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Abstract This cross-sectional study explores the impact of FTO gene single nucleotide polymorphisms (SNPs) rs9939609 and rs1421085 on dietary habits contributing to obesity risk in Thai adults. The study enrolled 384 participants from Bangkok, categorized as non-obese (BMI

Abstract Melioidosis is a life-threatening tropical disease caused by an intracellular gram-negative bacterium Burkholderia pseudomallei. B. pseudomallei polymerizes the host cell actin through autotransporters, BimA, and BimC, to facilitate intracellular motility. Two variations of BimA in B. pseudomallei have been reported previously: BimABp and BimA B. mallei-like (BimABm). However, little is known about genetic sequence variations within BimA and BimC, and their potential effect on the virulence of B. pseudomallei. This study analyzed 1,294 genomes from clinical isolates of patients admitted to nine hospitals in northeast Thailand between 2015 and 2018 and performed 3D structural analysis and plaque-forming efficiency assay. The genomic analysis identified 10 BimABp and 5 major BimC types, in the dominant and non-dominant lineages of the B. pseudomallei population structure. Our protein prediction analysis of all BimABp and major BimC variants revealed that their 3D structures were conserved compared to those of B. pseudomallei K96243. Sixteen representative strains of the most distant BimABp types were tested for plaque formation and the development of polar actin tails in A549 epithelial cells. We found that all isolates retained these functions. These findings enhance our understanding of the prevalence of BimABp and BimC variants and their implications for B. pseudomallei virulence.

Abstract End-stage kidney disease (ESKD) is fatal without treatment by kidney replacement therapies (KRTs). However, access to these treatment modalities can be problematic given the high costs. This systematic review (SR) aims to provide an updated economic evaluation of pairwise comparisons of KRTs and the implications for the proportion of patients with access to the KRT modalities, i.e., kidney transplantation (KT), hemodialysis (HD), and peritoneal dialysis (PD). This SR was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020. We searched studies in PubMed, Embase, Scopus, and Cost Effectiveness Analysis (CEA) registry, from inception to March 2023. Thirteen studies were included with pairwise comparisons among three KRTs, with varying proportions of patients for each modality. Seven studies were from high-income countries, including five from Europe. Summary findings are presented on a cost-effectiveness plane and incremental net benefit (INB). KT was the most cost-effective intervention across the pairwise comparisons. KT and PD were both more cost-effective alternatives to HD. HD was more costly and less effective than PD in all studies except one. Concurrent efforts to increase both KT and PD represented the best scenario to improve treatment options for ESKD patients.

Abstract Animal rabies is a potentially fatal infectious disease in mammals, especially dogs. Currently, the number of rabies cases in pet dogs is increasing in several regions of Thailand. However, no passive postexposure prophylaxis (PEP) has been developed to combat rabies infection in animals. As monoclonal antibodies (MAbs) are promising biological therapies for postinfection, we developed a canine-neutralizing MAb against rabies virus (RABV) via the single-chain variable fragment (scFv) platform. Immunized phage-displaying scFv libraries were constructed from PBMCs via the pComb3XSS system. Diverse canine VHVLκ and VHVLλ libraries containing 2.4 × 108 and 1.3 × 106 clones, respectively, were constructed. Five unique clones that show binding affinity with the RABV glycoprotein were then selected, of which K9RABVscFv1 and K9RABVscFv16 showed rapid fluorescent foci inhibition test (RFFIT) neutralizing titers above the human protective level of 0.5 IU/ml. Finally, in silico docking predictions revealed that the residues on the CDRs of these neutralizing clones interact mainly with similar antigenic sites II and III on the RABV glycoprotein. These candidates may be used to develop complete anti-RABV MAbs as a novel PEP protocol in pet dogs and other animals.

Abstract Currently, interferon-gamma release assay (IGRA) is costly and not included as latent tuberculosis infection (LTBI) screening test strategy in Thailand’s Universal Coverage Scheme (UCS) benefit package. The objective of this study was to assess the cost-utility of LTBI screening strategies among tuberculosis (TB) contacts in Thailand. A hybrid decision tree and Markov model was developed to compare the lifetime costs and health outcomes of tuberculin skin test (TST) and IGRA, in comparison to no screening, based on a societal perspective. Health outcomes were the total number of TB cases averted and quality-adjusted life years (QALYs), with results presented as an incremental cost-effectiveness ratio (ICER). One-way and probabilistic sensitivity analyses were performed to explore uncertainties in all parameters. The ICER of TST compared with no screening was 27,645 baht per QALY gained, while that of IGRA compared to TST was 851,030 baht per QALY gained. In a cohort of 1000 TB contacts, both TST and IGRA strategies could avert 282 and 283 TB cases, respectively. At the Thai societal willingness-to-pay threshold of 160,000 baht per QALY gained, TST was deemed cost-effective, whereas IGRA would not be cost-effective, unless the cost of IGRA was reduced to 1,434 baht per test.

Abstract This study aimed to assess the cost-utility and budget impact of dual to single HER2 targeted neoadjuvant therapy for HER2-positive breast cancer in Sri Lanka. A five-health state Markov model with lifetime horizon was used to assess the cost-utility of neoadjuvant trastuzumab (T) plus pertuzumab (P) or lapatinib (L) compared to single therapy of T with chemotherapy (C), in public healthcare system and societal perspectives. Input parameters were estimated using local data, network meta-analysis, published reports and literature. Costs were adjusted to year 2021 (1USD = LKR194.78). Five-year budget impact for public healthcare system was assessed. Incremental cost-effectiveness ratios in societal perspective for neoadjuvantLTC plus adjuvantT (strategy 3), neoadjuvantPTC plus adjuvantT (strategy 2), neoadjuvantLTC plus adjuvantLT (strategy 5), and neoadjuvantPTC plus adjuvantPT (strategy 4) compared to neoadjuvantTC plus adjuvantT (strategy 1) were USD2716, USD5600, USD6878, and USD12127 per QALY gained, respectively. One GDP per-capita (USD3815) was considered as the cost-effectiveness threshold for the analysis. Even though only the ICER for strategy 3 was cost-effective, uncertainty of efficacy parameter was revealed. For strategy 2 neoadjuvant PTC plus adjuvant T, a 25% reduction of neoadjuvant regimen cost was required to be cost effective for use in early HER2 positive breast cancer.

The rapid advancement in Artificial Intelligence (AI) and big data has developed significance in the water sector, particularly in hydrological time-series predictions. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have become research focal points due to their effectiveness in modeling non-linear, time-variant hydrological systems. This review explores the different architectures of RNNs, LSTMs, and Gated Recurrent Units (GRUs) and their efficacy in predicting hydrological time-series data. • RNNs are foundational but face limitations such as vanishing gradients, which impede their ability to model long-term dependencies. LSTMs and GRUs have been developed to overcome these limitations, with LSTMs using memory cells and gating mechanisms, while GRUs provide a more streamlined architecture with similar benefits. • The integration of attention mechanisms and hybrid models that combine RNNs, LSTMs, and GRUs with other Machine learning (ML) and Deep Learning (DL) has improved prediction accuracy by capturing both temporal and spatial dependencies. • Despite their effectiveness, practical implementations of these models in hydrological time series prediction require extensive datasets and substantial computational resources.Future research should develop interpretable architectures, enhance data quality, incorporate domain knowledge, and utilize transfer learning to improve model generalization and scalability across diverse hydrological contexts.

This study investigates the influence of El Niño Southern Oscillation (ENSO) on monthly precipitation anomaly (PPTA) in Northeast Thailand using 30 years (1993–2022) data obtained from 27 weather stations of the Thai Meteorological Department (TMD). Pearson correlation analysis elucidates the relationship between ENSO indices and PPTA. Results reveal significant correlations between ENSO indices and PPTA, providing insights into their intricate relationship. La Niña events have a stronger relationship with PPTA than El Niño events, with notable correlations observed. • Strong La Niña phases, PPTA tends to increase across Northeast Thailand when Niño 3, 3.4, and 4 are more strongly negative -0.64, -0.50, and -0.65, respectively. These correlations provide valuable insights into the hydrological influence of ENSO in Thailand. • After the ENSO event, there appears to be a 4–5 month lag period from the impact on PPTA that shows a higher correlation. • Wavelet transform coherence (WTC) analysis dissects the temporal and frequency-specific nature of the relationship between ENSO and PPTA found during the frequency of 2–7 years. These findings underscore the importance of studying regional variations in ENSO impacts for PPTA in Northeast Thailand.

Understanding hydrological processes necessitates the use of modeling techniques due to the intricate interactions among environmental factors. Estimating model parameters remains a significant challenge in runoff modeling for ungauged catchments. This research evaluates the Soil and Water Assessment Tool's capacity to simulate hydrological behaviors in the Tha Chin River Basin with an emphasis on runoff predictions from the regionalization of hydrological parameters of the gauged basin, Mae Khlong River Basin. Historical data of Mae Khlong River Basin from 1993 to 2017 were utilized for calibration, followed by validation using data from 2018 to 2022. • Calibration results showed the SWAT model's reasonable accuracy, with R² = 0.85, and the validation with R² of 0.64, indicating a satisfactory match between observed and simulated runoff. • Utilizing Machine Learning (ML) techniques for parameter regionalization revealed nuanced differences in model performance. The Random Forest (RF) model exhibited an R² of 0.60 and the Artificial Neural Networks (ANN) model slightly improved upon RF, showing an R² of 0.61 while the Support Vector Machine (SVM) model demonstrated the highest overall performance, with an R² of 0.63. • This study highlights the effectiveness of the SWAT and ML techniques in predicting runoff for ungauged catchments, emphasizing their potential to enhance hydrological modeling accuracy. Future research should focus on integrating these methodologies in various basins and improving data collection for better model performance.

Background Poor adherence to chronic cardiovascular treatments can impede targeted clinical outcomes. This study estimates the potential benefits of improving adherence among patients with cardiovascular disease requiring secondary prevention in Mexico, Thailand, and China. Methods and Results We performed Markov model simulation for patients with cardiovascular disease in 3 countries from health care and societal perspectives over a lifetime horizon. Two scenarios were compared: (1) optimal adherence based on a meta‐analysis of 51 randomized controlled trials and (2) status quo. The association between adherence and cardiovascular disease outcomes derives from a dose–response meta‐analysis of 4 051 338 patients. Outcomes include the accumulated number of cardiovascular events and associated costs in 2022 US dollars, life years, and quality‐adjusted life years. Optimal adherence could prevent 42 (95% credible interval [CrI], 29–56) cardiovascular events in Mexico, 34 (95% CrI, 24–50) in Thailand, and 63 (95% CrI, 43–89) in China per 1000 patients over a lifetime. Incremental effectiveness per patient was 0.60 (95% CrI, 0.47–0.74) life‐years in Mexico, 0.68 (95% CrI, 0.37–0.94) quality‐adjusted life years in Thailand, and 0.93 (95% CrI, 0.44–1.27) quality‐adjusted life years in China. Cost savings from societal perspective amounted to $412 (95% CrI, $211–$723), $316 (95% CrI, $187–$541), and $700 (95% CrI, $355–$1144) per patient for Mexico, Thailand, and China, respectively. Findings remained cost saving in deterministic and probabilistic sensitivity analyses. Conclusions Achieving optimal adherence in patients with cardiovascular disease requiring lipid‐lowering therapy saves costs and improves health outcomes in Mexico, Thailand, and China. These findings support national health care systems implementing strategies to improve adherence in these countries.

Aims: This study aimed to determine the expression and clinical significance of a cartilage protein, cartilage oligomeric matrix protein (COMP), in knee osteoarthritis (OA) patients. Methods: A total of 270 knee OA patients and 93 healthy controls were recruited. COMP messenger RNA (mRNA) and protein levels in serum, synovial fluid, synovial tissue, and fibroblast-like synoviocytes (FLSs) of knee OA patients were determined using enzyme-linked immunosorbent assay, real-time polymerase chain reaction, and immunohistochemistry. Results: COMP protein levels were significantly elevated in serum and synovial fluid of knee OA patients, especially those in the advanced stages of the disease. Serum COMP was significantly correlated with radiological severity as well as measures of body composition, physical performance, knee pain, and disability. Receiver operating characteristic curve analysis unveiled a diagnostic value of serum COMP as a biomarker of knee OA (41.64 ng/ml, area under the curve (AUC) = 1.00), with a sensitivity of 99.6% and a specificity of 100.0%. Further analysis uncovered that COMP mRNA expression was markedly upregulated in the inflamed synovium of knee OA, consistent with immunohistochemical staining revealing localization of COMP protein in the lining and sub-lining layers of knee OA inflamed synovium. Most notably, relative COMP mRNA expression in knee OA synovium was positively associated with its protein levels in serum and synovial fluid of knee OA patients. In human knee OA FLSs activated with tumour necrosis factor-alpha, COMP mRNA expression was considerably up-regulated in a time-dependent manner. Conclusion: All results indicate that COMP might serve as a supportive diagnostic marker for knee OA in conjunction with the standard diagnostic methods. Cite this article: Bone Joint Res 2024;13(6):261–271.

Abstract Schistosomiasis, caused by Schistosoma trematodes, is a significant global health concern, particularly affecting millions in Africa and Southeast Asia. Despite efforts to combat it, the rise of praziquantel (PZQ) resistance underscores the need for new treatment options. Protein kinases (PKs) are vital in cellular signaling and offer potential as drug targets. This study focused on focal adhesion kinase (FAK) as a candidate for anti-schistosomal therapy. Transcriptomic and proteomic analyses of adult S. mekongi worms identified FAK as a promising target due to its upregulation and essential role in cellular processes. Molecular docking simulations assessed the binding energy of FAK inhibitors to Schistosoma FAK versus human FAK. FAK inhibitor 14 and PF-03814735 exhibited strong binding to Schistosoma FAK with minimal binding for human FAK. In vitro assays confirmed significant anti-parasitic activity against S. mekongi, S. mansoni, and S. japonicum, comparable to PZQ, with low toxicity in human cells, indicating potential safety. These findings highlight FAK as a promising target for novel anti-schistosomal therapies. However, further research, including in vivo studies, is necessary to validate efficacy and safety before clinical use. This study offers a hopeful strategy to combat schistosomiasis and reduce its global impact.

Abstract Tuberculosis (TB) causes an economic impact on the patients and their households. Although Thailand has expanded the national health benefit package for TB treatment, there was no data on out-of-pocket payments and income losses due to TB from patients and their household perspectives. This national TB patient cost survey was conducted to examine the TB-related economic burden, and assess the proportion of TB patients and their households facing catastrophic total costs because of TB disease. A cross-sectional TB patient cost survey was employed following WHO methods. Structured interviews with a paper-based questionnaire were conducted from October 2019 to July 2021. Both direct and indirect costs incurred from the patient and their household perspective were valued in 2021 and estimated throughout pre- and post-TB diagnosis episodes. We assessed the proportion of TB-affected households facing costs > 20% of household expenditure due to TB. We analyzed 1400 patients including 1382 TB (first-line treatment) and 18 drug-resistant TB patients (DR-TB). The mean total costs per TB episode for all study participants were 903 USD (95% confident interval; CI 771–1034 USD). Of these, total direct non-medical costs were the highest costs (mean, 402 USD, and 95%CI 334–470 USD) incurred per TB-affected household followed by total indirect costs (mean, 393 USD, and 95%CI 315–472 USD) and total direct medical costs (mean, 107 USD, and 95%CI 81–133 USD, respectively. The proportion of TB-affected households facing catastrophic costs was 29.5% (95%CI 25.1–34.0%) for TB (first-line), 61.1% (95%CI 29.6–88.1%) for DR-TB and 29.9% (95%CI 25.6–34.4%) overall. This first national survey highlighted the economic burden on TB-affected households. Travel, food/nutritional supplementation, and indirect costs contribute to a high proportion of catastrophic total costs. These suggest the need to enhance financial and social protection mechanisms to mitigate the financial burden of TB-affected households.

The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), quantile mapping (QM), and empirical quantile mapping (EQM). Utilizing precipitation data from 30 observation stations across Southern Thailand, the evaluation encompasses five CMIP6 GCM models (CAMS-CSM1-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, IPSL-CM6A-LR). Evaluation metrics, root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation (r), index of agreement (d), and mean bias error (MBE) are employed to assess BC methods. Evaluation measures suggest that the DT method outperforms EQM and QM, with higher accuracy and lower errors (DT: RMSE = 3.61, MAE = 2.32; EQM: RMSE = 3.82, MAE = 3.70). Taylor diagrams show that CNRM-ESM2-1 has the highest correlation across sites (r = 0.36), albeit with a wider dispersion, while CanESM5 has a more balanced performance. Significant annual precipitation increases are projected for different spans 2021-30, 2061-70, and 2091–2100, particularly under SSP585, which will influence flood risk, water management, and climate adaptation. Future projections with SSP585 continuously projecting a larger probability of increased precipitation. The DT method is recommended for the downscaling of CMIP6 GCMs for precipitation projections in Southern Thailand, recognizing its superior performance. The study's findings provide a foundation for informed decision-making and adaptation planning in southern Thailand, urging policymakers to prioritize climate resilience and adaptation strategies while using a multi-model ensemble approach for robust climate forecasts.

Background The postural abnormality, forward shoulder posture (FSP), is the most common cause of respiratory impairment in older individuals with chronic obstructive pulmonary disease (COPD). A recent study found that performing pectoral stretching and scapular strengthening exercises for eight weeks could reduce FSP in healthy participants. We aimed to determine the effects of pectoral stretching and scapular stabilizer strengthening exercises on FSP, chest wall mobility, respiratory muscle strength, and pulmonary function in male patients with COPD. Methods This study was randomized clinical trial. Forty male COPD patients with FSP aged 60–90 years were included and randomly allocated to control (n=20) and exercise (n=20) groups. Following completion of the scapulothoracic exercises (three days/week, for eight weeks), respiratory functions were assessed by measuring the magnitude of FSP, chest mobility, respiratory muscle strength, and pulmonary functions. Results FSP and thoracic kyphosis angle significantly decreased compared to controls (p

Accurate drought prediction is crucial for enhancing resilience and managing water resources. Developing robust forecasting models and understanding the variables influencing their outcomes are essential. This study developed models that integrate wavelet transformation (WT) with advanced artificial intelligence (AI) models, increasing prediction accuracy. This study investigates the prediction of meteorological droughts using standalone bootstrapped random forest (BRF) and bi-directional long short-term memory (Bi-LSTM) models, compared to wavelet-decomposed hybrid models (WBRF, WBi-LSTM). These models were evaluated in the Mun River Basin, Thailand, utilizing monthly meteorological data (1993–2022) from the Thai Meteorological Department. The predictions were assessed using statistical metrics (R2, MAE, RMSE, and MAPE). For the Standardized Precipitation Index (SPI), the hybrid WBRF model consistently outperformed the standalone BRF across various metrics and timescales, demonstrating higher R2 (0.89–0.97 for SPI-3) and lower error metrics (MAE: 0.144–0.21 for SPI-6, RMSE: 0.2–0.3 for SPI-12). Similarly, the hybrid WBi-LSTM model outperformed the standalone Bi-LSTM in SPI predictions, exhibiting higher R2 (0.87–0.91 for SPI-3) and lower error metrics (MAE: 0.19–0.23 for SPI-6, RMSE: 0.27–0.81 for SPI-12) across all timescales. This trend was also observed for the China Z-index, Modified China Z-index, Hutchinson Drought Severity Index, and Rainfall Anomaly Index, where hybrid models achieved superior performance compared to standalone models. The WBi-LSTM model emerged as the preferred choice across different timespans. The integration of WT enhanced the predictive accuracy of hybrid models, making them effective tools for drought prediction.

Climate change (CC) is causing a significant threat to agriculture, a sector complicatedly tied to natural resources. Changes in precipitation patterns, atmospheric water content, and rising temperatures intensely affect global agriculture, especially in tropical regions. In this intense CC scenario, potential evapotranspiration (PET) and crop water requirement (CWR) are critical components of agricultural water management. This study evaluates the future impact of CC on precipitation, CWR, and PET in different provinces of Thailand's northern and northeastern regions. Three bias correction methods (Delta (DT), Empirical Quantile Mapping (EQM), and Quantile Mapping (QM)) were employed for precipitation downscaling from the CanESM5 CMIP6-GCM across selected 13 coffee farms with different coffee species. Arabica and Robusta coffee were carefully selected for this analysis. The DT method demonstrated superiority, exhibiting lower RMSE and higher correlation coefficients than EQM and QM. Farm-specific assessments illuminated water demand's complex dynamics during critical growth stages, showcasing variable CWR and PET. During the blooming stage in N-F1, CWR ranged from 16.7 to 33.7 mm/stage, highlighting the variability in water needs. Projected CC impacts on Arabica and Robusta coffee farms in Chiang Rai and Sisaket presented challenges, emphasizing farm-specific strategies to address potential water deficits or surpluses during critical growth phases. Projected 2023, 2028, and 2033 precipitation demonstrated incongruities with CWR and PET. The findings emphasize the crucial role of farm-specific adaptive strategies in mitigating the impacts of changing precipitation patterns on coffee cultivation.

Abstract Background It was hypothesized that glucose-6-phosphate dehydrogenase (G6PD) deficiency confers a protective effect against malaria infection, however, safety concerns have been raised regarding haemolytic toxicity caused by radical cure with 8-aminoquinolines in G6PD-deficient individuals. Malaria elimination and control are also complicated by the high prevalence of G6PD deficiency in malaria-endemic areas. Hence, accurate identification of G6PD deficiency is required to identify those who are eligible for malaria treatment using 8-aminoquinolines. Methods The prevalence of G6PD deficiency among 408 Thai participants diagnosed with malaria by microscopy (71), and malaria-negative controls (337), was assessed using a phenotypic test based on water-soluble tetrazolium salts. High-resolution melting (HRM) curve analysis was developed from a previous study to enable the detection of 15 common missense, synonymous and intronic G6PD mutations in Asian populations. The identified mutations were subjected to biochemical and structural characterisation to understand the molecular mechanisms underlying enzyme deficiency. Results Based on phenotypic testing, the prevalence of G6PD deficiency (T) and intronic (c.1365-13T>C and c.486-34delT) mutations was detected with intermediate to normal enzyme activity. The double missense mutations were less catalytically active than their corresponding single missense mutations, resulting in severe enzyme deficiency. While the mutations had a minor effect on binding affinity, structural instability was a key contributor to the enzyme deficiency observed in G6PD-deficient individuals. Conclusions With varying degrees of enzyme deficiency, G6PD genotyping can be used as a complement to phenotypic screening to identify those who are eligible for 8-aminoquinolines. The information gained from this study could be useful for management and treatment of malaria, as well as for the prevention of unanticipated reactions to certain medications and foods in the studied population.

Selecting appropriate input variables for developing a rainfall prediction model is significantly difficult. The present study proposed an innovative framework for input variable selection (IVS) model bootstrapped long short-term recurrent neural network (BTSP-LSTM-RNN) to identify relevant variables for monthly rainfall forecasting. Monthly meteorological and large-scale climatic variables (LCVs) from 1993 to 2022 at two selected river basins in the northern region of Thailand were used for model development. The proposed BTSP-LSTM-RNN model results were compared with the support vector regression with recursive feature elimination (SVR-RFE) and Gradient boosting (GB) by statistical metrics such as coefficient of determination (R2), mean absolute error (MAE), relative root mean squared error (RRMSE), Pearson’s correlation coefficient (r) and mean absolute percentage error (MAPE). BTSP-LSTM-RNN demonstrated exceptional performance, boasting a higher R2 (0.84), MAE (92.28), RRMSE (10.36) in the Wang basin, and R2 (0.83), MAE (242.60), RRMSE (9.93) in the Nan basin. BTSP-LSTM-RNN also achieved the lowest MAPE of 29.82%. Based on this IVS model results, two input variable combinations (IVCs) were designed. IVC-1 is based on BTSP-LSTM-RNN selection, and IVC-2 is an original set of variables. LSTM-RNN, multi-layer perceptron artificial neural network (MLP-ANN), and ensemble model with bootstrapping on random forest (RF) were employed for monthly prediction. When BTSP-LSTM-RNN's selected input variables from IVC-1 are utilized, the BSTP-RF model demonstrates robust performance. It achieves a high R2 (0.82), a low RRMSE (10.13%) suggests accurate predictions, and the r of 0.91 further supports the model's strong linear relationship with observed rainfall data. Based on prediction model results, the BTSP-LSTM-RNN (IVS) model plays a pivotal role in the selection of input variables for rainfall forecasting and its impact on the performance of prediction models (BSTP-RF, MLP-ANN, and LSTM-RNN). These results consistently underscored the pivotal role of the BTSP-LSTM-RNN IVS model in enhancing the precision and reliability of rainfall predictions.

Abstract Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms.

The demand for rice varieties with lower amylose content (AC) is increasing in Southeast Asia, primarily due to their desirable texture and cooking qualities. This study presents the development of whole-grain rice lines with low to intermediate glycemic index (GI) and reduced AC. We selected six rice lines for in vivo GI assessment based on their starch properties. We successfully identified two lines with low AC that exhibited low and intermediate GI values, respectively. Our findings indicate that dietary fiber (DF) content may significantly influence rice GI. The selected whole-grain low-GI line showed a higher ratio of soluble dietary fiber (SDF) to insoluble dietary fiber (IDF) compared to control varieties, highlighting SDF’s potential positive role in lowering whole-grain rice’s GI. This study underscores the feasibility of developing rice varieties with desirable agronomic traits, nutritional traits, and culinary attributes, particularly for individuals managing their blood sugar levels. Additionally, we proposed the positive role of starch composition, especially DF content, in modulating the GI of rice. This study reinforces the importance of incorporating starch properties and DF content into rice breeding programs to produce more health-oriented and marketable rice varieties.

Cassava mosaic disease (CMD) is found in many countries around the world. This disease is caused by a mosaic virus carried by whitefly. It hurts the growth and yield of cassava, which in turn causes damage to farmers who grow cassava. In this paper, we present a model for the transmission of CMD dynamics by the ordinary differential equation system and recommend the optimal control for this model when controlling the disease by uprooting diseased cassava and spraying insecticides. For this model analysis, we are given the basic reproductive number R0, which is the threshold number for classifying the disease-free equilibrium point when R0 < 1 and the endemic equilibrium point when R0 > 1 by using the next-generation method. Disease-free equilibrium points and endemic equilibrium points have found conditions of stability. Sensitivity analysis of basic reproductive numbers reveals the impact of the parameters on disease outbreaks. Then, the model is modified to an optimal control problem with two optimal control parameters, in which the goal is to reduce cassava infections to a minimum. The necessary conditions for optimal control of disease were created by Pontryagin’s maximum principle. Numerical simulations are shown to demonstrate the effectiveness of the control system in the final section.

Geophysical domains typically exhibit intricate, irregular boundaries characterized by fractal-like geometries, while underlying physical processes operate across a broad spectrum of spatial scales. The challenge lies in generating spatial discretization of these domains that conform to their geographical constraints, utilizing anisotropic, fully adaptive meshes. This problem is compounded by the vast range of scales and a notably heterogeneous parameter space. Current methodologies often rely on ad hoc, model-specific, or application-dependent approaches, which lack comprehensive descriptions. Consequently, the development of new spatial domains is labor-intensive, prone to errors, challenging to replicate, and difficult to maintain consistency due to substantial human involvement. This predicament poses obstacles to the reproducibility of simulations and the establishment of provenance in data handling and model initialization, and it hinders rigorous model intercomparisons. Furthermore, the likelihood of discrepancies in model initialization and forcing parameters increases when employing flexible adaptive meshes. This paper introduces a systematic approach to the automated generation of adaptive meshes for geophysical models. This method is efficient in its generation process and readily reproducible, offering robust and consistent adherence to the source data. The proposed approach facilitates research in complex multi-scale geophysical domains, which would be challenging using existing methods. A simulation of monthly mean currents was carried out as a case study in the Gulf of Thailand. Results revealed that the simulated current circulations agreed with the observation. Examples of its application in various ongoing geophysical modeling endeavors illustrate its effectiveness.

Typhoid fever remains a major public health concern, affecting over nine million individuals globally each year. Mathematical modeling approaches can provide valuable insights into typhoid transmission dynamics and inform preventive strategies. In this study, we developed a compartmental model incorporating key features of typhoid epidemiology and two crucial interventions: vaccination and sanitation practices. The model stratifies the population into susceptible, vaccinated, exposed, asymptomatic infected, symptomatic infected, and recovered compartments and tracks the bacterial load in the environment. We established a disease-free equilibrium and basic reproduction number R0. We also identified the endemic equilibrium and analyzed its existence. Numerical simulations demonstrated the critical impact of enhanced sanitation and vaccination in curtailing infections. Our model underscores the need for multifaceted control measures that encompass vaccine coverage, sanitation enforcement, and healthcare capacity building to mitigate typhoid in high-risk regions. This study provides a comprehensive framework to model the intricate transmission dynamics of typhoid fever, supporting informed public health policies and decision-making.

Introduction Several prevalence-based cost-of-illness (COI) studies have been conducted to estimate the economic burden of alcohol consumption borne by a particular society in a given year. Yet there are few studies examining the economic costs incurred by an individual drinker over his/her lifetime. Thus, this study aims to estimate the costs incurred by an individual drinker’s alcohol consumption over his or her lifetime in Thailand.Methods and analysis An incidence-based COI approach will be employed. To project individuals’ associated costs over a lifetime, a Markov modelling technique will be used. The following six alcohol-related diseases/conditions will be considered in the model: hypertension, haemorrhagic stroke, liver cirrhosis, liver cancer, alcohol use disorders and road injury. The analysis will cover both direct (ie, direct healthcare cost, costs of property damage due to road traffic accidents) and indirect costs (ie, productivity loss due to premature mortality and hospital-related absenteeism). The human capital approach will be adopted to estimate the cost of productivity loss. All costs will be presented in Thai baht, 2022.Ethics and dissemination The Institutional Review Board of Mahidol University, Faculty of Dentistry/Faculty of Pharmacy has confirmed that no ethical approval is required (COE.No.MU-DT/PY-IRB 2021/010.0605). Dissemination of the study findings will be carried out through peer-reviewed publications, conferences and engagement with policy-makers and public health stakeholders.

Abstract Parasitic roundworms cause significant sickness and mortality in animals and humans. In livestock, these nematodes have severe economic impact and result in losses in food production on a global scale. None of the currently available drugs ideally suit all treatment circumstances, and the development of drug-resistant nematode strains has become a challenge to control the infection. There is an urgent need to develop novel anthelmintic compounds. According to our previous report, N-methylbenzo[d]oxazol-2-amine (1) showed anthelmintic activity and lowest cytotoxicity. In this study, in vivo anthelmintic properties were evaluated using Trichinella spiralis infected mice. Toxicity was evaluated using the rats and mode of action using molecular docking and metabolomics approaches. The in vivo results demonstrate that a dose of 250 mg/kg reduced the T. spiralis abundance in the digestive tract by 49%. The 250 mg/kg Albendazole was served as control. The relatively low acute toxicity was categorized into chemical category 5, with an LD50 greater than 2000 mg/kg body. Molecular docking analysis showed the T. spiralis tubulin beta chain and glutamate-gated channels might not be the main targets of compound 1. Metabolomics analysis was used to explain the effects of compound 1 on the T. spiralis adult worm. The results demonstrated that compound 1 significantly up-regulated the metabolism of purine, pyrimidine and down-regulated sphingolipid metabolism. In conclusion, compound 1 could be a potential molecule for anthelmintic development. The bioavailability, pharmacokinetics, and absorption of this compound should be studied further to provide information for its future efficacy improvement.

Abstract Background Alcohol is one of the leading risk factors contributing to avoidable economic loss worldwide. Estimates from the economic cost of alcohol consumption studies play an important role in prioritizing healthcare resource use, supporting policy decisions, and justifying budgets for alcohol policy. This study aimed to estimate the economic burden associated with alcohol consumption in Thailand in 2021. Methods Prevalence-based cost of illness methodology was employed. The following costs were included in the analysis: healthcare costs; cost of law enforcement; cost of property damage due to road traffic accidents; cost of premature mortality; and cost of absenteeism from out-patient hospital visits and hospitalization. Human capital approach was adopted. All costs were presented in Thai baht, 2021. Results Alcohol consumption incurred a total estimated economic cost of 165,450.5 million baht, equivalent to 1.02% of Gross Domestic Product (GDP) and 2500 baht per capita. Cost of premature mortality was estimated at 157,918.7 million baht and accounted for the largest proportion of the total cost (95.45%). Healthcare cost was found to be the second highest share representing 4370.1 million baht (2.7% of the total cost). The number of premature death attributable to alcohol consumption in 2021 was estimated at 22,804. Conclusions Alcohol continues to impose a substantial economic burden in Thailand. Enforcement of existing well-formulated alcohol control policies is urgently required to mitigate the economic impact of alcohol consumption in the country.

Abstract Aberrant autophagic activity is observed in osteoarthritic joints. Vitamin D was shown to alleviate not only osteoarthritis severity, but also autophagy process. However, the influence of vitamin D on autophagy in knee osteoarthritis (KOA) remains ambiguous. This study aimed to determine the effect of vitamin D2 on serum levels of autophagosome protein LC3A in patients with KOA and whether LC3A levels were correlated with serum 25‐hydroxyvitamin D (25(OH)D) and clinical outcomes of patients with KOA. A total of 165 patients with KOA and 25 healthy controls were recruited. Vitamin D2 (ergocalciferol) was administered to patients with KOA at a weekly dosage of 40,000 IU. Serum LC3A, knee pain and functional scores, muscle strength, physical performance, and biochemical parameters were examined before and after 6 months of vitamin D2 supplementation. Serum LC3A levels were significantly higher in patients with KOA than healthy controls. In patients with KOA, vitamin D2 supplementation significantly decreased serum LC3A levels. Furthermore, baseline levels of serum LC3A were significantly associated with radiographic severity, pain and functional scores, total cholesterol, hs‐CRP, IL‐6, protein carbonyl, and serum 25(OH)D. After adjusting for established confounders, independent relationships among serum LC3A and radiographic severity, pain and functional scores, total cholesterol, hs‐CRP, IL‐6, protein carbonyl, and serum 25(OH)D were also observed. Vitamin D2 supplementation was shown to not only decrease serum levels of LC3A, inflammatory markers, as well as oxidative stress, but also improve muscle strength and physical performance in patients with KOA.

Abstract Glucose-6-phosphate dehydrogenase (G6PD) deficiency is one of the most common enzymopathies in humans, present in approximately half a billion people worldwide. More than 230 clinically relevant G6PD mutations of different classes have been reported to date. We hereby describe a patient with chronic hemolysis who presents a substitution of arginine by glycine at position 219 in G6PD protein. The variant was never described in an original publication or characterized on a molecular level. In the present study, we provide structural and biochemical evidence for the molecular basis of its pathogenicity. When compared to the wild-type enzyme, the Arg219Gly mutation markedly reduces the catalytic activity by 50-fold while having a negligible effect on substrate binding affinity. The mutation preserves secondary protein structure, but greatly decreases stability at higher temperatures and to trypsin digestion. Size exclusion chromatography elution profiles show monomeric and dimeric forms for the mutant, but only the latter for the wild-type form, suggesting a critical role of arginine 219 in G6PD dimer formation. Our findings have implications in the development of small molecule activators, with the goal of rescuing the phenotype observed in this and possibly other related mutants.

Abstract Background Multiple sclerosis is an inflammatory demyelination process in the central nervous system (CNS) causing neurological disability and poor quality of life. Currently, Thai Food and Drug Administration (FDA)-approved disease-modifying therapy is costly, and most patients with multiple sclerosis are ineligible for treatment in Thailand as previous studies have challenged its cost-effectiveness. Off-label use of rituximab is inexpensive and highly effective in treating multiple sclerosis, but evidence of its cost-effectiveness in Thailand is yet to be collected. Methods This study aimed to evaluate the cost-utility and budget impact of rituximab for multiple sclerosis treatment compared with best supportive care, the standard practice in Thailand to treat the disease. A Markov model with a one-month cycle length and lifetime horizon was applied to compare the costs and outcomes of rituximab and best supportive care based on a societal perspective. Accordingly, incremental cost-effectiveness ratios were estimated. Probabilistic and one-way sensitivity analyses were conducted to investigate parameter uncertainty. In addition, the Markov model was used to assess the 5-year budget impact from the government perspective. Results A rituximab biosimilar demonstrated higher effectiveness and lower associated costs, compared to best supportive care, with the highest probability of being cost-effective (96%). The probability of relapse was the most sensitive parameter according to the one-way sensitivity analysis. The calculated budget impact of treating patients with multiple sclerosis in Thailand was 26,360,000 Thai baht (THB) or 844,255 United States dollars (USD) in the first fiscal year, and approximately 20,810,000–23,080,000 THB (666,608–739,388 USD) in the next four fiscal years. Conclusion In Thailand, a rituximab biosimilar would reduce the overall costs of multiple sclerosis treatment and should, therefore, be included in the National List of Essential Medicines.

Abstract Despite the availability of three network meta-analyses (NMA) examining the efficacy, treatment completion, and adverse events associated with all latent tuberculosis infection (LTBI) treatments, there is currently no evidence to support the notion that the benefits of these treatments outweigh the potential risks. This NMA aimed to conduct a comprehensive comparison and update of the efficacy, treatment completion rates and adverse events associated with recommended treatment options for LTBI for individuals with confirmed LTBI, as outlined in the 2020 World Health Organization (WHO) Consolidated Guidelines for TB preventive treatment. A comprehensive search of the MEDLINE and Scopus databases was conducted until April 2023. The NMA was applied to estimate the risk difference and corresponding 95% confidence interval (CI) using a combination of direct and indirect evidence. The risk–benefit assessment was employed to evaluate the feasibility of the extra benefits in relation to the extra risks. The primary outcomes of interest in this study were active TB disease, completion rates, and adverse events. The meta-analysis incorporated data from 15 studies, which collectively demonstrated that the administration of a placebo resulted in a significant increase in the risk of developing TB disease by 1.279%, compared to the daily intake of isoniazid for 6 months (6H). Furthermore, treatment completion rates were significantly higher when using isoniazid plus rifapentine weekly for 3 months (3HP) and rifampicin daily for 4 months (4R), as compared to 6H. Considering adverse events, the combination of 3HP, 4R, and isoniazid administered daily for 9 months (referred to as 9H) significantly decreased adverse events by 4.53% in comparison to 6H. The risk–benefit assessment showed that alternative treatment regimens (9H, 4R, 3HR and 3HP) had a lower incidence of adverse events, while demonstrating a higher efficacy in preventing TB, as compared to 6H. This review indicates that there were no significant differences observed among various active treatment options in terms of their efficacy in preventing active TB. Moreover, completion rates were higher in 3HP and 4R, and a reduction in adverse events was observed in 3HP, 4R, and 9H.

Abstract In recent years, COVID-19 has evolved into many variants, posing new challenges for disease control and prevention. The Omicron variant, in particular, has been found to be highly contagious. In this study, we constructed and analyzed a mathematical model of COVID-19 transmission that incorporates vaccination and three different compartments of the infected population: asymptomatic $$(I_{a})$$ ( I a ) , symptomatic $$(I_{s})$$ ( I s ) , and Omicron $$(I_{m})$$ ( I m ) . The model is formulated in the Caputo sense, which allows for fractional derivatives that capture the memory effects of the disease dynamics. We proved the existence and uniqueness of the solution of the model, obtained the effective reproduction number, showed that the model exhibits both endemic and disease-free equilibrium points, and showed that backward bifurcation can occur. Furthermore, we documented the effects of asymptomatic infected individuals on the disease transmission. We validated the model using real data from Thailand and found that vaccination alone is insufficient to completely eradicate the disease. We also found that Thailand must monitor asymptomatic individuals through stringent testing to halt and subsequently eradicate the disease. Our study provides novel insights into the behavior and impact of the Omicron variant and suggests possible strategies to mitigate its spread.

The recent outbreak of rabies virus has affected numerous individuals in the community, highlighting the importance of studying the disease mathematically in epidemiology. In this paper, we develop a mathematical model for the spread of rabies disease using the harmonic mean incidence rate and determine the reproduction number R0 using the next generation matrix approach. The fractional dynamics of the model incorporate the interaction between infected individuals and environmental factors. We analyze the model using both Atangana-Baleanu-Caputo (ABC) and Caputo-Fabrizio (CF) techniques, drawing on both modern and classical approaches. For qualitative investigation using fractional operators, we utilize the Banach fixed-point theory and derive the Hyers-Ulam stability concept. We assign values to the model parameters and utilize the Newton interpolation technique to obtain a numerical scheme. Additionally, we perform sensitivity analysis on the model. In conclusion, our findings provide important insights for the study of epidemics.

Abstract Thailand is known to be endemic for leptospirosis. This bacterium may pose a potential risk to transfusion safety. This study was a cross-sectional study examining the seroprevalence of leptospirosis among Thai blood donors. A total of 1053 serum specimens collected from blood donors residing in 5 regions of Thailand during March to September 2020 were included in this study. All samples were tested for the presence of antibodies to 22 leptospiral serovars using the microscopic agglutination test (MAT) and anti-Leptospira IgG antibodies using commercially available enzyme immunoassay. We found no evidence of recent exposure to Leptospira spp. in sera of healthy Thai blood donors by MAT, including those in higher-risk areas. However, in this same group, we did find small numbers of past exposure (1.7%) to Leptospira spp. by IgG ELISA. According to the findings of this study, there is currently no evidence for implementing new blood banking procedures to identify possible carriers in Thailand, however these should be continually monitored and revised according to the infectious disease burden in each country. It should be noted that there was a difference in the occupation rate between the general population reported in Thailand and blood donors in this study; it may not reflect the actual situation in the country.

The 6th Asia Dengue Summit (ADS) themed "Road Map to Zero Dengue Death" was held in Thailand from 15th-16th June 2023. The summit was hosted by Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand in conjunction with Queen Saovabha Memorial Institute, The Thai Red Cross Society; Faculty of Tropical Medicine, Mahidol University; and the Ministry of Public Health. The 6th ADS was convened by Asia Dengue Voice and Action (ADVA); Global Dengue and Aedes Transmitted Diseases Consortium (GDAC); Southeast Asian Ministers of Education Tropical Medicine and Public Health Network (SEAMEO TROPMED); Fondation Mérieux (FMx) and the International Society for Neglected Tropical Diseases (ISNTD). Dengue experts from academia and research, and representatives from the Ministries of Health, Regional and Global World Health Organization (WHO) and International Vaccine Institute (IVI) participated in the three-day summit. With more than 51 speakers and 451 delegates from over 24 countries, 10 symposiums, and 2 full days, the 6th ADS highlighted the growing threat of dengue and its antigenic evolution, flagged the urgent need to overcome vaccine hesitancy and misinformation crisis, and focused on dengue control policies, newer diagnostics, therapeutics and vaccines, travel-associated dengue, and strategies to improve community involvement.

Plant diseases caused by spores can cause severe damage to crop production, resulting in yield losses of up to 75%. Spores can be carried over long distances by wind, water, insects, and humans, meaning that even one infected agricultural field can spread the disease to neighboring fields. Although mathematical models exist to describe the spread of plant diseases, they often do not take into account the transport and location of spores, which limits the ability to make spatial forecasts. To address this limitation, we developed a spatial model based on Healthy sites H, Latent sites L, Infected sites I, Removed sites R, and spore sites X (HLIRX), which describes the spore movement between fields, allowing us to simulate the spatial invasion of plant diseases. We analyzed the existence and stability of steady states in the model using the finite difference method. Our findings reveal that the disease-free equilibrium is unstable, while the boundary and endemic equilibria are asymptotically stable, depending on the spore dispersion term. Moreover, we observed that the dynamics of the population in each field exhibit a consistent pattern, with leftward or rightward shifts depending on the distance from the source of infection, which cannot be captured from the temporal model. We also investigated the impact of two spatial parameters, the dispersal kernel parameter and spore migration rate, and found that both parameters lead to an increase in the number of infected plants but have no significant effect on disease severity. In addition, we discovered that the time until the infected population peaks is linearly related to the distance between each field and the source of infection, with a Pearson correlation coefficient greater than 0.99 (p < 0.001). Our developed model provides a useful tool for studying the spatial dispersion of plant diseases distributed by spores, as it considers the transport and location of spores and can be used to make spatial forecasts.

Several hepatitis A outbreaks have recently been reported in Kerala state, India. To inform coverage decision of hepatitis A vaccine in Kerala, this study aimed to examine the cost-effectiveness of 1) hepatitis A vaccination among children aged 1 year and individuals aged 15 years, and 2) serological screening of individuals aged 15 years and vaccination of susceptible as compared to no vaccination or vaccination without serological screening. Both live attenuated hepatitis A vaccine and inactivated hepatitis A vaccine were considered in the analysis. A combination of decision tree and Markov models with a cycle length of one year was employed to estimate costs and benefits of different vaccination strategies. Analysis were based on both societal and payer perspectives. The lifetime costs and outcomes were discounted by 3%. Our findings indicated that all strategies were cost-saving for both societal and payer perspectives. Moreover, budget impact analysis revealed that vaccination without screening among individuals aged 15 years could save the government's budget by reducing treatment cost of hepatitis A. Our cost-effectiveness evidence supports the inclusion of hepatitis A vaccination into the vaccination program for children aged 1 year and individuals aged 15 years in Kerala state, India.

This paper presents an analysis and numerical simulation of financial crime population dynamics using fractional order calculus and Newton's polynomial. The dynamics of financial crimes are modeled as a fractional-order system, which is then solved using numerical methods based on Newton's polynomial. The results of the simulation provide insights into the behavior of financial crime populations over time, including the stability and convergence of the systems. The study provides a new approach to understanding financial crime populations and has potential applications in developing effective strategies for combating financial crimes. Fractional derivatives are commonly applied in many interdisciplinary fields of science because of its effectiveness in understanding and analyzing complicated phenomena. In this work, a mathematical model for the population dynamics of financial crime with fractional derivatives is reformulated and analyzed. A fractional-order financial crime model using the new Atangana-Baleanu-Caputo (ABC) derivative is introduced. The reproduction number for financial crime is calculated. In addition, the relative significance of model parameters is also determined by sensitivity analysis. The existence and uniqueness of the solution in consideration of the ABC derivative are discussed. A number of conditions are established for the existence and Ulam-Hyers stability of financial crime equilibria. A numerical scheme is presented for the proposed model, starting with the Caputo-Fabrizio fractional derivative, followed by the Caputo and Atangana-Baleanu fractional derivatives. Finally, we solve the models with fractal-fractional derivatives.

This study explores the use of numerical simulations to model the spread of the Omicron variant of the SARS-CoV-2 virus using fractional-order COVID-19 models and Haar wavelet collocation methods. The fractional order COVID-19 model considers various factors that affect the virus's transmission, and the Haar wavelet collocation method offers a precise and efficient solution to the fractional derivatives used in the model. The simulation results yield crucial insights into the Omicron variant's spread, providing valuable information to public health policies and strategies designed to mitigate its impact. This study marks a significant advancement in comprehending the COVID-19 pandemic's dynamics and the emergence of its variants. The COVID-19 epidemic model is reworked utilizing fractional derivatives in the Caputo sense, and the model's existence and uniqueness are established by considering fixed point theory results. Sensitivity analysis is conducted on the model to identify the parameter with the highest sensitivity. For numerical treatment and simulations, we apply the Haar wavelet collocation method. Parameter estimation for the recorded COVID-19 cases in India from 13 July 2021 to 25 August 2021 has been presented.

The genetic diversity within the circumsporozoite surface protein (PvCSP) of Plasmodium vivax, the predominant malaria species in Thailand, is primarily observed in the northwestern region along the Thailand–Myanmar border. However, as P. vivax cases shift to southern provinces, particularly Yala Province near the Thailand–Malaysia border, PvCSP diversity remains understudied. Between 2018 and 2020, 89 P. vivax isolates were collected in Yala Province, a significant malaria hotspot. Employing polymerase chain reaction amplification, restriction fragment length polymorphism (PCR-RFLP), and DNA sequencing, the gene encoding PvCSP (Pvcsp) was analyzed. All Yala P. vivax isolates belonged to the VK210 type, distinct from strains in the western region near the Myanmar border. The central repeat region of Pvcsp revealed two common peptide repeat motifs—GDRADGQPA and GDRAAGQPA—across all southern isolates. Sequence analysis identified two subtypes, with S1 more prevalent (92%) than S2 (8%). This study underscores the limited diversity of VK210 variants of P. vivax populations in southern Thailand. These baseline findings facilitate monitoring for potential new parasite variants, aiding in the future control and management of P. vivax in the region.

Handling missing values is a critical component of the data processing in hydrological modeling. The key objective of this research is to assess statistical techniques (STs) and artificial intelligence-based techniques (AITs) for imputing missing daily rainfall values and recommend a methodology applicable to the mountainous terrain of northern Thailand. In this study, 30 years of daily rainfall data was collected from 20 rainfall stations in northern Thailand and randomly 25–35 % of data was deleted from four target stations based on Spearman correlation coefficient between the target and neighboring stations. Imputation models were developed on training and testing datasets and statistically evaluated by mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and correlation coefficient (r). This study used STs, including arithmetic averaging (AA), multiple linear regression (MLR), normal-ratio (NR), nonlinear iterative partial least squares (NIPALS) algorithm, and linear interpolation was used. • STs results were compared with AITs, including long-short-term-memory recurrent neural network (LSTM-RNN), M5 model tree (M5-MT), multilayer perceptron neural networks (MLPNN), support vector regression with polynomial and radial basis function SVR-poly and SVR-RBF. • The findings revealed that MLR imputation model achieved an average MAE of 0.98, RMSE of 4.52, and R2 was about 79.6 % at all target stations. On the other hand, for the M5-MT model, the average MAE was 0.91, RMSE was about 4.52, and R2 was around 79.8 % compared to other STs and AITs. M5-MT was most prominent among AITs. Notably, the MLR technique stood out as a recommended approach due to its ability to deliver good estimation results while offering a transparent mechanism and not necessitating prior knowledge for model creation.

BackgroundIn patients with type 2 diabetes (T2D) and a history of heart failure (HF), sodium–glucose cotransporter-2 inhibitors (SGLT2is) have demonstrated cardiovascular (CV) benefits. However, the comparative efficacy of individual SGLT2is remains uncertain. This network meta-analysis (NMA) compared the efficacy and safety of five SGLT2is (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin, and sotagliflozin) on CV outcomes in these patients.Materials and methodsPubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched up to September 23, 2022, to identify all randomized controlled trials (RCTs) comparing SGLT2is to placebo in T2D patients with HF. The main outcomes included composite CV death/heart failure hospitalization (HFH), HFH, CV death, all-cause mortality, and adverse events. Pairwise and NMA approaches were applied.ResultsOur analysis included 11 RCTs with a total of 20,438 patients with T2D and HF. All SGLT2is significantly reduced HFH compared to standard of care (SoC) alone. “Add-on” SGLT2is, except ertugliflozin, significantly reduced composite CV death/HFH relative to SoC alone. Moreover, canagliflozin had lower composite CV death/HFH compared to dapagliflozin. Based on the surface under the cumulative ranking curve (SUCRA), the top-ranked SGLT2is for reducing HFH were canagliflozin (95.5%), sotagliflozin (66.0%), and empagliflozin (57.2%). Head-to-head comparisons found no significant differences between individual SGLT2is in reducing CV death. “Add-on” SGLT2is reduced all-cause mortality compared with SoC alone, although only dapagliflozin was statistically significant. No SGLT2is were significantly associated with serious adverse events. A sensitivity analysis focusing on HF-specific trials found that dapagliflozin, empagliflozin, and sotagliflozin significantly reduced composite CV death/HFH, consistent with the main analysis. However, no significant differences were identified from their head-to-head comparisons in the NMA. The SUCRA indicated that sotagliflozin had the highest probability of reducing composite CV death/HFH (97.6%), followed by empagliflozin (58.4%) and dapagliflozin (44.0%).ConclusionSGLT2is significantly reduce the composite CV death/HFH outcome. Among them, canagliflozin may be considered the preferred treatment for patients with diabetes and a history of heart failure, but it may also be associated with an increased risk of any adverse events compared to other SGLT2is. However, a sensitivity analysis focusing on HF-specific trials identified sotagliflozin as the most likely agent to reduce CV death/HFH, followed by empagliflozin and dapagliflozin.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022353754.

Host genetic polymorphisms are recognized as a critical determinant of diversity in clinical symptoms of Coronavirus disease 2019 (COVID-19). Accordingly, this study aimed to determine possible associations between single nucleotide polymorphisms (SNPs) in 37 candidate genetic variants and clinical consequences of COVID-19 – especially long-term symptoms, Long COVID. A total of 260 COVID-19 patients, divided into mild (n = 239) and severe (n = 21) and further categorized based on the presence of Long COVID (no, n = 211; yes, n = 49), were recruited. Genotyping of selected polymorphisms responsible for viral entry, immune response, and inflammation was performed using MassARRAY system. Out of 37 SNPs, 9 including leucine zipper transcription factor like-1 (LZTFL1) rs10490770 C allele, LZTFL1 rs11385942 dupA allele, nicotinamide adenine dinucleotide synthetase-1 (NADSYN1) rs12785878 TT genotype, plexin A-4 (PLXNA4) rs1424597 AA genotype, LZTFL1 rs17713054 A allele, interleukin-10 (IL10) rs1800896 TC genotype and C allele, angiotensin converting enzyme-2 (ACE2) rs2285666 T allele, and plasmanylethanolamine desaturase-1 (PEDS1) rs6020298 GG genotype and G allele were significantly associated with an increased risk of developing Long COVID, whereas interleukin-10 receptor subunit beta (IL10RB) rs8178562 GG genotype was significantly associated with a reduced risk of Long COVID. Kaplan-Meier curve displayed that the above gene polymorphisms were significantly associated with cumulative rate of Long COVID occurrence. Polymorphisms in LZTFL1 rs10490770, LZTFL1 rs11385942, LZTFL1 rs17713054, NADSYN1 rs12785878, PLXNA4 rs1424597, IL10 rs1800896, ACE2 rs2285666, PEDS1 rs6020298, and IL10RB rs8178562 appear to be genetic factors involved in development of Long COVID.

We developed a dengue epidemic model by considering hospitalized class and harmonic mean incidence rate. A qualitative study of the proposed model was conducted. The Basic reproduction number, and local and global stability are established. The highly dominant parameters on basic reproduction number R0 have been found by sensitivity analysis. NSFD and RK-4 schemes are used for numerical solutions. Furthermore, this manuscript considers the novel fractional-order operator developed by Atangana-Baleanu for transmission dynamics of the Dengue epidemic. Assuming the importance of the non-local Atangana-Baleanu fractional-order approach, the transmission mechanism of Dengue has been investigated while taking into account different phases of infection and various transmission routes of the disease. To conduct the proposed study, first of all, we shall formulate the model by using the classical operator of ordinary derivatives. We utilize the fractional order derivative and the model will be extended to a model containing fractional order derivatives. The operator being used is the fractional differential operator and has fractional order Φ1. The approach of newton’s polynomial is considered and a new numerical scheme is developed which helped in presenting an iterative process for the proposed ABC system. Based on this scheme, sample curves are obtained for various values of Φ1 and a pattern is derived between the dynamics of the infection and the order of the derivative.

Leaf brown spot, caused by fungi, is a terrible plant disease, and it can significantly reduce the quality and quantity of rice. In this paper, we developed the model based on leaf brown spot disease development and considered a preventive treatment using botanical fungicide. In addition, we develop a model with suitable optimal control strategies. The result shows disease-free equilibrium is asymptotically stable when $R_0>1$. In contrast, the endemic equilibrium is asymptotically stable when $R_0 > 1$. The obtained optimal control to can reduce the number of infected plants compared to that without control. In addition, the analytical results were confirmed by numerical simulations of the occurrence of the theoretical results.

Cassava mosaic disease (CMD) is caused by a virus transmitted by the whitefly. This disease can destroy cassava at any stage of its growth and it resulted in lower cassava yields. In this paper, we developed a mathematical model for the epidemic of cassava mosaic disease with a deterministic model which has saturation incidence rates. This model aims to explain the effect of vectors on cassava disease outbreaks. First, this model was analyzed using standard dynamic methods to determine the behavior of the solution. We found the existence and condition of disease-free and endemic steady state. The basic reproductive number ($ R_0 $) is obtained by using the next-generation method which $ R_0 $ helps assess the ability to spread infectious diseases. Second, the stability of the steady state was analyzed, then we obtain the condition of existence of local stability and global stability at each steady state of this model. Third, analysis of the sensitivity indices in the threshold number to determine the effect of the various parameters. Finally, the results of the theoretical model were validated by numerical simulations. It is represented by various graphs converging at a steady state and stable.

This paper aims to extend the spore dispersal model to the Healthy-Latent-Infectious-Removed (HLIR) epidemic model for assessing the severity of rice blast disease. The model was solved by the Finite Difference Method (FDM). The results of the model were compared to data from the Prachinburi Rice Research Center (PRRC) on the severity of rice blast disease. Because of a small error, the comparison results showed good agreement between the PRRC data and the simulation by looking at the value of Willmott's index of agreement (d). The first bed d was 0.7166, while the second bed d was 0.6421, indicating the model's performance. Furthermore, the optimal parameter, the fraction of spores deposited on the crop, was determined to be 0.173 and 0.016 for beds 1 and 2, respectively. The model can simulate and analyze rice blast outbreaks for educational purposes in future preparedness planning.

BackgroundGnathostomiasis is an important zoonosis in tropical areas that is mainly caused by third-stage Gnathostoma spinigerum larvae (G. spinigerum L3).ObjectivesThis study aimed to prove whether G. spinigerum L3 produces extracellular vesicles (EVs) and investigate human gene profiles related to the immune response against the larvae.MethodsWe created an immune cell model using normal human peripheral blood mononuclear cells (PBMCs) co-cultured with the larvae for 1 and 3 days, respectively. The PBMCs were harvested for transcriptome sequencing analysis. The EV ultrastructure was examined in the larvae and the cultured medium.ResultsExtracellular vesicle-like particles were observed under the larval teguments and in the pellets in the medium. RNA-seq analysis revealed that 2,847 and 3,118 genes were significantly expressed on days 1 and 3 after culture, respectively. The downregulated genes on day 1 after culture were involved in pro-inflammatory cytokines, the complement system and apoptosis, whereas those on day 3 were involved in T cell-dependent B cell activation and wound healing. Significantly upregulated genes related to cell proliferation, activation and development, as well as cytotoxicity, were observed on day 1, and genes regulating T cell maturation, granulocyte function, nuclear factor-κB and toll-like receptor pathways were predominantly observed on day 3 after culture.ConclusionG. spinigerum L3 produces EV-like particles and releases them into the excretory-secretory products. Overall, genotypic findings during our 3-day observation revealed that most significant gene expressions were related to T and B cell signalling, driving T helper 2 cells related to chronic infection, immune evasion of the larvae, and the pathogenesis of gnathostomiasis. Further in-depth studies are necessary to clarify gene functions in the pathogenesis and immune evasion mechanisms of the infective larvae.

Objectives: This study aimed to investigate whether mitochondrial DNA (mtDNA) content, an index of mitochondrial dysfunction, was associated with clinical parameters indicating anti-tuberculosis (TB) drug-induced liver injury (ATDILI) in TB patients and could emerge as an ATDILI biomarker. Methods: Leukocyte mtDNA content in 102 TB patients (49 ATDILI cases and 53 non-ATDILI cases) and 100 age-matched healthy controls was measured using real-time polymerase chain reaction. Results: Compared with healthy controls, both TB patients with and without ATDILI had significantly decreased mtDNA content. Compared with the patients without ATDILI, mtDNA content was significantly increased in those with ATDILI. Higher mtDNA content was observed to be independently associated with increased susceptibility to ATDILI. Increased mtDNA content measured within 1-7 days of treatment was independently associated with elevated levels of serum aminotransferases assessed within 8-60 days of treatment. After initiating treatment within 1-7 days, mtDNA content was detected to be more sensitive and selective for differentiating TB patients with ATDILI from those without ATDILI than serum aminotransferases. Kaplan-Meier analysis revealed a significant correlation between elevated mtDNA content and increased rate of ATDILI occurrence in TB patients, attested by Cox regression analysis, adjusting for confounders. Conclusion: Changes in leukocyte mtDNA content would reflect ATDILI progression and could be used as a potential stratification tool for identifying TB patients at risk of ATDILI.