Background and Purpose: Randomized Control Trials (RCTs) are the gold standard for establishing causality in drug efficacy, However, they have limitations due to strict inclusion criteria and complexity. When RCTs are not feasible, researchers turn to observational studies. Explainable AI (XAI) models provide an alternative approach to understanding cause-and-effect relationships. Experimental Approach: : In this study, we utilized an XAI model with a historical COVID-19 dataset to establish the hypothesis of drug efficacy. The datasets consisted of 3,307 COVID-19 patients from a hospital in Delhi, India. Eight XAI models were employed to assess factors influencing COVID-19 mortality. LIME and SHAP interpretability techniques were applied to the best-performing ML model to determine feature importance in outcome. Key Results: The XGBoost ML classifier outperformed (weighted F1 score, MCC, accuracy, ROC-AUC, sensitivity and specificity score of 91.7%, 58.8%, 91.3%, 92.2% 93.8%, and 70.2%, respectively) other models and the SHAP summary plot enabled the identification of significant features that contributes to COVID-19 mortality. These features encompassed comorbidities like renal and cardiac diseases and tuberculosis. Additionally, the XAI models revealed that medications such as enoxaparin, remdesivir, and ivermectin did not exhibit preventive effects on mortality Conclusion and Implications: While XAI models offer valuable insights, they should not replace RCTs as a priority for ensuring the safety and effectiveness of new drugs and treatments. However, XAI models can serve as valuable tools for suggesting future research directions and aiding clinical decision-making, particularly when the efficacy of a drug in a controlled trial is uncertain.

This paper sheds light on the changes suffered in cryptocurrencies due to the COVID-19 shock through a non-linear cross-correlations and similarity perspective. We have collected daily price and volume data for the seven largest cryptocurrencies considering trade volume and market capitalization. For both attributes (price and volume), we calculate their volatility and compute the Multifractal Detrended Cross-Correlations (MF-DCCA) to estimate the complexity parameters that describe the degree of multifractality of the underlying process. We detect (before and during COVID-19) a standard multifractal behaviour for these volatility time series pairs and an overall persistent long-term correlation. However, multifractality for price volatility time series pairs displays more persistent behaviour than the volume volatility time series pairs. From a financial perspective, it reveals that the volatility time series pairs for the price are marked by an increase in the non-linear cross-correlations excluding the pair Bitcoin vs Dogecoin (í µí»¼ í µí±¥í µí±¦ (0) = −1.14%). At the same time, all volatility time series pairs considering the volume attribute are marked by a decrease in the non-linear cross-correlations. The K-means technique indicates that these volatility time series for the price attribute were resilient to the shock of COVID-19. While for these volatility time series for the volume attribute, we find that the COVID-19 shock drove changes in cryptocurrency groups.

Introduction COVID-19 is known to induce plasma cell differentiation by elevating interleukin-6 level. Proliferation of dyscrasic plasma cells lead to production of monoclonal immunoglobulins. Cases with monoclonal gammopathy in COVID-19 have been reported in various countries. In this study, we present nine cases of Korean patients diagnosed with COVID-19 infection, and their consecutive electrophoresis results of monoclonal gammopathies. Material & Methods A total of 34 serial serum samples from 9 patients diagnosed with COVID-19 infection were analyzed for the protein electrophoresis, immunofixation, immunoglobulin quantification, SARS-CoV-2 antibody titers and interleukin-6 level assessments. Cases were classified into four groups based on the disease severity. Results Six cases revealed the presence of monoclonal paraprotein in serum electrophoresis results. Both two expired cases showed monoclonal bands, with one case of notable correlation with the increase of virus activity. Types of immunoglobulins were variable among six cases. Most of the cases showed high interleukin-6 levels and COVID-19 antibody titers, but their fluctuations did not correlate with monoclonal paraprotein patterns. Conclusions In conclusion, monoclonal paraproteins were observed with relatively higher frequency in the severe cases rather than mild cases. Longitudinal monitoring on monoclonal paraproteins associated with COVID-19 infection were proved to be transient in mild cases.

Considering three viral transmission routes— fomites, droplets, and aerosols— two routes have been the focus of debate about the relative role of droplets and aerosols in SARS-CoV-2 infection. We seek to quantify infection risk in an enclosed space via short-range and long-range airborne transmission to inform public health decision making. Data from five published studies were analyzed to predict relative exposure at distances of 1 m and farther, mediated by droplet size divided into two bins: ≧ 8 μm (medium and large droplets that we call “droplets”) and < 8 μm (small droplets that we call “aerosols”). The results at 1 m from an infectious individual were treated as a boundary condition to model infection risk at shorter and longer distance. At all distances, infection risk was treated as the sum of exposure to aerosols and droplets. It was assumed that number of virions is proportional to particle volume. The largest infection risk occurred close to the infectious individual, and out to approximately 1m, droplets and aerosols both contributed. Farther away, the largest risk was due to aerosols. For one model, droplet exposure disappeared at 1.8 m. Policy concerning physical distancing for meaningful infection reduction relies on exposure as a function of distance, yet within this construct particle size determines respiratory deposition. This two-fold distance effect can be used to evaluate measures such as plexiglass barriers, masking, and ventilation.

Objective To describe the maternal and neonatal outcomes of pregnant women with fasting blood glucose <4.7 mmol/L for whom the diagnosis and treatment pathways differed due to COVID-19. Design, setting and population. An Australian population-based data linkage study involving 3891 women. Methods: Pregnant women with fasting blood glucose <4.7 mmol/L between 24-32 weeks of gestation were categorized into three groups: women diagnosed with gestational diabetes by postprandial hyperglycaemia (PPGDM; n= 226); normal glucose tolerance group (NGT; n= 3125) and women not tested for postprandial hyperglycaemia, mostly during COVID-19 (LFBG; n= 540). The maternal and neonatal outcomes between groups were compared using generalized linear models. Main outcome measures: Risk of Large for gestational age baby. Results: Mean difference in birth weight (-115 grams, 95%CI -175.5— -32.6) and z-scores (-0.11, 95%CI -0.26— 0.02) were lower in the PPGDM group compared to the LFBG group. However, there were no differences between PPGDM and LFBG groups in the risk of large for gestational age (LGA) infants (RR: 1.09, 95%CI 0.66— 1.78). The maternal and neonatal outcomes in the LFBG group were comparable to the NGT group. Conclusion: The risk of LGA was similar across all groups regardless of post prandial glycemia and its treatment. Overall, women with a fasting blood glucose <4.7 mmol/L can be considered as a low-risk group for adverse perinatal outcomes.

Objectives: Community-acquired pneumonia (CAP) was a common respiratory tract infection in children, which can be caused by various pathogens, including bacteria, mycoplasma (MP), respiratory syncytial virus (RSV), and recently SARS-CoV-2 are the most widespread. We attempt to distinguish common and pathogen-specific host response characteristics by comparing the CAP of different pathogens. Methods: We included 200 CAP hospitalized cohort caused by SARS-CoV-2 (COVID-19, n=50), mycoplasma (CAP-MP, n=50), RSV (CAP-RSV, n=50) and other bacteria (CAP-Bacteria, n=50), of whom were balanced the potentially confounding factors (such as age and gender) based on Propensity Score Matching algorithm(PSM). We compared hematologic and biochemical indicators for different CAPs, samples were taken within 48 hours of admission. Results: Main clinical features of COVID-19 were fever, faster heart rate and lower antibiotic use. Notably, markers of immuno-inflammatory, including white blood cell, lymphocyte and procalcitonin (PCT) were not different among the CAP groups. Biomarkers reflecting nutrient metabolism showed total protein (TP) and albumin (ALB) levels in the COVID-19 group were lower than those in the CAP-MP group, the creatinine and urea levels of the COVID-19 patients were higher than that of CAP-MP group. The serum sodium and calcium levels in the COVID-19 group were the lowest and significantly lower than that in the CAP-MP group, while serum phosphorus levels were opposite. Moreover, we observed that the creatine kinase (CK) and creatine kinase-MB (CK-MB) levels in the COVID-19 were higher than those in the CAP-MP groups. Conclusions: Our study revealed common and unique pathophysiological features of different pathogens‐associated CAP, which may facilitate the pathogen-specific precision diagnosis and treatment.

Phishing emails have experienced a rapid surge in cyber threats globally, especially following the emergence of the COVID-19 pandemic. This form of attack has led to substantial financial losses for numerous organizations. Although various models have been constructed to differentiate legitimate emails from phishing attempts, attackers continuously employ novel strategies to manipulate their targets into falling victim to their schemes. This form of attack has led to substantial financial losses for numerous organizations. While efforts are ongoing to create phishing detection models, their current level of accuracy and speed in identifying phishing emails is less than satisfactory. Additionally, there has been a concerning rise in the frequency of phished emails recently. Consequently, there is a pressing need for more efficient and high-performing phishing detection models to mitigate the adverse impact of such fraudulent messages. In the context of this research, a comprehensive analysis is conducted on both components of an email message – namely, the email header and body. Sentence-level characteristics are extracted and leveraged in the construction of a new phishing detection model. This model utilizes K Nearest Neighbor (KNN)introducing the novel dimension of sentence-level analysis. Established datasets from Kaggle was employed to train and validate the model. The evaluation of this model’s effectiveness relies on key performance metrics including accuracy of 0.97, precision, recall, and F1-measure.

Introduction Tropical seasonality of respiratory syncytial virus (RSV) is one of the leading causes of hospitalization and mortality among children with respiratory tract infections. The non-pharmaceutical preventive measures implemented worldwide against Covid-19 may have also reduced the transmission of RSV, hence altering its epidemiological seasonality. However, with easing of Covid-19 restrictions, studies reported a drastic rebound of cases due to poor protective immunity from long-term reduced exposure to RSV. Thus, this is the first study to evaluate the changes of RSV epidemiology before, during and after Covid-19 pandemic in Malaysia. Methods We analyzed collated data from 2017 to 2022, involving 4084 children aged <12 years who were admitted for severe respiratory infections requiring non-invasive ventilation (NIV). Results Generally, we reported significant inverse relationship between RSV and Covid-19 infections during the pandemic (2020-2021) (p<0.05). In 2020, RSV positivity rate declined sharply to 8.3% and 5.9%, respectively in the 2 prominent seasons. Time series analysis showed a tremendous decrease in cases compared to the expected values, with the first season (98.3%, CI 95%) and second season (95.7%, CI 95%). However, with the lifting of the restriction order in 2022, RSV infections rose sharply with a positivity rate of 36.3% which was higher than before Covid-19 pandemic. Conclusion This study provides evidence of increasing RSV cases after Covid-19 pandemic due to immunity debt. Hence, the healthcare system must be prepared to deal with future RSV outbreaks with appropriate implementation of prophylaxis and public health measures.

Respiratory syncytial virus is associated with lower respiratory tract infections. As several types and genotypes can circulate at the same time, genomic characterisation is important for timely epidemiological control and treatment measures. In the last 6 seasons (2017-2023), 191236 nasopharyngeal swabs were processed for respiratory viruses. The incidence of RSV reached 7% in the pre-pandemic season. RSV was most frequent in children under 5 years of age (12.6%), but was also significant in those over 70 years of age (5.63%). The measures taken to control SARS-Cov2 infection were useful for RSV control and the incidence decreased to 1.8%, but caused a change in the types. Pre-pandemic, the majority circulating types were RSV-B/RSV-B/RSV-A and in pandemic it was RSV-B/RSV-B. In the last season, RSV-B and RSV-A were detected in the same proportion. Genetic characterization showed three new clades. This has been taken into account in order to take the correct measures.

The immunity of public health workers: observation from two waves SARS-CoV-2 Omicron variant endemic in Guangdong province, ChinaHuan Zhang1#, Lina Yi1#, Huifang Lin1#, Lirong Zou1, Pingping Zhou1, Chumin Liang1, Lilian Zeng1, Xue Zhuang1, Zhe Liu1, Jing Lu1, Jianfeng He1, Baisheng Li1*, Jiufeng Sun1,2,3,4,5*，Runyu Yuan1*.1Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangdong Provincial Key Laboratory of Pathogen Detection for Emerging Infectious Disease Response, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first appeared in Wuhan, China in 2019. Soon after, it was declared a pandemic by the World Health Organization (WHO). The health crisis imposed by a new virus and its rapid spread worldwide prompted the fast development of vaccines. For the first time in human history, two vaccines based on recombinant genetic material technology were approved for human use. These mRNA vaccines were applied in massive immunization programs around the world, followed by other vaccines based on more traditional approaches. Even though all vaccines were tested in clinical trials prior to their general administration, serious adverse events, usually of very low incidence, were mostly identified after application of millions of doses. Probing a direct correlation (the cause-effect paradigm) between vaccination and the appearance of adverse effects has proven challenging. This review focuses on anaphylaxis, myocarditis, vaccine-induced thrombotic thrombocytopenia, Guillain-Barré syndrome and transverse myelitis reported in the context of COVID-19 vaccination. We highlight their symptoms and laboratory tests required for an adequate diagnosis, and briefly outline the recommended treatments. The aim of the present work is to increase awareness among healthcare personal about these serious adverse events that may arise post-vaccination. Regardless of the ongoing discussion about the safety of COVID-19 vaccination, these adverse effects must be identified promptly and treated effectively to reduce the risk of complications.

In the wake of the havoc caused by the Covid-19 pandemic, it is imperative to use the available genomic sequence data to gain insight into the mutational and genomic diversity of SARS-CoV-2. Here we have performed comparative phylogenetic, mutational and genetic diversity analysis on 1962 SARS-CoV-2 genome sequences from seven worst hit Indian states during the third Covid-19 wave, to determine the Covid-19 strains and mutations in circulation during the third wave and the transmission pattern and disease epidemiology across the states and gain valuable insight into the viral evolution. 6083 Single nucleotide polymorphisms (SNPs) were discovered in the analysis with 93 SNPs common to all states. The genetic relatedness among the statewise multilocus genotypes was visualized by plotting a minimum spanning tree based on Bruvo’s distance framework. The phylogenetic tree based on Nei’s genetic distance showed distinct clades. The AMOVA results indicated that large proportion of the total genetic variation is distributed within the samples, rather than between the samples within each population and between the populations. Our findings provide insight into the SARS-CoV-2 variants and mutations which dominated the third Covid-19 wave in India and thus provide a basis to monitor and further assess these variants and their sub lineages and mutations for their clinical impact and reaction to existing and newly designed drugs and vaccines. The genetic diversity analysis helps in comprehending the viral transmission scenarios across the Indian states so as to enable the State government and researchers in developing state specific prevention measures for future.

The goal of this study is to analyze why some countries have had a high negative impact of COVID-19 pandemic crisis in terms of fatality rate compared to others. In particular, this study endeavors to verify whether statistical evidence supports the hypothesis that high general government gross debt in countries generates a structural vulnerability in socioeconomic and health systems to face emergencies, such as COVID-19 pandemic crisis. The research setting here analyzes the relationship between general government gross debt (as % GDP), healthcare expenditures per capita, and COVID-19 case fatality in an international context given by European countries. Empirical evidence reveals that European countries having lower COVID-19 fatality rate in 2020 (1.40%, indicated as group 1) had also lower government gross debt as % of GDP both in 2009 (50.79%) and 2019 (46.80%) than countries with higher fatality rate (2.83% given by group 2) that had 81.49% in 2009 and 67.22% in 2019. In addition, group 1 has from 2009 to 2019 a lower growth of government gross debt (% of GDP) given by 0.12 compared to group 2, which experienced a high growth of 0.29 generating a high burden for socioeconomic system and public finance. This structural situation leads countries of group 1 with lower COVID-19 fatality rates to support higher levels of health expenditure per capita (values higher than $3,100 per capita), whereas countries with a higher COVID-19 fatality rate have health expenditure per capita lower than previous group 1 (about $2,530 in 2009 and $2,600 in 2019) also for stringent austerity policies imposed by European Union. Estimated relationship shows that countries with 1% increase in the change of health expenditure per capita over 2009-2019 (predicted values considering in stage 1 the explanatory variable of government gross debt as % of GDP in 2009), it reduces the level of COVID-19 fatality rate by 2.63%. The contribution here expands the knowledge in these research topics by endeavoring to clarify primary causes of vulnerability of countries in crisis management driven by high levels of government gross debt, which deteriorate socioeconomic systems with stringent austerity policies that create constraints to increase health expenditures and improve health planning, reducing preparedness and resilience of countries to face crises similar to COVID-19 pandemic.

Letter to the EditorFunctional decline at 1 year in hospitalized elderly pneumonia with SARS-CoV-2 Omicron variant:Comparison with the ancestral strain and Alpha variantNaoyuki Miyashita1, Yasushi Nakamori2, Makoto Ogata1, Naoki Fukuda1, and Akihisa Yamura11First Department of Internal Medicine, Division of Respiratory Medicine, Infectious Disease and Allergology, Kansai Medical University, Japan2Department of Emergency Medicine, Kansai Medical University Medical Center, Japan

Background- COVID-19 took a major toll on the healthcare system in India. Multiple vaccines were developed to prevent infection with the SARS-CoV-2 virus. Infection outcome was different among unvaccinated patients and those vaccinated. Aims and Methodology- The research aimed to find out various outcomes of COVID-19 disease and to compare these outcomes in vaccinated and unvaccinated patients. Data was collected from the records in a tertiary care hospital. The patients were segregated into mild, moderate, and severe disease according to the ICMR guidelines and the results were analysed using SPSS software. Results- Data of 816 and 2010 individuals for second and third wave respectively was collected and analysed. The results show that a higher percentage of unvaccinated individuals were asymptomatic (54.7 and 59.3%) but within symptomatic individuals, mild forms of the disease are much more common in vaccinated individuals (66.6% and 63.3%) while severe forms of the disease were more common in unvaccinated individuals (5.7% and 3.1%). This suggests that while vaccination may not have a significant effect on whether symptoms are contracted, it plays a vital role in lowering the severity of the disease form. Implications- This study proved that there exists a strong correlation between vaccination and reduced severity of disease, and further emphasized the importance of vaccines in protection from grave outcomes. Calculation of risk estimate showed that the unvaccinated individuals were more at risk for severe disease than those vaccinated with one or two doses of vaccines.

This article reviews the essential role of mathematical models in understanding and combatting the pandemic of novel coronaviruses, in particular focusing the advance in the use of mathematical models in disease control in Japan. Highlighting the integral role of mathematical models in public health, the article introduces a model that factors in the heterogeneity of infectious contacts, concentrating on the effectiveness of testing and isolation, alongside a model that involves economic losses. The models exhibit how, given such heterogeneity, milder behavioral restrictions can still achieve suppression, rigorous testing and isolation can effectively curb the spread, and containment measures can mitigate economic losses. These models aid in grasping the complicated dynamics of disease transmission and optimizing interventions. The knowledge of population ecology is also considered effective for public health in statistical analysis, organizing concepts using dynamic mathematical models, which lead to policy proposals and deepen understanding. Evolution theory may help the understanding of virulence subject to change. However, effective prevention necessitates not only models but also the practical implementation of efficacious measures. The cooperation of various disciplines is particularly crucial in achieving a balance between health measures, economic interests, and human rights. Moreover, the article acknowledges the limitations of models and underscores the significance of real-world execution. Overall, the article advocates for a broader outlook to tackle future pandemics and related challenges, underscoring the importance of ongoing academic cooperation and global governance to effectively address emerging infectious diseases and their far-reaching implications.

Early SARS-CoV-2 variant detection relies on testing and genomic surveillance. The Omicron variant (B.1.1.529) has quickly become the dominant type among the previous circulating variants worldwide. Several subvariants have emerged exhibiting greater infectivity and immune evasion. In this study we aimed at studying the prevalence of the Omicron subvariants during the flu season and beyond in Lebanon through genomic screening and at determining the overall standing and trajectory of the pandemic in the country. A total of 155 RNA samples were sequenced and 39 Omicron variants were detected. XBB.1.5 (23.29%) was the most common, followed by XBB.1.9.1 (10.96%) and XBB.1.42 (7.5%). The first batch collected between September and November 2022, included the BA.2.75.2, BA.5.2, BA.5.2.20, BA.5.2.25 and BQ.1.1.5 lineages. Between December 2022 and January 2023, those lineages were replaced by BA.2.75.5, BN.1, BN.1.4, BQ.1, BQ.1.1, BQ.1.1.23, CH.1.1, CM.4 and XBK. Starting February 2023, we observed a gradual emergence and dominance of the recombinant XBB and its sub-lineages (XBB.1, XBB.1.5, XBB.1.5.2, XBB.1.5.3, XBB.1.9, XBB.1.9.1, XBB.1.9.2, XBB.1.16, XBB.1.22 and XBB.1.42). The timely detection and characterization of SARS-CoV-2 variants is important to reduce transmission through established disease control measures and to avoid introductions into animal populations that could lead to serious public health implications.

Background: There has been an increase in the number of patients with lung cancer who had previously contracted SARS-CoV-2 and currently require surgery. This study was to share the early postoperative outcomes in patients who underwent radical lung cancer surgery after early recovery of COVID-19 infection. Methods We retrospectively collected data for 99 patients who had undergone lung cancer surgery in our hospital during January 2022 and January 2023 (including the peak of the COVID-19 crisis）. The patients were divided into two groups according to the inclusion criteria and exclusion criteria. One of these included individuals with a history of SARS-CoV-2 infection. Perioperative and follow-up data at 30-day and 90-day were recorded. Results There were no statistical differences between groups (p > 0.05) in terms of their postoperative complications or 30-day and 90-day postoperative readmission rates. However, there were significant differences between groups (p < 0.05) in terms of their tumor sizes, pathological stages, total drainage volumes, drainage diversion times, and hospital stays. Conclusions The results of the present trial suggested that it is safe to implement radical curative lung cancer surgery in patients without pulmonary impairment and in the early stages (2–4 weeks) of recovery from SARS-CoV-2 infection.

Objectives: To investigate the oral symptoms of COVID-19 ’s “positive” group in Suining City after the comprehensive liberalization of epidemic prevention and control measures in China. To explore the related influencing factors of symptom differences and provide reference for the treatment of patients in the post-epidemic era. Methods: An online survey based on a random sample of 25 questions. Results: 1768 valid questionnaires were collected. The data showed that 363 people had oral problems after an infection with novel coronavirus. The oral problems after SARS‐CoV‐2 infection mainly focused on gingiva swelling or bleeding (38.57%), toothache (37.74%), bad breath (27.55%), oral ulcer (30.30%), abnormal taste (24.24%) and some infected people had a variety of oral symptoms. A small number of infected people also have other oral problems. The incidence of oral symptoms was significantly correlated with sex, age, educational level, place of residence, smoking, basic diseases, long-term saline gargle and vaccination of COVID-19 vaccine. In addition, the age, place of residence, smoking history, basic diseases, and vaccination also showed significant differences in prognosis. Conclusions: In the post-epidemic era, the most common oral problems are gingiva swelling or bleeding, toothache, bad breath, oral ulcer, and abnormal taste. And maintaining good living habits is the basis for promoting disease recovery and oral health.

The information on the microbiome’s human pathways and active members that can affect SARS-CoV-2 susceptibility and pathogenesis in the salivary proteome is very scarce. Here, we studied samples collected from April to June 2020 from unvaccinated patients. We compared 10 infected and hospitalized patients with severe (n=5) and moderate (n=5) Coronavirus Disease (COVID-19) with 10 uninfected individuals, including Non-COVID but susceptible individuals (n=5) and Non-COVID and non-susceptible healthcare workers with repeated high-risk exposures (n=5). By performing high-throughput proteomic profiling in saliva samples, we detected 226 unique differentially expressed (DE) human proteins between groups (q-value ≤0.05) out of 2721 unambiguously identified proteins (false discovery rate ≤1%). Major differences were observed between the Non-COVID vs the non-susceptible groups. Bioinformatics analysis of DE proteins revealed human proteomic signatures related to inflammatory responses, central cellular processes, and antiviral activity associated with saliva of SARS-CoV-2 infected patients (p-value ≤0.0004). Discriminatory biomarker signatures from human saliva include cystatins, protective molecules present in the oral cavity, calprotectins, involved in cell cycle progression, and histones, related to nucleosome functions. The expression level of two human proteins related to protein transport in the cytoplasm, named DYNC1 (p-value, 0.0021) and MAPRE1 (p-value, 0.047), correlated with angiotensin-converting enzyme 2 (ACE2) plasma activity. Finally, the proteomes of microorganisms present in the saliva samples showed 4 main microbial functional features related to ribosome functioning that are overrepresented in the infected group. Our study explores potential candidates involved in pathways implicated in SARS-CoV-2 susceptibility although further studies in larger cohorts will be necessary.

Monkeypox has recently surfaced as a public health issue, and the virus has spread rapidly worldwide in the post-COVID-19 period. These emerging infections hinder human health and the advancement of the global economy. There are currently no authorized medications to treat this condition. As a result, this study gap motivates us to seek a viable inhibitor from a natural source. In the present study, 10 plant-derived compounds and commercialized drugs respectively were subjected to molecular docking and 100 ns molecular dynamics simulations to identify the potent inhibitor against MPXV E8 ectodomain protein. Punicalagin showed a greater affinity for the target protein (-9.1 kcal/mol), and maraviroc, among commercial drugs, had a binding affinity of -7.4 kcal/mol which was lower than punicalagin. MD simulations substantiated this study and showed better stability of punicalagin with the target envelope protein, compared to the control drug maraviroc. This finding was validated by decreased RMSD, RMSF, Rg, and SASA values with increased H-bonding. Therefore, we could speculate that the plant-derived natural compound, punicalagin can act as a potent therapeutic against the MPXV.

The tourism industry has been severely impacted by a series of challenges in recent years, including the COVID-19 pandemic, the Russia-Ukraine War, and supply chain disruptions. These adversities have led to economic downturns and high inflation rates, making it difficult for the industry to recover and thrive. The existing situation is characterized by decreased travel demand, reduced revenue for tourism businesses, and limited opportunities for travelers to explore new destinations. One of the major problems faced by the current problem domain is the need to revitalize the tourism industry and stimulate travel activity. Traditional approaches to revive the industry have proved insufficient, and financial constraints have made it difficult for individuals to afford travel expenses. To address these problems, a comprehensive mobile application is proposed, integrating the move-to-earn concept. This concept incentivizes travelers to engage in physical activities while exploring destinations, earning rewards like digital tokens or discounts on travel-related services based on their activity level. By encouraging physical activity and exploration, the app revitalizes the tourism industry, boosting footfall and generating revenue for local businesses. Additionally, the rewards earned through the app provide financial relief and make travel more affordable, thereby overcoming the economic challenges faced by both travelers and the industry. Overall, this solution addresses the existing situation by incentivizing travel and physical activity, promoting local businesses, and mitigating the financial burden on travelers. It offers a comprehensive and innovative approach to revitalize the tourism industry and create a win-win situation for all stakeholders involved.

Background: The Covid-19 pandemic affected healthcare delivery globally. The impact was more for individuals with chronic diseases requiring regular visits to the hospital. This study aimed to assess the impact of the pandemic on care for patients with Sickle cell disease (SCD) in Nigeria. Methods: This was a cross-sectional survey involving 435 SCD patients from the six geopolitical zones of Nigeria. Data was collected through pre-tested and validated, semi-structured questionnaires using Google forms online and face-to-face interviews at the Clinic. Results: Teaching hospitals were the most utilised health facility by the respondents 269 (61.8%). Bone pain was the most frequent crisis 318 (70.7%) with a significant increase in frequency during the pandemic when compared with the preceding year, p=0.004. Many of the respondents 227(52.2%) required hospitalization during the pandemic. Major concerns expressed for accessing care include fear of exposure to Covid-19-infected patients or health workers who have been exposed to Covid and conversion of regular health facilities to Covid-19 treatment centres. During the Pandemic, 102 (23.4%) respondents had easy access to their doctors and telemedicine was available to 87(20%) but 56 (12.9%) and 97 (22.3%, ) had difficulties in getting blood transfusion and accessing laboratory tests respectively. Up to 139(32.9%) did not have difficulties getting pain relief, 169 (38.9%) had their routine medications while 166 (38.2%) had their income significantly affected during the pandemic. Conclusion: The findings revealed challenges in accessing routine medical care, concerns about exposure to COVID-19, and disruptions in healthcare services. This article discusses the implications of the survey results and proposes strategies to strengthen sickle cell care in preparation for future pandemics.

Background: The article presents the results of a longitudinal study performed to assess the humoral immune response to the Johnson & Johnson (J&J) COVID-19 vaccine (Ad26.COV2.S) in a low-income, Hispanic patient population. In our work, we evaluate the COVID-19 health status of the patient population following vaccination. This work is a follow-up of a pilot study that analyses additional data to better understand the impact of the vaccine. Methods: Longitudinal cohort study testing the antibody responses of patients each visit within 120 days (initial, 1st follow-up, and 2nd follow-up). This study used the Clungene® SARS-CoV-2 IgG/IgM Rapid Test Cassette to determine the presence of binding antibodies resulting from the J&J COVID-19 vaccine. Results: Fifty-seven (57) patients were eligible for participation in this study after initial baseline antibodies were measured; seventy-four (74) patients were requested to participate, 17 were excluded because moderate to strong immunoglobulin G (IgG)/immunoglobulin M (IgM) responses. High loss to follow-up were multifactorial, but a predominant reason was travel out of state for work or return to country of origin.