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  1. Ong SQ, Jaal Z
    J Insect Sci, 2018 Mar 01;18(2).
    PMID: 29718500 DOI: 10.1093/jisesa/iey032
    Larval age and nutrition significantly affected the insect's physiology. These influences are important when rearing a population of vectors that is used to monitor the resistance level, in which standardized conditions are crucial for a more harmonized result. Little information has been reported on the effects of larval age and nutrition on the susceptibility of insects to insecticides, and therefore, we studied the effects on the susceptibility of Culex quinquefasciatus Say's (Diptera: Culicidae) larvae to temephos by comparing the median lethal concentration (LC50) after 24 hr between the second and fourth instar larvae and between the larvae that fed on protein-based and carbohydrate-based larval diets. The susceptibility of the larvae was significantly affected by the larval diets, as the larvae that fed on protein-based beef food and milk food demonstrated significantly higher LC50 value compared with the larvae that fed on carbohydrate-based food: lab food and yeast food. The larval diet interacted significantly with the larval age: while the second instar larvae were susceptible to temephos when supplied with carbohydrate-based food, the second and fourth instar larvae had no significant effect when supplied with protein-based diets, implying that a protein-rich environment may cause the mosquito to be less susceptible to temephos. This study suggested the importance of standardizing nutrition when rearing a vector population in order to obtain more harmonized dosage-response results in an insecticide resistance monitoring program. Future research could focus on the biochemical mechanism between the nutrition and the enzymatic activities of the vector.
  2. Ong SQ, Jaal Z
    Parasit Vectors, 2015;8:28.
    PMID: 25588346 DOI: 10.1186/s13071-015-0639-2
    The trend in chemical insecticide development has focused on improving the efficacy against mosquitoes while reducing the environmental impact. Lethal lures apply an "attract-and-kill" strategy that draws the insect to the killing agent rather than bringing the killing agent to the insect.
  3. Ong SQ, Hamid SA
    PLoS One, 2022;17(12):e0279094.
    PMID: 36584101 DOI: 10.1371/journal.pone.0279094
    Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects-order, family, and genus-and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification.
  4. Ahmad H, Ong SQ, Tan EH
    Int J Insect Sci, 2019;11:1179543318823533.
    PMID: 30675104 DOI: 10.1177/1179543318823533
    Megaselia scalaris (Loew) is one of the best-known diets for the swiftlet. Previous studies have addressed the problem of some mass rearing conditions for this insect; unfortunately, the details of the nutritional composition of the life stages and cost of the breeding materials were insufficiently reported, even though this information is crucial for farming the edible-nest swiftlet. We aimed to investigate the nutritional composition of the life stages of M scalaris on a cost basis using 3 common commercial breeding materials: chicken pellets (CPs), fish pellets (FPs), and mouse pellets (MPs). Modified Association of Official Analytical Chemists (AOAC) proximate and mineral analyses were carried out on the insect's third instar larvae, pupal, and adult stages to determine the nutritional composition. Regardless of the breeding materials, the adult stage of M scalaris had significantly higher crude protein than the other stages; the pupae were rich in calcium, which is required for egg production; and the third instar larvae had the highest amount of crude fat compared with the other stages. Regarding the energy content, there were no significant differences among the stages according to the breeding materials. In terms of nutritional cost, CP was the most economic breeding material and generated the highest amount of nutrients per US dollar (US $). Different life stages of M scalaris were used by the swiftlets by supplying the required nutrients, and future studies should focus on effective diet feeding methods.
  5. Ong SQ, Ahmad H, Mohd Ngesom AM
    Infect Dis Rep, 2021 Feb 05;13(1):148-160.
    PMID: 33562890 DOI: 10.3390/idr13010016
    We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.
  6. Ong SQ, Ab Majid AH, Ahmad H
    Trop Life Sci Res, 2017 Jul;28(2):45-55.
    PMID: 28890760 MyJurnal DOI: 10.21315/tlsr2017.28.2.4
    In this study, bifenthrin (Maxxthor SC, Ensystex Australasia Pty Ltd), imidacloprid (Prothor SC, Ensystex Australasia Pty Ltd) and fipronil (Regent(®)50SC, Bayer) were applied on the natural infest manures according to the manufacturer rate during a broiler breeding cycle. Solvent direct-immersion extraction (SDIE) was used in detecting the target compound and later, quantification of the insecticide residues in field condition was investigated. The samples were prior cleaned up by solid-phase extraction (SPE) and analysed by Ultra-Performance Liquid Chromatography (UPLC) - photodiode array (PDA) system. In the field trial, three insecticides were showed accumulation during the broiler breeding period and it is suggested that they acted as adulticides when applied on the poultry manures, this is supported by the significant correlation between the increment of insecticide residues to the reduction percentage of adult flies (<0.05). Fipronil showed significantly greater reduction on the adult fly compared to the other insecticides, in which the reduction rate compared to control population at the end of the broiler breeding period; fipronil, imidaclopril and bifenthrin reduced 51.51%, 28.30% and 30.84% of adult flies, respectively.
  7. Ong SQ, Ahmad H, Tan EH
    Environ Entomol, 2018 12 07;47(6):1582-1585.
    PMID: 30165432 DOI: 10.1093/ee/nvy127
    Megaselia scalaris (Loew) (Diptera: Phoridae) provides great evidential value in estimating the postmortem interval (PMI) compared with other dipterans due to its common occurrence on human corpses both indoors and in concealed environments. Studies have focused on the effect of temperature, larval diet, and photoperiod on the development of the species; however, knowledge of M. scalaris development at different moisture levels is insufficient. This study aimed to investigate the effects of substrate moisture on the larval development time, pupal recovery, pupal weight, adult emergence, and adult head width of M. scalaris. The larvae were reared in five replicates on substrates with six moisture levels ranging from 50 to 90%. Larvae and puparia were sampled daily, and the collection time, number, and weight were recorded, measured, and then compared using multivariate analysis of variance with a post hoc least significant difference test. Larvae developed most quickly (3.75 ± 0.04 d) at 50% substrate moisture; the larvae were able to survive in extremely wet substrates (90% moisture), but the development time was significantly longer (6.48 ± 0.19 d). Moisture greatly influenced the pupation rate and adult emergence but showed a weak effect on the pupae weight and adult head width. Due to the significance of moisture on the development of M. scalaris, PMI estimation using M. scalaris with cadavers of different moisture content must be carefully conducted to avoid inaccuracy.
  8. Ong SQ, Ab Majid AH, Ahmad H
    J Econ Entomol, 2016 Feb 18.
    PMID: 26896536
    It is crucial to understand the degradation pattern of insecticides when designing a sustainable control program for the house fly, Musca domestica (L.), on poultry farms. The aim of this study was to determine the half-life and degradation rates of cyromazine, chlorpyrifos, and cypermethrin by spiking these insecticides into poultry manure, and then quantitatively analyzing the insecticide residue using ultra-performance liquid chromatography. The insecticides were later tested in the field in order to study the appropriate insecticidal treatment intervals. Bio-assays on manure samples were later tested at 3, 7, 10, and 15 d for bio-efficacy on susceptible house fly larvae. Degradation analysis demonstrated that cyromazine has the shortest half-life (3.01 d) compared with chlorpyrifos (4.36 d) and cypermethrin (3.75 d). Cyromazine also had a significantly greater degradation rate compared with chlorpyrifos and cypermethrin. For the field insecticidal treatment interval study, 10 d was the interval that had been determined for cyromazine due to its significantly lower residue; for ChCy (a mixture of chlorpyrifos and cypermethrin), the suggested interval was 7 d. Future work should focus on the effects of insecticide metabolites on targeted pests and the poultry manure environment.
  9. Ong SQ, Ahmad H, Majid AHA
    Pest Manag Sci, 2021 Dec;77(12):5347-5355.
    PMID: 34309999 DOI: 10.1002/ps.6573
    BACKGROUND: The application of computer vision and deep learning to pest monitoring has recently received much attention. Although several studies have demonstrated the application of object detection to the number of pests on a substrate, for house flies (Musca domestica L.), in which the larvae were aggregated and overlapped together, the object detection technique was difficult to implement. We demonstrate a novel method for estimating larval abundance by using computer vision on larval breeding substrate, in which the reflective color and topography are affected by the size of the population.

    RESULTS: We demonstrate a method using a web-based tool to construct a deep learning model and later export the model for deployment. We train the model by using breeding substrate images with different spectra of illumination on known densities of larvae and evaluate the training model in both the test set and field-collected samples. In general, the model was able to predict the larval abundance by the laboratory-prepared breeding substrate with 87.56% to 94.10% accuracy, precision, recall, and F-score on the unseen test set, and white and green illumination performed significantly higher compared to other illuminations. For field samples, the model was able to obtain at least 70% correct predictions by using white and infrared illumination.

    CONCLUSION: Larval abundance can be monitored with computer vision and deep learning, and the monitoring can be improved by using more biochemistry parameters as the predictors and examples of field samples included building a more robust model. © 2021 Society of Chemical Industry.

  10. Ong SQ, Ahmad H, Jaal Z, Rus AC
    J Econ Entomol, 2016 Feb;109(1):352-9.
    PMID: 26546486 DOI: 10.1093/jee/tov326
    In this study, the toxicology of two commercial larvicides--cyromazine (Neporex 50SP) and ChCy (combination of chlorpyrifos and cypermethrin, Naga 505)--and five commercial adulticides--thiamethoxam (Agita 10WG), cyfluthrin (Responsar WP), lambda-cyhalothrin (Icon 2.8EC), fipronil (Regent 50SC), and imidacloprid (Toxilat 10WP)--was examined against the WHO/VCRU (World Health Organization/ Vector Control Research Unit) susceptible strain and the AYTW (Ayer Tawar) field strain of house fly, Musca domestica L. These pesticides were administered topically, in the diet, or as a dry residue treatment on plywood. Probit analysis using at least five concentrations and the concentration that was lethal to 50% (LC(50)) of the organisms was applied to compare the toxicology and resistance levels of the AYTW population to different insecticides. In the larvicide laboratory study, ChCy was more effective than cyromazine, with a significantly lower LC(50) value when administered topically or in the diet, although the AYTW population was susceptible to both larvicides with a resistance ratio (RR) <10. For the adulticide laboratory study, cyfluthrin and fipronil exhibited the lowest LC50 values of the adulticides, indicating that they are both effective at controlling adult flies, although lambda-cyhalothrin showed moderate resistance (RR = 11.60 by topical application; 12.41 by plywood treatment). Further investigation of ChCy, cyromazine, cyfluthrin, and fipronil under field conditions confirmed that ChCy and cyromazine strikingly reduced larval density, and surprisingly, ChCy also exhibited adulticidal activity, which significantly reduced adult fly numbers compared with the control group. Cyfluthrin and fipronil were also confirmed to be effective, with a significant reduction in adult fly numbers compared with the control group.
  11. Ong SQ, Ahmad H, Ab Majid AH, Jaal Z
    J Med Entomol, 2017 11 07;54(6):1626-1632.
    PMID: 28981905 DOI: 10.1093/jme/tjx128
    The potential of integrating the mycoinsecticide, Metarhizium anisopliae (Met.), into house fly control programs is tremendous. However, the interaction between the fungus and insecticide, when applied at poultry farms, remains poorly understood. This study investigated the interaction between M. anisopliae and two selected insecticides, cyromazine and ChCy (a mixture of chlorpyrifos and cypemethrin), with three objectives: to assess the compatibility of M. anisopliae and the insecticides by measuring fungal vegetative growth and conidia production in the presence of insecticides; to evaluate the effect of M. anisopliae on these insecticides by analyzing insecticidal residue using ultra performance liquid chromatography; and to study the synergistic effects of M. anisopliae and the insecticides by applying sublethal concentrations of insecticides with M. anisopliae to house fly larvae. Metarhizium anisopliae was more tolerant to ChCy than to cyromazine, as M. anisopliae showed significantly more growth when grown with this insecticide. The M. anisopliae + ChCy combination resulted in significantly less chlorpyrifos residues compared to the ChCy plate, and 62-72% house fly larva mortality occurred when M. anisopliae and sublethal concentrations of ChCy were combined, implicating synergistic effects of the fungus with low concentrations of ChCy. Integrating M. anisopliae with compatible chemical at right concentration is crucial for poultry farm house fly control programs.
  12. Ong SQ, Pauzi MBM, Gan KH
    Acta Trop, 2022 Jul;231:106447.
    PMID: 35430265 DOI: 10.1016/j.actatropica.2022.106447
    Mosquito-borne diseases are emerging and re-emerging across the globe, especially after the COVID19 pandemic. The recent advances in text mining in infectious diseases hold the potential of providing timely access to explicit and implicit associations among information in the text. In the past few years, the availability of online text data in the form of unstructured or semi-structured text with rich content of information from this domain enables many studies to provide solutions in this area, e.g., disease-related knowledge discovery, disease surveillance, early detection system, etc. However, a recent review of text mining in the domain of mosquito-borne disease was not available to the best of our knowledge. In this review, we survey the recent works in the text mining techniques used in combating mosquito-borne diseases. We highlight the corpus sources, technologies, applications, and the challenges faced by the studies, followed by the possible future directions that can be taken further in this domain. We present a bibliometric analysis of the 294 scientific articles that have been published in Scopus and PubMed in the domain of text mining in mosquito-borne diseases, from the year 2016 to 2021. The papers were further filtered and reviewed based on the techniques used to analyze the text related to mosquito-borne diseases. Based on the corpus of 158 selected articles, we found 27 of the articles were relevant and used text mining in mosquito-borne diseases. These articles covered the majority of Zika (38.70%), Dengue (32.26%), and Malaria (29.03%), with extremely low numbers or none of the other crucial mosquito-borne diseases like chikungunya, yellow fever, West Nile fever. Twitter was the dominant corpus resource to perform text mining in mosquito-borne diseases, followed by PubMed and LexisNexis databases. Sentiment analysis was the most popular technique of text mining to understand the discourse of the disease and followed by information extraction, which dependency relation and co-occurrence-based approach to extract relations and events. Surveillance was the main usage of most of the reviewed studies and followed by treatment, which focused on the drug-disease or symptom-disease association. The advance in text mining could improve the management of mosquito-borne diseases. However, the technique and application posed many limitations and challenges, including biases like user authentication and language, real-world implementation, etc. We discussed the future direction which can be useful to expand this area and domain. This review paper contributes mainly as a library for text mining in mosquito-borne diseases and could further explore the system for other neglected diseases.
  13. Isawasan P, Abdullah ZI, Ong SQ, Salleh KA
    MethodsX, 2023;10:101947.
    PMID: 36636281 DOI: 10.1016/j.mex.2022.101947
    Mosquito identification and classification are the most important steps in a surveillance program of mosquito-borne diseases. With conventional approach of data collection, the process of sorting and classification are laborious and time-consuming. The advancement of computer vision with transfer learning provides excellent alternative to the challenge. Transfer learning is a type of machine learning that is viable and durable in image classification with limited training images. This protocol aims to develop step-by-step procedure in developing a classification system with transfer learning algorithm for mosquito, we demonstrate the protocol to classify two species of Aedes mosquito - Aedes aegypti L. and Aedes albopitus L, but user can adopt the protocol for higher number of species classification. We demonstrated the way of start from the scratch, fine-tuning two pre-trained model performance by using different combination of hyperparameters - batch size and learning rate, and explain the terminology in the Appendix. This protocol target on the domain expert such as entomologist and public health practices to develop their own model to solve the task of mosquito/insect classification.
  14. Ong SQ, Ahmad H, Jaal Z, Rus A, Fadzlah FH
    J Med Entomol, 2017 Jan;54(1):24-29.
    PMID: 28082628 DOI: 10.1093/jme/tjw140
    Determining the control threshold for a pest is common prior to initiating a pest control program; however, previous studies related to the house fly control threshold for a poultry farm are insufficient for determining such a threshold. This study aimed to predict the population changes of house fly population by comparing the intrinsic rate of increase (rm) for different house fly densities in a simulated system. This study first defined the knee points of a known population growth curve as a control threshold by comparing the rm of five densities of house flies in a simulated condition. Later, to understand the interactions between the larval and adult populations, the correlation between larval and adult capacity rate (rc) was studied. The rm values of 300- and 500-fly densities were significantly higher compared with the rm values at densities of 50 and 100 flies. This result indicated their representative indices as candidates for a control threshold. The rc of larval and adult populations were negatively correlated with densities of fewer than 300 flies; this implicated adult populations with fewer than 300 flies as declining while the larval population was growing; therefore, control approaches should focus on the immature stages. The results in the present study suggest a control threshold for house fly populations. Future works should focus on calibrating the threshold indices in field conditions.
  15. Ong SQ, Ahmad H, Nair G, Isawasan P, Majid AHA
    Sci Rep, 2021 05 10;11(1):9908.
    PMID: 33972645 DOI: 10.1038/s41598-021-89365-3
    Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.
  16. Ong SQ, Mat Jalaluddin NS, Yong KT, Ong SP, Lim KF, Azhar S
    Ecol Evol, 2023 Jun;13(6):e10212.
    PMID: 37325726 DOI: 10.1002/ece3.10212
    Natural history museum collections are the most important sources of information on the present and past biodiversity of our planet. Most of the information is primarily stored in analogue form, and digitization of the collections can provide further open access to the images and specimen data to address the many global challenges. However, many museums do not digitize their collections because of constraints on budgets, human resources, and technologies. To encourage the digitization process, we present a guideline that offers low-cost and technical knowledge solutions yet balances the quality of the work and outcomes. The guideline describes three phases of digitization, namely preproduction, production, and postproduction. The preproduction phase includes human resource planning and selecting the highest priority collections for digitization. In the preproduction phase, a worksheet is provided for the digitizer to document the metadata, as well as a list of equipment needed to set up a digitizer station to image the specimens and associated labels. In the production phase, we place special emphasis on the light and color calibrations, as well as the guidelines for ISO/shutter speed/aperture to ensure a satisfactory quality of the digitized output. Once the specimen and labels have been imaged in the production phase, we demonstrate an end-to-end pipeline that uses optical character recognition (OCR) to transfer the physical text on the labels into a digital form and document it in a worksheet cell. A nationwide capacity workshop is then conducted to impart the guideline, and pre- and postcourse surveys were conducted to assess the confidence and skills acquired by the participants. This paper also discusses the challenges and future work that need to be taken forward for proper digital biodiversity data management.
  17. Ong SQ, Isawasan P, Ngesom AMM, Shahar H, Lasim AM, Nair G
    Sci Rep, 2023 Nov 05;13(1):19129.
    PMID: 37926755 DOI: 10.1038/s41598-023-46342-2
    Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system.
  18. Ong SQ, Dawood MM, Rahman H, Alias MF, Moideen MA, Lee PC, et al.
    MethodsX, 2024 Jun;12:102563.
    PMID: 38328504 DOI: 10.1016/j.mex.2024.102563
    Mosquito-borne diseases pose a significant threat in many Southeast Asian countries, particularly through the sylvatic cycle, which has a wildlife reservoir in forests and rural areas. Studying the composition and diversity of vectors and pathogen transmission is especially challenging in forests and rural areas due to their remoteness, limited accessibility, lack of power, and underdeveloped infrastructure. This study is based on the WHO mosquito sampling protocol, modifies technical details to support mosquito collection in difficult-to-access and resource-limited areas. Specifically, we describe the procedure for using rechargeable lithium batteries and solar panels to power the mosquito traps, demonstrate a workflow for processing and storing the mosquitoes in a -20 °C freezer, data management tools including microclimate data, and quality assurance processes to ensure the validity and reliability of the results. A pre- and post-test was utilized to measure participant knowledge levels. Additional research is needed to validate this protocol for monitoring vector-borne diseases in hard-to-reach areas within other countries and settings.
  19. Ng DC, Liew CH, Tan KK, Chin L, Ting GSS, Fadzilah NF, et al.
    BMC Infect Dis, 2023 Jun 12;23(1):398.
    PMID: 37308825 DOI: 10.1186/s12879-023-08357-y
    BACKGROUND: Children account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19.

    METHODS: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.

    RESULTS: A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively.

    CONCLUSION: Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.

  20. Chun-Ern Ng D, Liew CH, Tan KK, Lim HY, Zailanalhuddin NEB, Tan SF, et al.
    Pediatr Int, 2023;65(1):e15690.
    PMID: 38037505 DOI: 10.1111/ped.15690
    BACKGROUND: We describe the epidemiology, clinical characteristics, and outcomes of multisystem inflammatory syndrome in children (MIS-C) among children from Negeri Sembilan, Malaysia.

    METHODS: A retrospective, multicentre, observational study was performed among children ≤15 years old who were hospitalized for MIS-C between January 18, 2021 and June 30, 2023. The incidence of MIS-C was estimated using reported SARS-CoV-2 cases and census population data. Descriptive analyses were used to summarize the clinical presentation and outcomes.

    RESULTS: The study included 53 patients with a median age of 5.7 years (IQR 1.8-8.7 years); 75.5% were males. The overall incidence of MIS-C was approximately 5.9 cases per 1,000,000 person-months. Pediatric intensive care unit (PICU) admission was required for 22 (41.5%) patients. No mortalities were recorded. Children aged 6-12 years were more likely to present with cardiac dysfunction/shock (odds ratio [OR] 5.43, 95% confidence interval [CI] 1.67-17.66), whereas children below 6 years were more likely to present with a Kawasaki disease phenotype (OR 5.50, 95% CI 1.33-22.75). Twenty patients (37.7%) presented with involvement of at least four organ systems, but four patients (7.5%) demonstrated single-organ system involvement.

    CONCLUSION: An age-based variation in the clinical presentation of MIS-C was demonstrated. Our findings suggest MIS-C could manifest in a spectrum, including single-organ involvement. Despite the high requirement for PICU admission, the prognosis of MIS-C was favorable, with no recorded mortalities.

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