The escalating global spread of chikungunya virus, exemplified by the 2025 local outbreak in Guangdong Province, China, underscores the urgent need for effective vaccination strategies. This paper discusses advancements in chikungunya vaccine development through the lens of regulatory science. We reviewed recent progress in vaccine platforms, including the live-attenuated IXCHIQ^® and VLP-based Vimkunya^®, as well as the emerging mRNA candidates, highlighting their immunogenicity and safety profiles. We further addressed unique challenges in chikungunya vaccine development, such as viral genetic diversity, intermittent outbreaks, and ethical and practical barriers to conducting traditional efficacy trials. To overcome these, we proposed innovative regulatory strategies to accelerate approval, including: (1) surrogate endpoints based on neutralizing antibodies supported by animal challenge models; (2) streamlined chemistry, manufacturing, and controls and nonclinical/ clinical study requirements through platform technology leveraging; and (3) integrated lifecycle risk management with enhanced postmarketing surveillance. Drawing on experience with platform-based vaccine regulation, we emphasize the importance of regulatory agility, international collaboration, and science-driven policy to facilitate the rapid development and deployment of safe, effective, and broadly protective chikungunya vaccines against future arbovirus threats.

Objective: To identify the underexplored nature of localized, longterm spatiotemporal patterns (2014-2023) and predictive modeling feasibility in these regions. The analysis specifically explored the potential for robust forecasting of dengue incidence trends.

Methods: Dengue incidence trends were analyzed across five northeastern Thai provinces: Sisaket (SSK), Ubon Ratchathani (UBN), Yasothon (YST), Amnat Charoen (ACR), and Mukdahan (MDH) over a ten-year period (2014-2023). Analysis of Kruskal-Wallis H test followed by post hoc Dunn’s tests with Bonferroni adjustment were used for inter-provincial comparisons. Seasonal- Trend Decomposition using Loess (STL) and Seasonal Index calculations were applied to identify seasonal patterns, and Simple Linear Regression to assess the influence of monthly rainfall on dengue seasonality. Additionally, a negative binomial regression analysis was conducted, and spatial autocorrelation was investigated using Moran's I analysis.

Results: The analysis revealed significant spatiotemporal heterogeneity and distinct cyclical patterns in dengue incidence. Pronounced annual peaks consistently occurred during the rainy season (June-September), with major epidemics observed in 2015, 2019, and 2023. Inter-provincial comparisons revealed statistically significant differences in monthly dengue incidence across the study area (H=10.08, P=0.039). Post-hoc Dunn’s tests indicated that UBN and SSK had higher transmission burden compared to YST and MDH. Seasonal Index calculations confirmed July as the predominant overall peak month (indices 1.4-2.5), with minimal dengue activity from January to March. While rainfall significantly influenced seasonality across all provinces. A negative binomial regression analysis indicated that all of the variables included in the model had no statistically significant relationship with dengue incidence. Furthermore, a Moran's I analysis revealed a significant clustering effect only in YST (1=0.314, P=0.037).

Conclusions: The finding underscores the critical requirement for adopting a highly focused approach, necessitating a shift toward region-specific interventions to effectively combat dengue and sustain progress toward good health and well-being for all.

Objective: To evaluate the efficacy and safety of lamivudine and tenofovir in preventing mother-to-child transmission of hepatitis B virus (HBV) in pregnant women with chronic HBsAg positivity and high viral load.

Methods: A prospective, matched cohort study was conducted on pregnant women with high viral load (>10⁶ copies/mL) from March 2015 to March 2018 at two hospitals in Vietnam. Infants born to these women were followed up until January 2019. Data were collected on maternal HBV DNA load, organ function test results, and adverse reactions, as well as infant HBV markers at birth and at 1, 6, and 12 months of age. Data were analyzed between February and June 2019.

Results: A total of 80 pregnant women were enrolled and divided into lamivudine group (n=39) and tenofovir group (n=41). In the tenofovir group, the median maternal HBV DNA load decreased sharply from 7.2 (6.8-7.6) log₁₀ copies/mL before treatment to 4.4 (2.8-5.6) log₁₀ copies/ mL at delivery, which was a significantly greater reduction than that observed in the lamivudine group. The mean ALT and creatinine levels in both groups increased by no more than 2-fold compared with baseline, but remained within the normal range. The efficacy of preventing HBV mother-to-child transmission was similar between the two infant groups: 4.8% in the lamivudine group and 3.8% in the tenofovir group.

Conclusions: Tenofovir should be the preferred option for preventing mother-to-child transmission of hepatitis B virus in pregnant women with high HBV viral load. Future studies with larger sample sizes and additional control groups are warranted to strengthen the evidence and improve the quality of treatment recommendations.

Objective: To predict malaria outbreaks early and accurately using machine learning models when data presents challenges such as imbalance.

Methods: To predict the risk of malaria outbreaks, we applied six machine learning models—including naive Bayes, logistic regression, random forest, K-nearest neighbors, decision tree, and extreme gradient boosting—to a dataset of 8000 samples with 17 climatic and non-climatic features. To address class imbalance, we used the Synthetic Minority Over-sampling Technique algorithm. The models were evaluated using 10-fold cross-validation repeated 10 times to ensure robust validation.

Results: Extreme gradient boosting achieved the highest F1-score, improving from 0.926 on imbalanced data to 0.991 after balancing. While random forest, naive Bayes, and decision tree also improved their performance when balancing data with Synthetic Minority Over-sampling Technique (increasing F1-score from 0.893 to 0.931, from 0.765 to 0.962, and from 0.785 to 0.812, respectively), logistic regression and K-nearest neighbors did not show any improvement.

Conclusions: These findings demonstrate that integrating balanced climatic and non-climatic data with advanced machine learning can improve malaria outbreak risk prediction and support public health strategies.

Rationale: Nocardiosis is an uncommon but serious opportunistic infection in immunocompromised patients, particularly following haematopoietic stem cell transplantation (HSCT). Nocardia farcinica is notable for its virulence and tendency to cause disseminated disease.

Patient concerns: A female HSCT recipient presented several months post-transplant with progressive left shoulder pain unresponsive to conservative management.

Diagnosis: Magnetic resonance imaging revealed an abscess involving the acromioclavicular joint and surrounding musculature. Histopathology showed filamentous, branching Gram-positive bacilli, and microbiological identification confirmed Nocardia farcinica. Chest imaging demonstrated pulmonary involvement consistent with disseminated nocardiosis.

Interventions: Combination antimicrobial therapy with imipenem, amikacin, and trimethoprim-sulfamethoxazole was initiated, followed by prolonged oral therapy.

Outcomes: The patient achieved complete clinical and radiological resolution with no relapse.

Lessons: Early recognition and timely initiation of appropriate combination antimicrobial therapy are essential for favourable outcomes in disseminated nocardiosis after HSCT.