The need for active and integrated involvement of the community and health professionals in the prevention and control of dengue hemorrhagic fever in Indonesia.

Successful control and prevention of dengue fever requires active involvement from all parties. For this reason, three innovative programs are needed, namely: i) increasing knowledge, attitude and practice (KAP) of the community and health professionals as capital in controlling dengue fever in a sustainable manner; ii) application of "3M Plus" to suppress vector breeding in household settings; iii) promotion of the "Jumantik" program as an effective community empowerment approach to prevent and control dengue fever based on community independence. It was concluded that successful control of dengue fever requires integration of the community and health workers through various innovative programs.

Genome mining for ribosomally synthesized and post-translationally modified peptides (RiPPs) in Streptomyces bacteria.

Ribosomally synthesized post-translationally modified peptides (RiPPs) are a novel category of bioactive natural products (NPs). Streptomyces bacteria are a potential source of many bioactive NPs. Limited opportunities are available to characterize all the bioactive NP gene clusters. In this study, 410 sequences of Streptomyces were analyzed for RiPPs through genome mining using the National Center for Biotechnology Information (NCBI), by combining BAGEL and anti-SMASH. A total of 4098 RiPPs were found; including both classified (lanthipeptide, RiPP-like, bacteriocin, LAPs, lassopeptide, thiopeptides) and nonclassified RiPPs. Soil was identified as a rich habitat for RiPPs. These data may offer alternative future remedies for various health issues.

VUStruct: a compute pipeline for high throughput and personalized structural biology.

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

Workstation Risk Factors for Work-related Musculoskeletal Disorders Among IT Professionals in Indonesia.

Objectives: This study aimed to identify workstation factors influencing work-related musculoskeletal disorders (WMSDs) among information technology (IT) professionals in Indonesia. Methods: A cross-sectional study was conducted among 150 IT workers at small-enterprise companies who were randomly selected across East Java, Indonesia. The data were modeled using multiple linear regression, with a 95% level of confidence for determining statistical significance. Results: The respondents reported that the neck had the highest level of discomfort and was the most at risk of WMSDs, followed by the lower back, right shoulder, and upper back. Screen use duration (p=0.040) was associated with whole-body WMSDs, along with seat width (p=0.059), armrest (p=0.027), monitor (p=0.046), and a combined telephone and monitor score (p=0.028). Meanwhile, the factors significantly related to the risk of WMSDs in the hands and wrist were working period (p=0.039), night shift (p=0.024), backrest (p=0.008), and mouse score (p=0.032). Conclusions: Occupational safety authorities, standards-setting departments, and policymakers should prioritize addressing the risk factors for WMSDs among IT professionals.

Financial and digital financial literacy through social media use towards financial inclusion among batik small enterprises in Indonesia.

Batik, an Indonesian textile art form, holds immense economic and cultural importance. Small and medium enterprises (SMEs) specialising in batik play a crucial role in Indonesia's economic growth and cultural preservation, contributing significantly to the gross domestic product (GDP) and preserving the nation's heritage. Nevertheless, these enterprises face several challenges, such as slow growth and limited access to credit. The batik industry also lags in financial literacy and the adoption of digital marketing strategies, hindering its development. This quantitative study aims to investigate the relationship between financial literacy, digital financial literacy, and financial inclusion in batik SMEs and also examined the moderating effect of online social networks. A survey was conducted involving 535 managers, owners, and financial officers of small batik enterprises. Subsequently, the SmartPLS statistical analysis method was employed for data analysis. The results demonstrate that financial literacy and digital financial literacy play a significant role in accessing financial inclusion for batik small enterprises. Moreover, the utilisation of social media was found to moderate these relationships, amplifying the impact of financial and digital literacy on financial inclusion. The findings contribute to the existing knowledge, provide insights for enhancing batik small enterprises, and propose a digital financial model to promote financial inclusion.

Multimorbidity among the Indigenous population: A systematic review and meta-analysis.

BACKGROUND: Multimorbidity, the concurrent presence of multiple chronic health conditions in an individual, represents a mounting public health challenge. Chronic illnesses are prevalent in the Indigenous populations, which contributes to multimorbidity. However, the epidemiology of multimorbidity in this population is not well studied. This review aimed to elucidate the extent, determinants, consequences, and prevention of multimorbidity within Indigenous populations globally, contrasting findings with non-Indigenous populations. METHODS: Adhering to the PRISMA guidelines, this systematic review assimilated peer-reviewed articles and grey literature, focusing on the prevalence, determinants, implications, and preventive strategies of multimorbidity in global Indigenous populations. Emphasis was given to original, English-language, full-text articles, excluding editorials, and conference abstracts. FINDINGS: Of the 444 articles identified, 13 met the inclusion criteria. Five studies are from Australia, and the rest are from the USA, Canada, New Zealand, and India. The study indicated a higher multimorbidity prevalence among Indigenous populations, with consistent disparities observed across various age groups. Particularly, Indigenous individuals exhibited a 2-times higher likelihood of multimorbidity compared to non-Indigenous populations. Noteworthy findings underscored the elevated severity of certain comorbid conditions, especially strokes, within Indigenous groups, with further revelations highlighting their significant pairing with conditions such as heart diseases and diabetes. INTERPRETATION: The findings affirm the elevated burden of multimorbidity among Indigenous populations. Prevalence and risk of developing multimorbidity are significantly higher in this population compared to their non-Indigenous counterparts. Future research should prioritize harmonized research methodologies, fostering insights into the multimorbidity landscape, and promoting strategies to address health disparities in Indigenous populations.

MRI Radiomics Combined with Clinicopathological Factors for Predicting 3-Year Overall Survival of Hepatocellular Carcinoma After Hepatectomy.

Background: A limited number of studies have examined the use of radiomics to predict 3-year overall survival (OS) after hepatectomy in patients with hepatocellular carcinoma (HCC). This study develops 3-year OS prediction models for HCC patients after liver resection using MRI radiomics and clinicopathological factors. Materials and Methods: A retrospective analysis of 141 patients who underwent surgical resection of HCC was performed. Patients were randomized into two set: the training set (n=98) and the validation set (n=43) including the survival groups (n=111) and non-survival groups (n=30) based on 3-year survival after hepatectomy. Furthermore, x2 or Fisher's exact test, univariate and multivariate logistic regression analyses were conducted to determine independent clinicopathological risk factors associated with 3-year OS. 1688 quantitative imaging features were extracted from preoperative T2-weighted imaging (T2WI) and contrast-enhanced magnetic resonance imaging (CE-MRI) of arterial phase (AP), portal venous phases (PVP)and delay period (DP). The features were selected using the variance threshold method, the select K best method and the least absolute shrinkage and selection operator (LASSO) algorithm. By using Bernoulli Naive Bayes (BernoulliNB) and Multinomial Naive Bayes (MultinomialNB) classifiers, we constructed models based on the independent clinicopathological factors and Rad-scores. To determine the best model, receiver operating characteristics (ROC) and Delong's test were used. Moreover, calibration curves were used to determine the calibration ability of the model, while decision curve analysis (DCA) was implemented to evaluate its clinical benefit. Results: The fusion model showed excellent prediction precision with AUC of 0.910 and 0.846 in training and validation set and revealed significant diagnostic accuracy and value in the calibration curve and DCA analysis. Conclusion: Nomograms based on MRI radiomics and clinicopathological factors have significant predictive value for 3-year OS after hepatectomy and can be used for risk classification.

Pyroptosis in Diabetic Peripheral Neuropathy and its Therapeutic Regulation.

Pyroptosis is a pro-inflammatory form of cell death resulting from the activation of gasdermins (GSDMs) pore-forming proteins and the release of several pro-inflammatory factors. However, inflammasomes are the intracellular protein complexes that cleave gasdermin D (GSDMD), leading to the formation of robust cell membrane pores and the initiation of pyroptosis. Inflammasome activation and gasdermin-mediated membrane pore formation are the important intrinsic processes in the classical pyroptotic signaling pathway. Overactivation of the NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome triggers pyroptosis and amplifies inflammation. Current evidence suggests that the overactivation of inflammasomes and pyroptosis may further induce the progression of cancers, nerve injury, inflammatory disorders and metabolic dysfunctions. Current evidence also indicates that pyroptosis-dependent cell death accelerates the progression of diabetes and its frequent consequences including diabetic peripheral neuropathy (DPN). Pyroptosis-mediated inflammatory reaction further exacerbates DPN-mediated CNS injury. Accumulating evidence shows that several molecular signaling mechanisms trigger pyroptosis in insulin-producing cells, further leading to the development of DPN. Numerous studies have suggested that certain natural compounds or drugs may possess promising pharmacological properties by modulating inflammasomes and pyroptosis, thereby offering potential preventive and practical therapeutic approaches for the treatment and management of DPN. This review elaborates on the underlying molecular mechanisms of pyroptosis and explores possible therapeutic strategies for regulating pyroptosis-regulated cell death in the pharmacological treatment of DPN.

Recent advances in molecular mechanisms of skin wound healing and its treatments.

The skin, being a multifaceted organ, performs a pivotal function in the complicated wound-healing procedure, which encompasses the triggering of several cellular entities and signaling cascades. Aberrations in the typical healing process of wounds may result in atypical scar development and the establishment of a persistent condition, rendering patients more vulnerable to infections. Chronic burns and wounds have a detrimental effect on the overall quality of life of patients, resulting in higher levels of physical discomfort and socio-economic complexities. The occurrence and frequency of prolonged wounds are on the rise as a result of aging people, hence contributing to escalated expenditures within the healthcare system. The clinical evaluation and treatment of chronic wounds continue to pose challenges despite the advancement of different therapeutic approaches. This is mainly owing to the prolonged treatment duration and intricate processes involved in wound healing. Many conventional methods, such as the administration of growth factors, the use of wound dressings, and the application of skin grafts, are used to ease the process of wound healing across diverse wound types. Nevertheless, these therapeutic approaches may only be practical for some wounds, highlighting the need to advance alternative treatment modalities. Novel wound care technologies, such as nanotherapeutics, stem cell treatment, and 3D bioprinting, aim to improve therapeutic efficacy, prioritize skin regeneration, and minimize adverse effects. This review provides an updated overview of recent advancements in chronic wound healing and therapeutic management using innovative approaches.

Phytoremediation: a transgenic perspective in omics era.

Phytoremediation is an environmental safety strategy that might serve as a viable preventative approach to reduce soil contamination in a cost-effective manner. Using plants to remediate pollution from the environment is referred to as phytoremediation. In the past few decades, plants have undergone genetic manipulation to overcome inherent limitations by using genetically modified plants. This review illustrates the eco-friendly process of cleaning the environment using transgenic strategies combined with omics technologies. Herbicides tolerance and phytoremediation abilities have been established in genetically modified plants. Transgenic plants have eliminated the pesticides atrazine and metolachlor from the soil. To expand the application of genetically engineered plants for phytoremediation process, it is essential to test strategies in the field and have contingency planning. Omics techniques were used for understanding various genetic, hormonal, and metabolic pathways responsible for phytoremediation in soil. Transcriptomics and metabolomics provide useful information as resources to understand the mechanisms behind phytoremediation. This review aims to highlight the integration of transgenic strategies and omics technologies to enhance phytoremediation efficiency, emphasizing the need for field testing and comprehensive planning for successful implementation.

Progress toward universal health coverage in Vietnam: Evidence on dispensing trends of diabetes medications from 2015 to 2021.

AIMS: This study aims to investigate the trends in treatment coverage through dispensing diabetes medications in Vietnam from 2015 to 2021. The findings will serve to inform health policies to mitigate the health burden of Type 2 diabetes mellitus (T2DM). METHODS: We collected information on major antidiabetic medicines from General Department of Vietnam Customs and payments for antidiabetics via the National Health Insurance Program. We applied ordinary least squares models, accounting for economic and health outcome characteristics, to estimate the association between the annual mass of medications and related factors. RESULTS: Nationally, the total mass/doses of all antidiabetic drugs increased rapidly from 2015 to 2021, based on both databases. Metformin was the most frequently prescribed medicine, with the total mass increasing nearly threefold over the study period. Gliclazide, a Sulfonylureas drug, ranked second. In the multivariate regression analysis, a one-unit increase in adults with diabetes (in 1,000 s) was associated with 0.11 % (95 %CI = 0.0005; 0.0076) and 0.13 % (95%CI = 0.0007; 0.0242) higher mass of Metformin and Glimepiride, respectively. CONCLUSION: Our data suggested that policies changes were related to significant increase in antidiabetic medication dispenses in Vietnam. The high treatment coverage indicates impressive progress in achieving universal health coverage in Vietnam, meeting the UN Sustainable Development Goal (SDG).

Modelling the significance of value-belief-norm framework to predict mass adoption potentials of internet of things-enabled wearable fitness devices.

Self-health monitoring technologies have become increasingly popular in averting unanticipated health complications. However, the adoption rate of such technologies in developing countries is surprisingly low. Furthermore, empirical studies on the application of the value-belief-norm (VBN) model to elucidate intention to use IoT-enabled wearable fitness devices (IoT-enabled WFDs) are scarce. This study aimed to expand the VBN model by integrating health values, health consciousness, health knowledge-seeking, and social norms as influencing constructs. The proposed holistic framework was empirically tested to examine these constructs on users' decision-making process of adopting IoT-enabled WFDs. A web-based survey involving 866 adults in China aged 18-30 years was conducted. The gathered data were analysed using partial least squares-structural equation modeling. The results revealed the significant influence of health consciousness and health knowledge-seeking on personal health beliefs, as well as the favourable impact of personal health beliefs on personal norms and awareness of consequences. The results further demonstrated the substantial influence of awareness of consequences and ascription of responsibilities on personal norms. Besides that, personal norms and societal norms were found to have strong influence on the intention to adopt IoT-enabled WFDs, which was revealed to have significant influence on the actual usage. This study's findings offer novel theoretical insights on the behavioural characteristics of adopting IoT-enabled WFDs and serve as a practical guideline for industry experts and marketers to establish appropriate marketing strategies to support the IoT-enabled wearable sector. The findings also benefit policymakers in their efforts of developing strategies that emphasise the unique benefits of self-healthcare monitoring to encourage active lifestyle and decrease obesity and overweight-related health risks.

Nonequivalent Atomic Vibrations at Interfaces in a Polar Superlattice.

In heterostructures made from polar materials, e.g., AlN-GaN-AlN, the nonequivalence of the two interfaces is long recognized as a critical aspect of their electronic properties; in that, they host different 2D carrier gases. Interfaces play an important role in the vibrational properties of materials, where interface states enhance thermal conductivity and can generate unique infrared-optical activity. The nonequivalence of the corresponding interface atomic vibrations, however, is not investigated so far due to a lack of experimental techniques with both high spatial and high spectral resolution. Herein, the nonequivalence of AlN-(Al0.65Ga0.35)N and (Al0.65Ga0.35)N-AlN interface vibrations is experimentally demonstrated using monochromated electron energy-loss spectroscopy in the scanning transmission electron microscope (STEM-EELS) and density-functional-theory (DFT) calculations are employed to gain insights in the physical origins of observations. It is demonstrated that STEM-EELS possesses sensitivity to the displacement vector of the vibrational modes as well as the frequency, which is as critical to understanding vibrations as polarization in optical spectroscopies. The combination enables direct mapping of the nonequivalent interface phonons between materials with different phonon polarizations. The results demonstrate the capacity to carefully assess the vibrational properties of complex heterostructures where interface states dominate the functional properties.

Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies.

BACKGROUND: Pancreatic cancer (PC) stands out as one of the most formidable malignancies and exhibits an exceptionally unfavorable clinical prognosis due to the absence of well-defined diagnostic indicators and its tendency to develop resistance to therapeutic interventions. The primary objective of this present study was to identify extracellular matrix (ECM)-related hub genes (HGs) and their corresponding molecular signatures, with the intent of potentially utilizing them as biomarkers for diagnostic, prognostic, and therapeutic applications. METHODS: Three microarray datasets were sourced from the NCBI database to acquire upregulated differentially expressed genes (DEGs), while MatrisomeDB was employed for filtering ECM-related genes. Subsequently, a protein-protein interaction (PPI) network was established using the STRING database. The created network was visually inspected through Cytoscape, and HGs were identified using the CytoHubba plugin tool. Furthermore, enrichment analysis, expression pattern analysis, clinicopathological correlation, survival analysis, immune cell infiltration analysis, and examination of chemical compounds were carried out using Enrichr, GEPIA2, ULCAN, Kaplan Meier plotter, TIMER2.0, and CTD web platforms, respectively. The diagnostic and prognostic significance of HGs was evaluated through the ROC curve analysis. RESULTS: Ten genes associated with ECM functions were identified as HGs among 131 DEGs obtained from microarray datasets. Notably, the expression of these HGs exhibited significantly (p < 0.05) higher in PC, demonstrating a clear association with tumor advancement. Remarkably, higher expression levels of these HGs were inversely correlated with the likelihood of patient survival. Moreover, ROC curve analysis revealed that identified HGs are promising biomarkers for both diagnostic (AUC > 0.75) and prognostic (AUC > 0.64) purposes. Furthermore, we observed a positive correlation between immune cell infiltration and the expression of most HGs. Lastly, our study identified nine compounds with significant interaction profiles that could potentially act as effective chemical agents targeting the identified HGs. CONCLUSION: Taken together, our findings suggest that COL1A1, KRT19, MMP1, COL11A1, SDC1, ITGA2, COL1A2, POSTN, FN1, and COL5A1 hold promise as innovative biomarkers for both the diagnosis and prognosis of PC, and they present as prospective targets for therapeutic interventions aimed at impeding the progression PC.

Assessing the choice of smoke-free policies for multiunit housing and its associated determinants in Bangladesh: a cross-sectional study.

OBJECTIVES: This study aimed to assess the desire for smoke-free housing, determine the choice of smoke-free policies for multiunit housing (MUH), and identify the factors associated with policy choice among MUH residents in Bangladesh. DESIGN: We conducted a cross-sectional study from April to November 2019 using a semi-structured survey questionnaire. SETTING: This study was conducted in seven divisional cities of Bangladesh: Dhaka, Chattogram, Rajshahi, Khulna, Sylhet, Barishal, and Rangpur. PARTICIPANTS: A total of 616 adult individuals living in MUH for at least 2 years participated in the study. PRIMARY OUTCOME MEASURE: Multinomial logistic regression was used to identify the determinants of the choice of smoke-free policies for MUH. RESULTS: Overall, 94.8% of the respondents wanted smoke-free housing. Among those who wanted smoke-free housing, 44.9% preferred a smoke-free building policy, 28.3% preferred a smoke-free common area policy, 20.2% favoured a smoke-free unit policy, and 6.7% did not know what policy they should choose. Three factors were found to be significantly associated with the choice of a smoke-free building policy: staying at home for more than 12 hours (adjusted OR (aOR): 2.6; 95% CI 1.035 to 6.493), being a non-smoker (aOR: 3.2; 95% CI 1.317 to 7.582), and having at least one family member who smoked (aOR: 3.0; 95% CI 1.058 to 8.422). Results also showed that residents having at least one child under 15 in the family (aOR: 0.3; 95% CI 0.152 to 0.778) were less likely to choose a smoke-free common area policy and that women (aOR: 3.7; 95% CI 1.024 to 13.188) were more likely to choose a smoke-free unit policy. CONCLUSIONS: MUH residents in urban Bangladesh highly demanded smoke-free housing. Most residents favoured a smoke-free building policy for MUH. Those who stayed at home for a longer time, were non-smokers, and had smoking family members were more likely to choose this policy.

Nitrogen and sulfur cycling and their coupling mechanisms in eutrophic lake sediment microbiomes.

Microorganisms play important roles in the biogeochemical cycles of lake sediment. However, the integrated metabolic mechanisms governing nitrogen (N) and sulfur (S) cycling in eutrophic lakes remain poorly understood. Here, metagenomic analysis of field and bioreactor enriched sediment samples from a typical eutrophic lake were applied to elucidate the metabolic coupling of N and S cycling. Our results showed significant diverse genes involved in the pathways of dissimilatory sulfur metabolism, denitrification and dissimilatory nitrate reduction to ammonium (DNRA). The N and S associated functional genes and microbial groups generally showed significant correlation with the concentrations of NH4+, NO2- and SO42, while with relatively low effects from other environmental factors. The gene-based co-occurrence network indicated clear cooperative interactions between N and S cycling in the sediment. Additionally, our analysis identified key metabolic processes, including the coupled dissimilatory sulfur oxidation (DSO) and DNRA as well as the association of thiosulfate oxidation complex (SOX systems) with denitrification pathway. However, the enriched N removal microorganisms in the bioreactor ecosystem demonstrated an additional electron donor, incorporating both the SOX systems and DSO processes. Metagenome-assembled genomes-based ecological model indicated that carbohydrate metabolism is the key linking factor for the coupling of N and S cycling. Our findings uncover the coupling mechanisms of microbial N and S metabolism, involving both inorganic and organic respiration pathways in lake sediment. This study will enhance our understanding of coupled biogeochemical cycles in lake ecosystems.

Associations of gestational weight gain with the long-term postpartum weight gain, body mass index, waist circumference and abdominal obesity: A 27-year prospective cohort study.

BACKGROUND: This prospective cohort study aimed to investigate the associations between gestational weight gain (GWG) and long-term postpartum maternal weight gain, body mass index (BMI), waist circumference (WC), and the risk of general and abdominal obesity, beyond motherhood (some 27 y after childbirth). METHODS: Participants were 1953 women enrolled in the Mater-University of Queensland Study of Pregnancy cohort study that started in the early 1980 s, with the most recent follow-up at 27 y postpartum. We examined the prospective associations of GWG in pregnancy with weight, BMI, and WC and the risk of adiposity 27 y after the index pregnancy. We used linear and multinomial logistic regressions to examine the independent effect of GWG on each outcome, adjusting for potential confounders and mediators. RESULTS: The average GWG during pregnancy was 14.88 kg (SD 5.24). One in four women (25.50%) gained below the Institute of Medicine (IOM) recommendations and one in three (34.00%) gained excess weight during pregnancy. Every 100 g/week increment of GWG was associated with 2.0 (95% CI: 1.5, 2.6) kg, 0.7 (0.5, 0.9) kg/m2, 1.3 (0.8, 1.8) cm greater body weight, BMI, and WC, respectively 27 y postpartum. Women who gained inadequate weight in pregnancy had significantly lower odds of general obesity (OR; 0.70, 95% CI:0.53,0.94) or abdominal obesity (0.73; 0.56,0.96), whereas those who gained excess gestational weight had much higher odds of general obesity (4.49; 3.36,6.00) and abdominal obesity (3.09; 2.29,4.16). These associations were independent of potential confounders. CONCLUSION: Maternal GWG in pregnancy independently and strongly predicted beyond motherhood weight gain trajectory. GWG within IOM recommendation may prevent long-term development of both general and central obesity.

An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease.

The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.