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Influenza, an acute respiratory illness, remains a significant public health challenge, contributing substantially to morbidity and mortality worldwide. Its seasonal prevalence exhibits diversity across regions with distinct climates. This study aimed to explore the seasonal patterns of influenza and their correlation with meteorological and air pollution factors across six regions of Thailand. We conducted an analysis of monthly average temperature, relative humidity, precipitation, PM10, NO 2 , O 3 concentrations, and influenza incidence data from 2009 to 2019 using wavelet analysis. Our findings reveal inconsistent biannual influenza prevalence patterns throughout the study period. The biannual pattern emerged during 2010-2012 across all regions but disappeared during 2013-2016. However, post-2016, the biannual cycles resurfaced, with peaks occurring during the rainy and winter seasons in most regions, except for the southern region. Wavelet coherence reveals that relative humidity can be the main influencing factor for influenza incidence over a one-year period in the northern, northeastern, central, Bangkok-metropolitan, and eastern regions, not in the southern region during 2010-2012 and 2016-2018. Similarly, precipitation can drive the influenza incidence at the same period for the northeastern, central, Bangkok-metropolitan, and eastern regions. PM10 concentration can influence influenza incidence over a half-year period in the northeastern, central, Bangkok-metropolitan, and eastern regions of Thailand during certain years. These results enhance our understanding of the temporal dynamics of influenza seasonality influenced by weather conditions and air pollution over the past 11 years. Such knowledge is invaluable for resource allocation in clinical settings and informing public health strategies, particularly in navigating Thailand's climatic complexities., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The COVID-19 pandemic has significantly impacted public health and necessitated urgent actions to mitigate its spread. Monitoring and predicting the outbreak's progression have become vital to devise effective strategies and allocate resources efficiently. This study presents a novel approach utilizing Multivariate Long Short-Term Memory (LSTM) to analyze and predict COVID-19 trends in Central Thailand, particularly emphasizing the multi-feature selection process. To consider a comprehensive view of the pandemic's dynamics, our research dataset encompasses epidemiological, meteorological, and particulate matter features, which were gathered from reliable sources. We propose a multi-feature selection technique to identify the most relevant and influential features that significantly impact the spread of COVID-19 in the region to enhance the model's performance. Our results highlight that relative humidity is the key factor driving COVID-19 transmission in Central Thailand. The proposed multi-feature selection technique significantly improves the model's accuracy, ensuring that only the most informative variables contribute to the predictions, avoiding the potential noise or redundancy from less relevant features. The proposed LSTM model demonstrates its capability to forecast COVID-19 cases, facilitating informed decision-making for public health authorities and policymakers., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Mass vaccination has proven to be an effective control measure for mitigating the transmission of infectious diseases. Throughout history, various vaccination strategies have been employed to control infections and terminate outbreaks. In this study, we utilized the transmission of COVID-19 as a case study and constructed a stochastic age-structured compartmental model to investigate the effectiveness of different vaccination strategies. Our analysis focused on estimating the outbreak extinction probability under different vaccination scenarios in both homogeneous and heterogeneous populations. Notably, we found that population heterogeneity can enhance the likelihood of outbreak extinction at varying levels of vaccine coverage. Prioritizing vaccinations for individuals with higher infection risk was found to maximize outbreak extinction probability and reduce overall infections, while allocating vaccines to those with higher mortality risk has been proven more effective in reducing deaths. Moreover, our study highlighted the significance of booster doses as the vaccine effectiveness wanes over time, showing that they can significantly enhance the extinction probability and mitigate disease transmission., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The Malayan pit viper ( Calloselasma rhodostoma ) is a hemotoxic snake widely found in Southeast Asia and is responsible for the majority of poisoning cases in this region, including Thailand. However, a comprehensive knowledge of the venom protein profile and classification, as well as novel venom proteins, of this viper is still limited. Recently, the detailed composition of several snake venoms has been discovered through the use of transcriptome analysis. Therefore, the aim of this study was to employ a next-generation sequencing platform and bioinformatics analysis to undertake venom-gland de novo transcriptomics of Malayan pit vipers. Furthermore, 21,272 functional coding genes were identified from 36,577 transcripts, of which 314 transcripts were identified as toxin proteins, accounting for 61.41% of total FPKM, which can be categorized into 22 toxin gene families. The most abundant are snake venom metalloproteinase kistomin (P0CB14) and zinc metalloproteinase/disintegrin (P30403), which account for 60.47% of total toxin FPKM and belong to the SVMP toxin family, followed by snake venom serine protease 1 (O13059) and Snaclec rhodocetin subunit beta (P81398), which account for 6.84% and 5.50% of total toxin FPKM and belong to the snake venom serine protease (SVSP) and Snaclec toxin family, respectively. Amino acid sequences of the aforementioned toxins were compared with those identified in other important medical hemotoxic snakes from Southeast Asia, including the Siamese Russell's viper ( Daboia siamensis ) and green pit viper ( Trimeresurus albolabris ), in order to analyze their protein homology. The results demonstrated that ranges of 58%-62%, 31%-60%, and 48%-59% identity was observed among the SVMP, Snaclec, and SVSP toxin families, respectively. Understanding the venom protein profile and classification is essential in interpreting clinical symptoms during human envenomation and developing potential therapeutic applications. Moreover, the variability of toxin families and amino acid sequences among related hemotoxic snakes found in this study suggests the use and development of universal antivenom for the treatment of envenomating patients is still challenging., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results., (© 2023 The Authors. Published by Elsevier Ltd.)

Background: Stroke recurrence is increasing in the working-age population. This study aimed to summarize and evaluate the risk factors for recurrent stroke among the working-age population., Methods: Relevant studies were extracted from several databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Fixed- or random-effects estimates of the pooled odds ratio (OR) and 95% confidence interval (CI) of risk factors for recurrent stroke were generated based on heterogeneity. Subgroup and publication bias analyses were also performed., Results: Seventeen studies were included in the meta-analysis. Pooled effects results revealed that the risk of recurrent stroke in the working-age group was as follows: Diabetes (OR = 1.85, 95% CI: 1.47, 2.32), hypertension (OR = 1.27, 95% CI: 1.12, 1.44), smoking (OR = 1.52, 95% CI: 1.27, 1.81), history of cardiac disease (OR = 2.86, 95% CI: 2.22, 3.67), history of stroke (OR = 2.45, 95% CI: 1.81, 3.31), and National Institutes of Health stroke severity score (OR = 1.09, 95% CI: 1.03, 1.15)., Conclusion: These findings suggest that several factors, such as diabetes, hypertension, smoking, history of cardiac disease and stroke, and severity of a stroke, are potential risk factors for recurrent stroke in the working-age group. Therefore, strategies to reduce those risk factors should be adopted and attention paid to prevent recurrent stroke among working-age populations., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Mental well-being is a state of positive mental health, but there is currently no valid tool for assessing mental well-being in the Thai population. The purpose of this research was to develop and validate a mental well-being scale for Thai people. Data from 2000 Thai citizens were analyzed to explore the structural components of mental well-being. Exploratory factor analysis (n = 1000) was conducted and determined a mental well-being factor structure consisting of three factors: 1) positive emotion and thinking, 2) positive relationship and 3) positive functioning. Confirmatory factor analysis of a validation group (n = 1000) identified 10 items within these three factors of mental well-being. Psychometric analyses supported internal consistency reliability, as well as convergent and discriminant validity of the scale. The findings suggest that the construction of this new mental well-being scale for Thai people is reliable and valid. This scale will be a useful tool for addressing and identifying mental health problems among the Thai population., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Objectives: An outbreak of the novel coronavirus in December 2019 caused a worldwide pandemic. This disease also impacts European countries, including Germany. Without effective medicines or vaccines, non-pharmaceutical interventions are the best strategy to reduce the number of cases., Study Design: A deterministic model was simulated to evaluate the number of infectious and healthcare demand., Method: Using an age-structured SEIR model for the COVID-19 transmission, we project the COVID-19-associated demand for hospital and ICU beds within Germany. We estimated the effectiveness of different control measures, including active case-finding and quarantining of asymptomatic persons, self-isolation of people who had contact with an infectious person, and physical distancing, as well as a combination of these control measures., Results: We found that contact tracing could reduce the peak of ICU beds as well as mass testing. The time delay between diagnosis and self-isolation influences the control measures. Physical distancing to limit the contact rate would delay the peak of the outbreak, which results in the demand for ICU beds being below the capacity during the early outbreak., Conclusions: Our study analyzed several scenarios in order to provide policymakers that face the pandemic of COVID-19 with insights into the different measures available. We highlight that the individuals who have had contact with a virus-positive person must be quarantined as soon as possible to reduce contact with possible infectious cases and to reduce transmission. Keeping physical distance and having fewer contacts should be implemented to prevent overwhelming ICU demand., Competing Interests: The authors declare no conflicts of interest., (© 2021 The Author(s).)