Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal Natural Sciences Engineering and Technology Journal Sriwijaya Journal of Forensic and Medicolegal 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 Social Sciences Transportation Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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: Indonesian Medical Research Organization

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

Abstract

Introduction: The capacity to predict an individual's age from biological evidence constitutes a significant advancement in forensic intelligence. DNA methylation, a stable epigenetic mark, provides a molecular basis for "epigenetic clocks." However, the operational reliability of these clocks necessitates rigorous validation across diverse biological samples and populations, particularly for challenging, low-template touch DNA evidence. Methods: Following approval from the Ethical Committee of CMHC Indonesia (No. 128/EC/CMHC/2023), we recruited 150 healthy Indonesian male volunteers aged 18-65. Semen, saliva, and high-yield standardized touch DNA samples were collected. DNA was extracted, quantified fluorometrically, and subjected to bisulfite conversion with efficiency controls. The methylation levels of a curated five-CpG panel (ELOVL2, FHL2, TRIM59, KCNQ1DN, C1orf132) were quantified using a rigorously controlled pyrosequencing workflow. Body-fluid-specific age prediction models were developed using multiple linear regression, validated with 10-fold cross-validation, and assessed for statistical assumptions including multicollinearity. Results: The models for semen and saliva demonstrated high predictive accuracy, yielding Mean Absolute Deviation (MAD) values of 3.19 years (R²=0.94) and 3.55 years (R²=0.92), respectively. The model developed from high-yield touch DNA was less precise but still highly informative, with a MAD of 5.49 years (R²=0.85). All models satisfied the assumptions of linear regression, with Variance Inflation Factors below 2.5 indicating low multicollinearity. The 95% prediction intervals were narrowest for semen, reflecting its superior precision. Conclusion: This study validates a robust, targeted epigenetic panel for age prediction in a Southeast Asian population. We present highly accurate, tissue-specific models for semen and saliva, suitable for immediate consideration in forensic casework. The touch DNA model, while requiring cautious interpretation, provides a valuable framework for generating investigative leads from trace evidence. Our findings underscore the critical importance of tissue-specific modeling and provide a detailed methodological and statistical blueprint for the responsible implementation of forensic age estimation.
Post-Mortem High-Anion-Gap Metabolic Acidosis and Blood Formate Quantitation as Diagnostic Markers of Fatal Oplosan Intoxication: A Retrospective Diagnostic Accuracy Study at a Tertiary Forensic Center in Indonesia Bambang Sutrisno; Sri Mulyati; Karina Chandra; Jason Willmare
Sriwijaya Journal of Forensic and Medicolegal Vol. 4 No. 1 (2026): Sriwijaya Journal of Forensic and Medicolegal
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjfm.v4i1.257

Abstract

Introduction: Bootleg liquor (oplosan) containing illicit methanol remains a leading cause of preventable forensic death in Indonesia, yet objective post-mortem biochemical diagnostic criteria are incompletely standardised. Methods: This retrospective diagnostic accuracy study evaluated post-mortem high-anion-gap metabolic acidosis (HAGMA) and blood formate quantitation as confirmatory markers of fatal methanol intoxication at Hospital X, Central Java, between January 2019 and December 2023. Medical examiner records, autopsy reports, and post-mortem biochemistry data from 120 adult decedents were reviewed: 74 confirmed methanol (oplosan) fatalities and 46 non-methanol metabolic acidosis deaths as the comparison group. The reference standard was post-mortem blood methanol >20 mg/dL with documented oplosan exposure history. Post-mortem blood formate was quantified by gas chromatography–flame ionisation detection (GC-FID). Sensitivity, specificity, PPV, NPV, and ROC analysis were performed with 95% confidence intervals by the Wilson score method. Results: Mean blood formate was 18.8 ± 4.9 mmol/L in the methanol group versus 1.2 ± 0.8 mmol/L in controls (p < 0.001). Post-mortem albumin-corrected anion gap was 28.7 ± 5.1 versus 14.2 ± 4.6 mmol/L (p < 0.001). Blood formate >2.0 mmol/L achieved sensitivity 100% (95% CI 95.1–100%), specificity 80.4% (95% CI 65.9–90.1%), and AUC 0.989 (95% CI 0.971–0.998). HAGMA achieved sensitivity 94.6% (95% CI 86.4–98.0%), specificity 91.3% (95% CI 78.2–97.0%), and AUC 0.976. Combined positivity yielded a specificity 100% and a PPV 100%. Multivariable logistic regression identified formate as the dominant independent predictor (OR 123.8, 95% CI 21.6–709.3). Conclusion: Post-mortem blood formate and HAGMA are highly accurate complementary markers for confirming fatal oplosan intoxication and should be incorporated into standardised Indonesian forensic autopsy protocols.
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.
Co-Authors Ahmad Erza Bambang Sutrisno Bryan Helsey Cindy Susanti Daniel Hilman Maulana Danniel Hilman Maulana Febria Suryani Gayatri Putri Immanuel Simbolon Jason Willmare Leonardo Simanjuntak Lisha Sandrina Maria Rodriguez Muhammad Yoshandi Mustafa Mahmud Sri Mulyati