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All Journal Natural Sciences Engineering and Technology Journal Sriwijaya Journal of Forensic and Medicolegal Crown: Journal of Dentistry and Health Research Enigma in Education
Karina Chandra
Department of Anesthesiology and Intensive Care, CMHC Research Center, Palembang, Indonesia
Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

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Effectiveness of High-Fidelity Simulation on Creative Clinical Reasoning Among Nursing Students: A Quasi-Experimental Study Danniel Hilman Maulana; Karina Chandra; Maria Rodriguez
Enigma in Education Vol. 3 No. 2 (2025): Enigma in Education
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/edu.v3i2.118

Abstract

High-fidelity simulation has become a key pedagogical strategy in nursing education, yet its specific effect on creative clinical reasoning remains underexplored, particularly in Southeast Asian higher-education contexts. The aim of this study was to evaluate the effectiveness of high-fidelity simulation on creative clinical reasoning, clinical judgment, and self-efficacy among nursing students. This quasi-experimental pre-test/post-test control group study, conducted at a private university in Palembang, Indonesia, evaluated its effectiveness among ninety final-year nursing students allocated to a simulation group (n=46) receiving four structured high-fidelity simulation sessions over eight weeks or a control group (n=44) receiving conventional case-based learning. Outcomes were measured using the Health Sciences Reasoning Test, a Creative Clinical Problem-Solving Scale, the Lasater Clinical Judgment Rubric, and the General Self-Efficacy Scale, with all scores standardized to a 0–100 metric, and analyzed using paired t-tests, independent t-tests, and ANCOVA with pre-test covariates. The simulation group demonstrated significantly greater improvements than controls in clinical reasoning (mean difference 16.2, 95% CI 13.8–18.6, p<0.001, Cohen’s d=1.98), creative problem-solving (15.5, p<0.001, d=2.10), self-efficacy (14.5, p<0.001, d=1.73), and clinical judgment (16.3, p<0.001, d=2.12); ANCOVA confirmed significant group effects (partial η²=0.322–0.375), and a positive dose-response correlation was observed (r=0.72, p<0.001). High-fidelity simulation was highly effective in enhancing creative clinical reasoning and related competencies, with large effect sizes supporting its systematic integration into nursing education curricula.
Reading the Epigenetic Clock: A Comparative Analysis of DNA Methylation Markers for Age Estimation in Semen, Saliva, and Touch DNA Febria Suryani; Bryan Helsey; Leonardo Simanjuntak; Karina Chandra; Mustafa Mahmud; Lisha Sandrina; Ahmad Erza
Sriwijaya Journal of Forensic and Medicolegal Vol. 3 No. 1 (2025): Sriwijaya Journal of Forensic and Medicolegal
Publisher : Phlox Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjfm.v3i1.233

Abstract

Background. Predicting an individual's age from biological evidence is a significant advance in forensic intelligence. DNA methylation, a stable epigenetic mark, provides the molecular basis for epigenetic clocks, but their operational reliability requires validation across diverse sample types and populations, particularly for low-template touch DNA. Objective. To develop and validate body-fluid-specific age prediction models from a curated five-CpG panel in semen, saliva and touch DNA, and to compare their accuracy in an Indonesian population. Methods. Following approval from the Ethical Committee of CMHC Indonesia (No. 128/EC/CMHC/2023), 150 healthy Indonesian male volunteers aged 18–65 provided semen, saliva and high-yield standardized touch DNA. Methylation at five CpG sites (ELOVL2, FHL2, TRIM59, KCNQ1DN, C1orf132) was quantified by controlled pyrosequencing after bisulfite conversion with efficiency controls. Body-fluid-specific models were built by multiple linear regression and validated by 10-fold cross-validation. Results. The semen and saliva models were highly accurate, with mean absolute deviations of 3.19 years (R²=0.94) and 3.55 years (R²=0.92). The touch DNA model was less precise but still informative (MAD 5.49 years, R²=0.85). All models satisfied the assumptions of linear regression, with variance inflation factors below 2.5, and 95% prediction intervals were narrowest for semen. Conclusion. The panel is validated for age prediction in a Southeast Asian population. The semen and saliva models are accurate enough for consideration in casework, while the touch DNA model, interpreted cautiously, can generate investigative leads from trace evidence. The findings underline the importance of tissue-specific modeling and provide a methodological blueprint for responsible forensic age estimation.
An In-Silico Investigation of Machine Learning for Integrating Genomic and Digital Biomarker Data in Cardiovascular Risk Stratification Immanuel Simbolon; Cindy Susanti; Gayatri Putri; Karina Chandra; Muhammad Yoshandi; Daniel Hilman Maulana
Natural Sciences Engineering and Technology Journal Vol. 5 No. 2 (2025): Natural Sciences Engineering and Technology Journal
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/nasetjournal.v5i2.71

Abstract

Conventional models for stratifying cardiovascular disease (CVD) risk have limitations. The integration of static genomic data and dynamic digital biomarkers from wearable technology holds theoretical promise, but its potential quantitative impact remains poorly defined. This study aimed to develop and validate an in-silico framework to quantify the theoretical maximum predictive gain of an integrated risk model under idealized conditions. We developed a sophisticated data generating process (DGP) to create a synthetic dataset of 5,000 individuals. The DGP incorporated demographic and clinical variables with distributions and correlations based on epidemiological literature. It included a simulated polygenic risk score (PRS) for coronary artery disease and advanced digital biomarkers derived from wireless health monitoring data, such as heart rate variability (HRV) and time in moderate-to-vigorous physical activity (MVPA). The 10-year risk of Major Adverse Cardiovascular Events (MACE) was generated via a defined logistic function incorporating these variables plus stochastic noise. We compared the performance of the ACC/AHA Pooled Cohort Equations (PCE) against several machine learning models (Logistic Regression, Random Forest, XGBoost) using the area under the receiver operating characteristic curve (AUC-ROC), precision, recall, and F1-score. In this simulated environment, the integrated XGBoost model achieved near-optimal predictive performance with an AUC-ROC of 0.92 (95% CI, 0.90-0.94), significantly outperforming the benchmark PCE model (AUC-ROC 0.76; 95% CI, 0.73-0.79; p < 0.001). The inclusion of the PRS and, most notably, dynamic digital biomarkers like HRV, provided substantial incremental improvements in risk discrimination over traditional factors alone. In conclusion, this in-silico study demonstrates the substantial theoretical potential of integrating genomic and advanced digital biomarker data through machine learning for CVD risk stratification. While these idealized results are not directly generalizable, they provide a quantitative rationale for pursuing real-world data collection and validation studies. This work establishes a methodological proof-of-concept and highlights the potential for a paradigm shift toward more dynamic and personalized cardiovascular risk assessment.
CRISPRi-Mediated Repression of gtfB Attenuates Streptococcus mutans Virulence and Promotes Ecological Homeostasis in a Preclinical Cariogenic Biofilm Model Khairiel Anwar; Maria Rodriguez; Sony Sanjaya; Danniel Hilman Maulana; Karina Chandra; Isadora Selestine
Crown: Journal of Dentistry and Health Research Vol. 3 No. 1 (2025): Crown: Journal of Dentistry and Health Research
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/crown.v3i1.234

Abstract

Introduction: Streptococcus mutans is a primary etiological agent of dental caries, largely due to its capacity to form robust, acidogenic biofilms. This virulence is critically dependent on glucosyltransferases, particularly GtfB, which synthesizes the adhesive extracellular glucan matrix. Conventional antimicrobial strategies often lack specificity, leading to oral dysbiosis. This study aimed to develop and evaluate a highly targeted CRISPR interference (CRISPRi) system to silence the gtfB gene in S. mutans, thereby inhibiting its cariogenic potential without adversely affecting the viability of key oral commensal species. Methods: A CRISPRi system, comprising a nuclease-deactivated Cas9 (dCas9) and a single guide RNA (sgRNA) targeting the gtfB promoter, was engineered into S. mutans UA159. The efficacy of gtfB silencing was quantified via qRT-PCR. The consequential effects on bacterial growth kinetics, insoluble glucan synthesis, and single-species biofilm formation were assessed using spectrophotometry, anthrone assays, crystal violet staining, and confocal laser scanning microscopy (CLSM). The ecological impact was investigated in a multi-species biofilm model containing S. mutans and the commensal bacteria Streptococcus gordonii, Streptococcus oralis, and Actinomyces naeslundii, with microbial composition analyzed by species-specific qPCR. All research activities were conducted in Indonesia. Results: The CRISPRi system induced a profound and specific downregulation of gtfB mRNA expression by over 98% (p<0.001) in the engineered S. mutans strain compared to the wild-type. This silencing did not impair bacterial planktonic growth. However, it led to a significant reduction in insoluble glucan production by 85% (p<0.001) and a corresponding 79% decrease in total biofilm biomass (p<0.001). CLSM imaging confirmed the formation of structurally deficient biofilms with minimal extracellular matrix. In the multi-species model, repression of S. mutans virulence significantly altered the biofilm ecology, resulting in a 65% reduction in the proportional abundance of S. mutans and a concomitant increase in the representation of commensal species, thereby fostering a community structure more aligned with oral health. Conclusion: Targeted repression of the gtfB gene using a CRISPRi-based approach effectively 'disarms' S. mutans, neutralizing its primary cariogenic mechanism without being bactericidal. This strategy not only attenuates its virulence but also shifts the ecological balance in favor of beneficial commensal bacteria. These findings underscore the therapeutic potential of gene-targeted virulence modulation as a precise, ecologically-sound strategy for the prevention and treatment of dental caries.
Co-Authors Ahmad Erza Bambang Sutrisno Bryan Helsey Cindy Susanti Daniel Hilman Maulana Danniel Hilman Maulana Febria Suryani Gayatri Putri Immanuel Simbolon Isadora Selestine Jason Willmare Khairiel Anwar Leonardo Simanjuntak Lisha Sandrina Maria Rodriguez Muhammad Yoshandi Mustafa Mahmud Sony Sanjaya Sri Mulyati