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Correction: RNA purification-free detection of SARS-CoV-2 using reverse transcription loop-mediated isothermal amplification (RT-LAMP)

Lai MY, Suppiah J, Thayan R, Ismail I, Mustapa NI, Soh TST, et al.

Trop Med Health, 2023 Nov 22;51(1):66.
PMID: 37993919 DOI: 10.1186/s41182-023-00555-3
Fulltext Corrigendum to "Colorimetric detection of SARS-CoV-2 by uracil-DNA glycosylase (UDG) reverse transcription loop-mediated isothermal amplification (RT-LAMP)" [International Journal of Infectious Diseases 120 (2023) Pages 132-134]

Lai MY, Bukhari FDM, Zulkefli NZ, Ismail I, Mustapa NI, Soh TST, et al.

Int J Infect Dis, 2023 Nov;136:91.
PMID: 37774600 DOI: 10.1016/j.ijid.2023.09.011
Fulltext The predictive and diagnostic accuracy of vascular endothelial growth factor and pentraxin-3 in severe dengue

Low GKK, Gan SC, Zainal N, Naidu KD, Amin-Nordin S, Khoo CS, et al.

Pathog Glob Health, 2018 09;112(6):334-341.
PMID: 30246621 DOI: 10.1080/20477724.2018.1516417

This study aimed to evaluate vascular endothelial growth factor (VEGF) and pentraxin 3 (PTX-3) as predictive and diagnostic markers in differentiating severe dengue from non-severe dengue. The study was conducted in Ampang Health Clinic, Ampang Hospital and Serdang Hospital. The plasma levels of VEGF and PTX-3 were compared between severe dengue and non-severe dengue by ELISA from the day of presentation until discharged. Multiple logistic regression was used to develop predictive and diagnostic models by incorporating other clinical parameters. The receiver operating characteristics (ROC) analysis was used to assess the accuracy of the biomarkers and the developed models. Eighty-two patients were recruited, 29 with severe dengue and four died. The Area Under the Curve (AUC) was statistically significant in VEGF as diagnostic marker at Day 2 and 3 of illness with sensitivity of 80.00%-100.00% and specificity of 76.47%-80.00%. The predictive model with AUC of 0.84 (p
Fulltext Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models

Lim MC, Singh S, Lai CH, Gill BS, Kamarudin MK, Md Zamri ASS, et al.

Epidemiol Health, 2023;45:e2023093.
PMID: 37905314 DOI: 10.4178/epih.e2023093

OBJECTIVES: This study aimed to develop susceptible-exposed-infectious-recovered-vaccinated (SEIRV) models to examine the effects of vaccination on coronavirus disease 2019 (COVID-19) case trends in Malaysia during Phase 3 of the National COVID-19 Immunization Program amidst the Delta outbreak.
METHODS: SEIRV models were developed and validated using COVID-19 case and vaccination data from the Ministry of Health, Malaysia, from June 21, 2021 to July 21, 2021 to generate forecasts of COVID-19 cases from July 22, 2021 to December 31, 2021. Three scenarios were examined to measure the effects of vaccination on COVID-19 case trends. Scenarios 1 and 2 represented the trends taking into account the earliest and latest possible times of achieving full vaccination for 80% of the adult population by October 31, 2021 and December 31, 2021, respectively. Scenario 3 described a scenario without vaccination for comparison.
RESULTS: In scenario 1, forecasted cases peaked on August 28, 2021, which was close to the peak of observed cases on August 26, 2021. The observed peak was 20.27% higher than in scenario 1 and 10.37% lower than in scenario 2. The cumulative observed cases from July 22, 2021 to December 31, 2021 were 13.29% higher than in scenario 1 and 55.19% lower than in scenario 2. The daily COVID-19 case trends closely mirrored the forecast of COVID-19 cases in scenario 1 (best-case scenario).
CONCLUSIONS: Our study demonstrated that COVID-19 vaccination reduced COVID-19 case trends during the Delta outbreak. The compartmental models developed assisted in the management and control of the COVID-19 pandemic in Malaysia.
Fulltext Droplet digital PCR application for the detection of SARS-CoV-2 in air sample

Rashid SA, Nazakat R, Muhamad Robat R, Ismail R, Suppiah J, Rajendran K, et al.

Front Public Health, 2023;11:1208348.
PMID: 37965510 DOI: 10.3389/fpubh.2023.1208348

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) may transmit through airborne route particularly when the aerosol particles remain in enclosed spaces with inadequate ventilation. There has been no standard recommended method of determining the virus in air due to limitations in pre-analytical and technical aspects. Furthermore, the presence of low virus loads in air samples could result in false negatives. Our study aims to explore the feasibility of detecting SARS-CoV-2 ribonucleic acid (RNA) in air samples using droplet digital polymerase chain reaction (ddPCR). Active and passive air sampling was conducted between December 2021 and February 2022 with the presence of COVID-19 confirmed cases in two hospitals and a quarantine center in Klang Valley, Malaysia. SARS-CoV-2 RNA in air was detected and quantified using ddPCR and real-time reverse transcriptase-polymerase chain reaction (RT-PCR). The comparability of two different digital PCR platforms (QX200 and QIAcuity) to RT-PCR were also investigated. Additionally negative staining transmission electron microscopy was performed to visualize virus ultrastructure. Detection rates of SARS-CoV-2 in air samples using ddPCR were higher compared to RT-PCR, which were 15.2% (22/145) and 3.4% (5/145), respectively. The sensitivity and specificity of ddPCR was 100 and 87%, respectively. After excluding 17 negative samples (50%) by both QX200 and QIAcuity, 15% samples (5/34) were found to be positive both ddPCR and dPCR. There were 23.5% (8/34) samples that were detected positive by ddPCR but negative by dPCR. In contrast, there were 11.7% (4/34) samples that were detected positive by dPCR but negative by ddPCR. The SARS-CoV-2 detection method by ddPCR is precise and has a high sensitivity for viral RNA detection. It could provide advances in determining low viral titter in air samples to reduce false negative reports, which could complement detection by RT-PCR.
Fulltext SARS-CoV-2 genomic surveillance in Malaysia: displacement of B.1.617.2 with AY lineages as the dominant Delta variants and the introduction of Omicron during the fourth epidemic wave

Azami NAM, Perera D, Thayan R, AbuBakar S, Sam IC, Salleh MZ, et al.

Int J Infect Dis, 2022 Dec;125:216-226.
PMID: 36336246 DOI: 10.1016/j.ijid.2022.10.044

OBJECTIVES: This study reported SARS-CoV-2 whole genome sequencing results from June 2021 to January 2022 from seven genome sequencing centers in Malaysia as part of the national surveillance program.
METHODS: COVID-19 samples that tested positive by reverse transcription polymerase chain reaction and with cycle threshold values <30 were obtained throughout Malaysia. Sequencing of SARS-CoV-2 complete genomes was performed using Illumina, Oxford Nanopore, or Ion Torrent platforms. A total of 6163 SARS-CoV-2 complete genome sequences were generated over the surveillance period. All sequences were submitted to the Global Initiative on Sharing All Influenza Data database.
RESULTS: From June 2021 to January 2022, Malaysia experienced the fourth wave of COVID-19 dominated by the Delta variant of concern, including the original B.1.617.2 lineage and descendant AY lineages. The B.1.617.2 lineage was identified as the early dominant circulating strain throughout the country but over time, was displaced by AY.59 and AY.79 lineages in Peninsular (west) Malaysia, and the AY.23 lineage in east Malaysia. In December 2021, pilgrims returning from Saudi Arabia facilitated the introduction and spread of the BA.1 lineage (Omicron variant of concern) in the country.
CONCLUSION: The changing trends of circulating SARS-CoV-2 lineages were identified, with differences observed between west and east Malaysia. This initiative highlighted the importance of leveraging research expertise in the country to facilitate pandemic response and preparedness.