Background: Early diagnosis of dengue infection is crucial for better management of the disease. Diagnostic tests based on the detection of dengue virus (DENV) Non Structural Protein 1 (NS1) antigen are commercially available with different sensitivities and specificities observed in various settings. Dengue is endemic in Indonesia and clinicians are increasingly using the NS1 detection for dengue confirmation. This study described the performance of Panbio Dengue Early NS1 and IgM Capture ELISA assays for dengue detection during our surveillance in eight cities in Indonesia as well as the genetic diversity of DENV NS1 genes and its relationship with the NS1 detection.Methods: The NS1 and IgM/IgG ELISA assays were used for screening and confirmation of dengue infection during surveillance in 2010–2012. Collected serum samples (n = 440) were subjected to RT-PCR and virus isolation, in which 188 samples were confirmed for dengue infection. The positivity of the ELISA assays were correlated with the RT-PCR results to obtain the sensitivity of the assays. The NS1 genes of 48 Indonesian virus isolates were sequenced and their genetic characteristics were studied.Results: Using molecular data as gold standard, the sensitivity of NS1 ELISA assay for samples from Indonesia was 56.4% while IgM ELISA was 73.7%. When both NS1 and IgM results were combined, the sensitivity increased to 89.4%. The NS1 sensitivity varied when correlated with city/geographical origins and DENV serotype, in which the lowest sensitivity was observed for DENV-4 (19.0%). NS1 sensitivity was higher in primary (67.6%) compared to secondary infection (48.2%). The specificity of NS1 assay for non-dengue samples were 100%. The NS1 gene sequence analysis of 48 isolates revealed the presence of polymorphisms of the NS1 genes which apparently did not influence the NS1 sensitivity.Conclusions: We observed a relatively low sensitivity of NS1 ELISA for dengue detection on RT-PCR-positive dengue samples. The detection rate increased significantly when NS1 data was combined with IgM. In our study, the low sensitivity of NS1 antigen detection did not relate to NS1 genetic diversity. Rather, the performance of the NS1 antigen test was affected by the infection status of patients and geographical origin of samples.

Outbreaks of dengue virus (DENV) in Indonesia have been mainly caused by the DENV serotype-1; -2; or -3. The DENV-4 was the least-reported serotype in Indonesia during the last five decades. We recently conducted a molecular epidemiology study of dengue in the Jember regency, East Java province, Indonesia. Dengue is endemic in the region and outbreaks occur annually. We investigated the clinical characteristics and etiology of dengue-like febrile illness in this regency to understand the disease dynamics. A total of 191 patients with clinical symptoms similar to dengue were recruited during an 11-month study in 2019–2020. Children accounted for the majority of cases and dengue burden was estimated in 41.4% of the cases based on NS1 antigen, viral RNA, and IgG/IgM antibody detection with the majority (73.4%) being primary infections. Secondary infection was significantly associated with a higher risk of severe dengue manifestation. All four DENV serotypes were detected in Jember. Strikingly, we observed the predominance of DENV-4, followed by DENV-3, DENV-1, and DENV-2. Genotype determination using Envelope gene sequence revealed the classification into Genotype I, Cosmopolitan Genotype, Genotype I, and Genotype II for DENV-1, -2, -3, and -4, respectively. The predominance of DENV-4 in Jember may be associated with a new wave of DENV infections and spread in a non-immune population lacking a herd-immunity to this particular serotype.

Wolbachia-infected Aedes aegypti is the latest technology that was developed to eliminate dengue fever. The Ministry of Research and Technology of the Republic of Indonesia (Kemenristekdikti) established an expert group to identify future potential risks that may occur over a period of 30 years associated with the release of Wolbachia-infected Ae. aegypti. The risk assessment consisted of identifying different hazards that may have impacts on humans and the environment. From the consensus among the experts, there were 56 hazards identified and categorized into 4 components, namely, ecological matters, efficacy in mosquito management, economic and sociocultural issues, and public health standards. There were 19 hazards in the ecological group. The overall likelihood in the ecology of the mosquito is very low (0.05), with moderate consequence (0.74), which resulted in negligible risk. For the efficacy in mosquito management group, there were 12 hazards that resulted in very low likelihood (0.11) with high consequence (0.85). The overall risk for mosquito management efficacy was very low (0.09). There were 14 hazards identified in the public health standard with very low likelihood (0.07), moderate consequence (0.50) and negligible risk (0.04). Lastly, 13 hazards were identified in the economic and sociocultural group with low likelihood (0.01) but of moderate consequence (0.5), which resulted in a very low risk (0.09). The risk severity level of the four components leading to the endpoint risk of “cause more harm” due to releasing Wolbachia-infected Ae. aegypti is negligible (0.01).

A recent modeling study estimated that there could be as many as 20,000 human melioidosis cases per year in Indonesia, with around 10,000 potential deaths annually. Nonetheless, the true burden of melioidosis in Indonesia is still unknown. The Indonesia Melioidosis Network was formed during the first melioidosis workshop in 2017. Here, we reviewed 101 melioidosis cases (99 human and two animal cases) previously reported and described an additional 45 human melioidosis cases. All 146 culture-confirmed cases were found in Sumatra (n = 15), Java (n = 104), Kalimantan (n = 15), Sulawesi (n = 11) and Nusa Tenggara (n = 1). Misidentification of Burkholderia pseudomallei was not uncommon, and most cases were only recently identified. We also evaluated clinical manifestations and outcome of recent culture-confirmed cases between 2012 and 2017 (n = 42). Overall, 15 (36%) cases were children (age <15 years) and 27 (64%) were adults (age ≥15 years). The overall mortality was 43% (18/42). We conducted a survey and found that 57% (327/548) of healthcare workers had never heard of melioidosis. In conclusion, melioidosis is endemic throughout Indonesia and associated with high mortality. We propose that top priorities are increasing awareness of melioidosis amongst all healthcare workers, increasing the use of bacterial culture, and ensuring accurate identification of B. pseudomalleiand diagnosis of melioidosis.

A multiresponse multipredictor semiparametric regression (MMSR) model is a combination of parametric and nonparametric regressions models with more than one predictor and response variables where there is correlation between responses. Due to this correlation we need to construct a symmetric weight matrix. This is one of the things that distinguishes it from the classical method, which uses a parametric regression approach. In this study, we theoretically developed a method of determining a confidence interval for parameters in a MMSR model based on a truncated spline, and investigating asymptotic properties of estimator for parameters in a MMSR model, especially consistency and asymptotic normality. The weighted least squares method was used to estimate the MMSR model. Next, we applied a pivotal quantity method, a Cramer–Wold theorem, and a Slutsky theorem to determine the confidence interval, investigate consistency, and asymptotic normality properties of estimator for parameters in a MMSR model. The obtained results were that the estimated regression function is linear to observation. We also obtained a $100\left(1-\alpha \right)\%$ confidence interval for parameters in the MMSR model, and the estimator for parameters in MMSR model was consistent and asymptotically normally distributed. In the future, these obtained results can be used as a theoretical basis in designing a standard toddlers growth chart to assess nutritional status.

Tuberculosis (TB) continues to be a major global health issue, especially in areas with limited resources where diagnostic tools are often insufficient. Traditional TB detection methods are slow and lack sensitivity, particularly for early-stage or low bacterial load cases. This study introduces a new multimodal deep learning model that integrates sputum image segmentation across RGB, hue, saturation, and value (HSV), and CIELAB color channels, using the YOLOv8 model for real-time detection and segmentation. The model uses the International Union Against Tuberculosis and Lung Disease (IUATLD) grading scale for accurate Mycobacterium tuberculosis (MTB) classification. Our approach shows high accuracy (92.24%) and precise forecasting (mean absolute percent error (MAPE) of 0.23%), greatly enhancing diagnostic speed and reliability. This research offers a novel method for classifying MTB using a multimodal deep learning model that integrates sputum image segmentation across RGB, HSV, and CIELAB color channels. By using the YOLOv8 model for real-time bounding box detection and segmentation, and the IUATLD grading scale for classification, our method achieves high accuracy and precision in identifying TB bacteria. Our findings indicate that this multimodal deep learning approach significantly improves diagnostic accuracy and speed, providing a reliable tool for early TB detection.