Suboptimal gestational weight gain has been associated with adverse perinatal and maternal outcomes, including increased risk of non-communicable diseases later in life. This study aimed to determine the proportion and determinants of suboptimal GWG. A cross-sectional study was conducted among 475 pregnant women in Selangor between January and March 2020. The study included all pregnant women at their second or third trimester who fulfilled the inclusion and exclusion criteria. A multistage sampling was applied. The GWG adequacy was based on recommendations from the Institute of Medicine (2009). Multinomial logistic regressions were used for data analysis. Out of the 475 respondents, 224 (47.2%) pregnant women had inadequate GWG, 142 (29.9%) had adequate GWG, and 109 (22.9%) had excessive GWG. Multinomial logistic regression showed that having diabetes in pregnancy (AdjOR 2.24, 95% CI: 1.31, 3.83, p = 0.003), middle (M40) monthly household income (AdjOR 2.33, 95% CI: 1.09, 4.96, p = 0.029), low (B40) monthly household income (AdjOR 2.22, 95% CI: 1.07, 4.72, p = 0.039), and an obese pre-pregnancy BMI (AdjOR 2.77, 95% CI: 1.43, 5.35, p = 0.002) were significantly associated with inadequate GWG. Overweight (AdjOR 5.18, 95% CI: 2.52, 10.62, p < 0.001) and obese pre-pregnancy BMIs (AdjOR 17.95, 95% CI: 8.13, 36.95, p < 0.001) were significantly associated with excessive GWG. Improving maternal and perinatal outcomes requires targeted interventions focusing on these modifiable determinants.

This scoping review examines the distribution, sources, and characterization of atmospheric microplastics (AMPs) in Southeast Asia (SEA), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive search of Scopus and PubMed identified 58 relevant articles, with 16 meeting the inclusion criteria. Findings indicate high microplastic (MP) concentrations in urban centres, notably in Malaysia, Indonesia, and Thailand, a pattern driven by rapid urbanisation, industrial emissions, textile production, and insufficient waste management. Predominant polymer types include polyethylene (PE), polypropylene (PP), and polyester (PET), with fibres and black particles being the most common forms. Black particles, often linked to tire wear and vehicular emissions, underscore traffic pollution’s role in AMP distribution, while PET fibres reflect the influence of SEA’s textile industry. Geographic gaps were observed, with limited studies in countries such as Cambodia and Laos. The review highlights the need for standardised sampling and quantification methods to ensure data comparability and calls for expanded research into rural and coastal regions. Future studies should prioritise longitudinal investigations into the effects of chronic exposure on health; this is particularly relevant for nanoplastics (NPs) because of their greater potential for biological penetration. These insights form a crucial foundation for mitigating AMP pollution in SEA.

Hospital buildings require special attention to protect patients and healthcare workers from hospital-acquired infections and sick building illnesses. This is the first study to assess the prevalence of fungus in indoor air, outdoor air, and their contamination on surfaces at selected locations in four highly contaminated hospitals (A, B, C, and D) in Peninsular Malaysia. A total of 294 indoor air samples, 106 scrapped and 169 swabbed, were collected from July 2019 to August 2020. Bioaerosol concentrations were calculated using the colony-forming unit (CFU)/m³. Molecular identification was performed on the cultures. The internal transcribed spacer (ITS) region in the rRNA gene of the isolates was amplified by PCR. Results showed that fungal burden was in the range between 18 and 2597 CFU/m³. Fungal load in selected locations at Hospital D were in the higher range between 106 and 2597 CFU/m³, with two locations exceeding the national guidelines. Fungal genera were highly identified in air samples (47) compared to swabbed (29) and scrapped (18) samples. The dominant species were C. halotolerans, C. tenuissimum, P. alfredii, P. brevicompactum, P. brocae, P. cataractarum, and A. aculeatus. Fungal loads were higher in the Orthopedic and Oral Surgeon Clinic, the On Call Emergency Room, wards, and pathways.

Background: Wastewater-based surveillance (WBS) has emerged as a valuable tool for population-level monitoring of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission, yet the interplay between sampling strategies and disease prevalence in shaping detection performance remains ambiguous. We investigated how grab and composite sampling influence SARS-CoV-2 ribonucleic acid (RNA) detection dynamics and predictive lag times across high- and low-prevalence communities in Selangor, Malaysia. Methods: A 28-week longitudinal study was conducted in Selangor, Malaysia, comparing grab and composite wastewater sampling in communities with high and low Coronavirus disease 2019 (COVID-19) prevalence. SARS-CoV-2 RNA in 348 samples was quantified using digital Reverse Transcription Polymerase Chain Reaction (RT-dPCR), and viral lineages were characterized by Nanopore sequencing. Detection sensitivity and lead times relative to reported cases were evaluated. Results: In low-prevalence settings, grab sampling showed higher detection sensitivity than composite sampling (92.0% vs. 70.0%), whereas both methods achieved similarly high detection in high-prevalence areas (>97.0%). Lag-time analysis indicated that grab sampling in high-prevalence settings was significantly associated with case trends at potential two-week lead (p = 0.024), while composite sampling in low-prevalence settings showed the strongest association at a potential one-week lead (p = 0.0022). Overall, lag structures varied by both sampling strategy and prevalence context. Both sampling approaches captured the replacement of Omicron sublineages (XBB.1.5, XBB.1.9.1, XBB.1.16) and identified additional circulating variants, including EG.5, that were not captured in the available clinical sequencing dataset during the same period. Conclusions: These findings reveal that local transmission intensity is associated with the utility of different sampling designs. Context-specific optimization of WBS sampling strategies enhances sensitivity, reduces detection lag, and strengthens early warning and genomic-tracking capacity in public health surveillance frameworks.

Background: In recent years, the rapid emergence and global spread of dengue has become a public health burden. Clinical surveillance alone has limited capacity, with delayed detection of upcoming outbreaks. Hence, the potential use of wastewater-based surveillance (WBS) for early detection of incoming surges of dengue cases could complement proactive public health action. However, there are still substantial gaps in the standard approach for sampling and detection methods in dengue WBS. Objective: This review aims to determine the current methodological approach for the detection of dengue virus (DENV) in wastewater across geographical areas. Methods: The review will be conducted systematically following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. In the initial stage, peer-reviewed publications from PubMed, Embase, Scopus, and Web of Science will be searched using predefined terms such as “Dengue” and “WBS.” Keywords will be adjusted to suit each database to identify studies related to DENV WBS from inception until June 2025. Subsequently, the references from relevant articles will be screened for eligibility. All data will be extracted from full-text articles highlighting the characteristics and methodological context of the investigated DENV WBS using a standardized form. The ROBINS-I (Risk of Bias in Non-randomized Studies - of Interventions) tool and the GRADE (Grading of Recommendations Assessment, Development, and Evaluations) system will be used to assess study bias and the quality of the evidence. Further descriptive analysis and meta-analysis will be applied to evaluate the methodologies for DENV WBS. Results: The data on DENV detection in wastewater will be synthesized by analyzing sampling techniques, viral detection method, study sites, geographic locations and dengue serotype. A meta-analysis will be conducted using a random-effects model if data are heterogeneous, with pooled estimates reported as 95% CIs. Heterogeneity will be assessed using I ² and chi-square tests, with subgroup and sensitivity analyses conducted as needed. Findings will be reported in accordance with PRISMA 2020. As of October 2025, records have been identified from the databases and data analysis is expected to be completed by the first quarter of 2026. Conclusions: This protocol outlines a systematic approach to identifying and evaluating existing methods for detecting DENV in wastewater. This research aims to provide valuable insights into best practices for dengue surveillance and offer guidance for future research by highlighting current strengths and limitations in the field.

Dengue fever, with a high proportion of asymptomatic infections, poses a major global public health challenge that traditional surveillance systems frequently underestimate. Wastewater-based epidemiology (WBE) has emerged as a promising approach to monitoring infectious diseases beyond enteric viruses. Dengue virus is shed in urine, feces, and saliva, providing a biological basis for wastewater detection alongside clinical surveillance. This systematic review and meta-analysis synthesize current evidence on dengue virus (DENV) detection in wastewater and evaluate methodological factors influencing detection success in WBE. A systematic literature search using selected databases and predetermined keywords, followed by eligibility screening, resulted in ten studies being included, covering community surveillance and experimental trials. DENV ribonucleic acids (RNA) were most consistently detected and enriched in wastewater solids, indicating this matrix as the most reliable for surveillance. Among concentration methods, ultrafiltration achieved the highest viral recovery efficiency, while reverse transcription digital polymerase chain reaction (RT-dPCR) demonstrated superior sensitivity and precision compared to those of reverse transcription quantitative polymerase chain reaction (RT-qPCR), particularly at low viral concentrations. Storage at −80 °C was critical for preserving RNA integrity. The meta-analysis yielded a pooled DENV positivity rate of 24% (95% CI: 20–28%) after exclusion of outliers. Overall, solid-phase analysis combined with RT-dPCR represents the most sensitive methodological approach across the included studies. Harmonized protocols are needed to support future translation of dengue WBE into community surveillance as current evidence mainly demonstrates methodological feasibility and provides a technical foundation for future public health integration. Therefore, further longitudinal and multi-site validation is required to establish its broader applicability for dengue surveillance.