with young caregivers and their families were tasked with addressing increased service demands and adapting service delivery to follow public health guidelines.

The Impact of Public Health Restrictions on Young Caregivers and How They Navigated a Pandemic: Baseline Interviews from a Longitudinal Study Conducted in Ontario, Canada (preprint)

This qualitative research study is a part of a larger research project exploring the experiences of young caregivers aged 5-26 years and their families navigating the COVID-19 pandemic between 2020 to 2023. Data was collected from 14 young caregivers who participated in baseline interviews. The central research question guiding this study: What was, is, and will be the impact of changing public health restrictions on young caregivers and their families during the pandemic and pandemic recovery? Seven themes emerged through analysis: 1) Navigating Care During the Height of Public Health Restrictions, 2) Neighbourhood and Built Environment During the Pandemic, 3) Perceptions Towards COVID and Public Health Restrictions/Efforts, 4) The Impact of Public Health Restrictions on Relationships, 5) Mental Health Challenges of Being a Young Caregiver During a Pandemic, 6) Navigating Formal Services and Supports and 7) Recommendations from Young Caregivers. The findings from this empirical research suggest that young caregivers found it easier to navigate their caregiving responsibilities when public health restrictions and work-from-home mandates were initially implemented, however, this later changed due to challenges in finding respite from caregiving, maintaining social connections with friends, creating personal space at home, and finding adequate replacements of programs once offered in-person.

Discovery and evaluation of active compounds from Xuanfei Baidu Formula against COVID-19 via SARS-Cov-2 Mpro (preprint)

Background The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) is still a widespread concern. As one of the effective traditional Chinese medicine (TCM) formula, Xuanfei Baidu formula (XFBD) shows significant efficacy for treatment of COVID-19 patients. However, its antiviral compounds and mechanism are still unclear. Purpose: In this study, we explored the bioactive compounds of XFBD and its antiviral mechanism by integrating computational analysis and experimental testing. Methods Aiming at the SARS-CoV-2 main protease (Mpro), as a key target in virus replication, the fluorescence resonance energy transfer (FRET) assay was built to screen out satisfactory natural inhibitors from XFBD. The surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) were undertaken to verify the binding affinity of Mpro-ligand. Omicron BA.1.1 and BA.2 variants were used to evaluate the antiviral activity of the focused compounds in non-cytotoxicity concentrations. For introducing the molecular mechanism, computational modeling and NMR spectra were employed to predict the binding mode and binding site of Mpro-ligand. Results From a library of 83 natural compounds, acteoside, licochalcone B, licochalcone D, linoleic acid, and physcion showed the satisfactory inhibition effect on Mpro with IC50 from 1.93 to 42.96 µM, which were further verified by SPR. Showing the excellent binding affinity, acteoside was witnessed to gain valuable insights into the thermodynamic signatures by ITC and presented antiviral activity on Omicron BA.1.1 and BA.2.3 variants in vitro. The results revealed that acteoside inhibited Mpro via forming the hydrogen bond between 7-H of acteoside and Mpro. Conclusion Acteoside is regarded as a representative active natural compound in XFBD to inhibit replication of SARS-CoV-2, which provides the antiviral evidence and some insight into the identifications of SARS-CoV-2 Mpro natural inhibitors.

Autoantigen profiling reveals a shared post-COVID signature in fully recovered and Long COVID patients (preprint)

Some individuals do not return to baseline health following SARS-CoV-2 infection, leading to a condition known as Long COVID. The underlying pathophysiology of Long COVID remains unknown. Given that autoantibodies have been found to play a role in severity of COVID infection and certain other post-COVID sequelae, their potential role in Long COVID is important to investigate. Here we apply a well-established, unbiased, proteome-wide autoantibody detection technology (PhIP-Seq) to a robustly phenotyped cohort of 121 individuals with Long COVID, 64 individuals with prior COVID-19 who reported full recovery, and 57 pre-COVID controls. While a distinct autoreactive signature was detected which separates individuals with prior COVID infection from those never exposed to COVID, we did not detect patterns of autoreactivity that separate individuals with Long COVID relative to individuals fully recovered from SARS-CoV-2 infection. These data suggest that there are robust alterations in autoreactive antibody profiles due to infection; however, no association of autoreactive antibodies and Long COVID was apparent by this assay.

Promoting economic and environmental resilience in the post-COVID-19 era through the city and regional on-road fuel sustainability development

How to control the global temperature rise within 1.5 °C in the post-COVID-19 era has attracted attention. Road transport accounts for nearly a quarter of global CO2 emissions, and the related sulfur dioxide (SO2) emissions also trigger air pollution issues in population-intensive cities and areas. Many cities and states have announced a timetable for phasing out urban-based fossil fuel vehicles. By combining a Markov-chain model with a dynamic stochastic general equilibrium (DSGE) model, the impacts of on-road energy structural change led by phasing out fossil fuel vehicles in the road transportation sector are evaluated. The impact of automobile emissions (both CO2 and SO2) on the environment is evaluated, taking into consideration of variation between cities, regions, and countries. Two other major driving forces in addition to CO2 emissions reduction in promoting fossil fuel vehicles' transition toward net-zero carbon are identified and analyzed with multiple different indicators. Under the framework of the DSGE model, climate policy instruments' effects on economic development, energy consumption, and their link to economic and environmental resilience are evaluated under exogenous shocks as well.

Epidemiological and clinical characteristics of SARS-CoV-2 Omicron variant

The COVID-19 pandemic continues to rage worldwide and the SARS-CoV-2 Omicron variant has now replaced the Delta variant as the leading epidemic strain.Omicron, as one of the SARS-COV-2 variants, incorporates the most critical mutations of the Alpha and Delta variants, and is characterized by having multiple mutation sites, high viral load, stronger infectivity and immune escape, which significantly reduced the protective effect of the vaccine.However, compared with the previous SARS-CoV-2 strains, the Omicron variant is less likely to infect lung tissue, it usually causes mild clinical symptom with reduced hospitalization rate, severe disease rate and fatality rate.Three doses of allogeneic vaccine can offer better tolerance and immunogenicity, and improve the protective effect of the vaccines.The application of small-molecule antiviral drugs and neutralizing antibodies can significantly reduce the hospitalization rate and mortality rate.In this paper, the epidemiological and characteristics of the Omicron variant were reviewed in order to provide reference for clinical diagnosis and treatment of the disease.

The roles of meteorological variables, demographic factors, and policy response measures in the variation of COVID-19 daily cases: Evidence from different climate zones (preprint)

It is widely considered that weather conditions affect the spread of COVID-19, but to date, the collective influence of demographic factors and government policy response measures have hardly been considered. The objective of this study is to utilize a machine learning method to assess the corresponding roles of meteorological variables, demographic factors, and government response measures in daily new cases of COVID-19 among multiple climate zones at city/county level. The overall model showed good performance with a validated R2 of 0.86, as satisfactory as individual climate zone models. Population density ranked the most important factor, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated among meteorological factors, but the association with daily new cases seemed to be inconsistent among different climate zones. Implementing stricter response measures could help effectively contain the spread of COVID-19, but did so with a lagged effect, and the typical lockdown measures might not be applicable to all climate conditions. This study preliminarily analyzed the roles of certain factors in the transmission of COVID-19, and provided practical evidence for developing an early health warning system of global pandemics by leveraging big data technology and multiple sourced data fusion.

Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery (preprint)

In this work, we propose MEDICO, a Multi-viEw Deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 Inhibitor disCOvery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multi-view representation learning framework to sufficiently and adaptively learn comprehensive structural semantics from targeted molecular topology and geometry. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, unique, and novel molecules under benchmarking comparisons. In particular, we showcase the multi-view deep learning model enables us to generate not only the molecules structurally similar to the targeted molecules but also the molecules with desired chemical properties, demonstrating the strong capability of our model in exploring the chemical space deeply. Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid-19 drugs. Further, we apply MEDICO to the structural optimization of three well-known Mpro inhibitors (N3, 11a, and GC376) and achieve ~88% improvement in their binding affinity to Mpro, demonstrating the application value of our model for the development of therapeutics for SARS-CoV-2 infection.

Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants (preprint)

The never-ending emergence of SARS-CoV-2 variations of concern (VOCs) has challenged the whole world for pandemic control. In order to develop effective drugs and vaccines, one needs to efficiently simulate SARS- CoV-2 spike receptor binding domain (RBD) mutations and identify high-risk variants. We pretrain a large pro- tein language model on approximately 408 million pro- tein sequences and construct a high-throughput screen- ing for the prediction of binding affinity and antibody escape. As the first work on SARS-CoV-2 RBD mu- tation simulation, we successfully identify mutations in the RBD regions of 5 VOCs and can screen millions of potential variants in seconds. Our workflow scales to 4096 NPUs with 96.5% scalability and 493.9X speedup in mixed precision computing, while achieving a peak performance of 366.8 PFLOPS (reaching 34.9% theo- retical peak) on Pengcheng Cloudbrain-II. Our method paves the way for simulating coronavirus evolution in or- der to prepare for a future pandemic that will inevitably take place.

Exploring the Impact of University Student Engagement on Junior Faculty's Online Teaching Anxiety and Coping Strategies during COVID-19

(1) Background: When online teaching or blended teaching becomes the new normal in college teaching and learning during the pandemic phase, how to cope with teaching anxiety and enhance online student engagement has been frequently discussed among scholars and practitioners. (2) Methods: This qualitative study aims to investigate the impact of online college student engagement on junior faculty's online teaching anxiety in the pandemic era, with an emergent shift to online teaching as a new normal for higher education. The study analyzed the ways junior faculty adapted to enhance online student engagement and cope with anxiety-provoking sources. (3) Results: Online teaching anxiety may occur at the beginning of the semester or during a large amount of assessment and marking and can also occur with student complaints and the inactive online engagement of students. Student engagement is the most challenging pedagogical issue during online teaching, especially social and emotional engagement. (4) Conclusion: This study recommends that peer mentoring and university-level faculty professional development services are effective strategies to reduce junior faculty's teaching anxiety. Pedagogy training and support should provide faculty with hands-on activities with problem-solving toolkits that they can take away to their own teaching.

Meta-analysis of the prevalence of anxiety and depression among frontline healthcare workers during the COVID-19 pandemic

Objective To systematically review the prevalence of anxiety and depression among frontline healthcare workers during the coronavirus disease 2019 (COVID-19) pandemic. Methods Computers were used to search CNKI, VIP, WanFang Data, PubMed, and other Chinese and English databases. The search period was limited to December 2019 to April 2022. Cross-sectional studies collected data on the prevalence of anxiety and depression among frontline healthcare workers since the onset of COVID-19. The STATA 15.1 software was used for the meta-analysis of the included literature. Results A total of 30 studies were included, with a sample size of 18,382 people. The meta-analysis results showed that during the COVID-19 pandemic, the total prevalence of anxiety among frontline healthcare workers was 43.00%, with a 95% confidence interval (CI) of 0.36–0.50, and the total prevalence of depression was 45.00%, with a 95% CI of 0.37–0.52. The results of the subgroup analysis showed that prevalence of anxiety and depression in women, married individuals, those with children, and nurses was relatively high. Frontline healthcare workers with a bachelor's degree or lower had a higher prevalence of anxiety. The prevalence of depression was higher among frontline healthcare workers with intermediate or higher professional titles. Conclusion During the COVID-19 pandemic, the prevalence of anxiety and depression among frontline healthcare workers was high. In the context of public health emergencies, the mental health status of frontline healthcare workers should be given full attention, screening should be actively carried out, and targeted measures should be taken to reduce the risk of COVID-19 infection among frontline healthcare workers. Systematic review registration http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022344706.

A retrospective cohort study: vaccination status and safety analysis of SARS-CoV-2 vaccine in patients with Wilson's disease (preprint)

Background Wilson's disease (WD) is a rare hepatic and neurological disorder that tends to worsen slowly. However, traumatic injuries, surgeries, and severe infections can dramatically worsen the disease. No studies have reported whether SARS-CoV-2 vaccination affects disease progression in patients with WD. Vaccination decisions in this population are affected by the potential adverse events following vaccination. This study aimed to determine SARS-CoV-2 vaccination status and post-vaccination adverse events in patients with WD. To assess the correlation between vaccination status and Unified Wilson's Disease Rating Scale (UWDRS) scores. Methods This was a multicenter, retrospective, observational cohort study. Data on patient characteristics, current vaccination status, vaccine type, vaccine dose, and adverse events of the vaccine were collected. We evaluated the vaccination rates in various regions of China. Subjective reasons for non-vaccination were analyzed. To compare the baseline data between the two groups, age, male, liver cirrhosis, hypersplenism, post-splenectomy, kidney damage, bone damage, and cholecystitis were used as confounding factors to compare the differences between the vaccinated and unvaccinated groups. Differences in UWDRS scores between vaccinated and unvaccinated patients were analyzed using independent-sample t-tests. Paired sample t-tests were used to analyze whether there were differences in UWDRS scores before and after vaccination in vaccinated patients. Logistic regression analysis was used to assess the correlation between vaccination status and increased UWDRS scores. Results Among the 554 patients with WD included in the study, the mean age was 25.3±10.85 years. A total of 368 (66.4%) patients received at least one dose of the SARS-CoV-2 vaccine (1 dose, n=10; 2 doses, n=161; 3 doses, n=197). A total of 186 patients (33.6%) were unvaccinated. The subjective reasons why patients did not receive the vaccine were advice from the medical center (n=83,44.6%), fear of worsening WD symptoms (n=63, 33.9%), and doubts about the effectiveness and safety of the vaccine (n=40, 21.5%). There was no significant difference in confounding factors between the vaccinated and unvaccinated groups (P>0.05). This indicated that the baseline data of the two groups maintained a certain balance. Multifactorial regression analysis showed no correlation between the confounding factors and vaccination status. There was no statistically significant difference in the UWDRS scores between the two groups (P>0.05). There was no statistically significant difference in the UWDRS scores before and after vaccination (P>0.05). Logistic regression analysis showed that vaccination against SARS-CoV-2 was not significantly associated with increased scores on the UWDRS (OR=0.924, 95% CI:0.336-2.540, P=0.879), hepatic subscale (OR=0.170, 95% CI:0.148-3.027, P=0.603), neurological subscale (OR=1.322, 95% CI:0.219-7.984, P=0.761), and psychiatric subscale (OR=1.011, 95% CI:0.183-5.571, P=0.990). A total of 78 (21.2%) of the 368 patients who received at least one dose of the vaccine had adverse events. Eight patients (2.2%) showed worsening symptoms of extrapyramidal damage. Local and systemic adverse reactions occurred in 70 (19.0 %) patients, 48 (13.0%) had local pain at the injection site, 6 (1.6%) had swelling of the vaccination arm, 6 (1.6%) had fever, 4 (1.1%) had headache, 3 (0.8%) had weakness, 2 (0.5%) had stomach ache, and 1 (0.3%) had palpitations. Conclusion In this cohort study, vaccination against SARS-CoV-2 was safe in patients with WD, providing evidence for the safety of vaccination in patients with WD. It also provides a reference for patients with other heavy metal metabolism disorders, nervous system damage, and similar diseases to be vaccinated against SARS-CoV-2.

Impact of government interventions on the stock market during COVID-19: a case study in Indonesia

This paper aims to examine the short-term impact of government interventions on 11 industrial sectors in the Indonesian Stock Exchange (IDX) during the COVID-19 pandemic. Whereas earlier studies have widely investigated the impact of government interventions on the financial markets during the pandemic, there is lack of research on analysing the financial impacts of various interventions in different industrial sectors, particularly in Indonesia. In this research, five key types of government interventions are selected amid the pandemic from March 2020 to July 2021, including economic stimulus packages, jobs creation law, Jakarta lockdowns, Ramadan travel restrictions, and free vaccination campaign. Based on an event study methodology, the research reveals that the first economic stimulus package was critical in reviving most sectors following the announcement of the first COVID-19 case in Indonesia. Jakarta lockdowns impacted stock returns negatively in most sectors, but the impacts were relatively insignificant in comparison to other countries in the region. The recurrence of lockdowns in Jakarta had a minor detrimental impact, showing that the market had acclimated to the new normal caused by the COVID-19 pandemic. Additionally, Ramadan travel restrictions caused minor negative impacts on the stock market. Furthermore, the second Ramadan travel restrictions generated a significant reaction from the technology sector. Finally, while free vaccination campaign and job creation law did not significantly boost the stock market, both are believed to result in a positive long-term effect on the country’s economy if appropriately executed. The findings are critical for investors, private companies, and governments to build on recovery action plans for major industrial sectors, allowing the stock market to bounce back quickly and efficiently. As this study limits its analysis to the short-term impact of individual interventions, future studies can examine long-term and combined effects of interventions which could also help policy makers to form effective portfolios of interventions in the event of a pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s43546-022-00312-4.

Effect of SARS-CoV-2 infection on asthma patients

Background SARS-CoV-2 causes coronavirus disease 2019 (COVID-19), a new coronavirus pneumonia, and containing such an international pandemic catastrophe remains exceedingly difficult. Asthma is a severe chronic inflammatory airway disease that is becoming more common around the world. However, the link between asthma and COVID-19 remains unknown. Through bioinformatics analysis, this study attempted to understand the molecular pathways and discover potential medicines for treating COVID-19 and asthma. Methods To investigate the relationship between SARS-CoV-2 and asthma patients, a transcriptome analysis was used to discover shared pathways and molecular signatures in asthma and COVID-19. Here, two RNA-seq data (GSE147507 and GSE74986) from the Gene Expression Omnibus were used to detect differentially expressed genes (DEGs) in asthma and COVID-19 patients to find the shared pathways and the potential drug candidates. Results There were 66 DEGs in all that were classified as common DEGs. Using a protein-protein interaction (PPI) network created using various bioinformatics techniques, five hub genes were found. We found that asthma has some shared links with the progression of COVID-19. Additionally, protein-drug interactions with common DEGs were also identified in the datasets. Conclusion We investigated possible links between COVID-19 and asthma using bioinformatics databases, which might be useful in treating COVID-19 patients. More studies on populations affected by these diseases are needed to elucidate the molecular mechanism behind their association.