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All Journal Natural Sciences Engineering and Technology Journal Sriwijaya Journal of Radiology and Imaging Research Sriwijaya Journal of Obstetrics and Gynecology
Nur Diana
Department of Molecular Biology, CMHC Research Center, Palembang, Indonesia
Aerospace Engineering 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 Electrical & Electronics Engineering Energy Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation

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Radiomics-Based Machine Learning for Automated Detection and Rupture-Risk Stratification of Cerebral Vascular Malformations: A Retrospective Cohort Study Hesti Putri; Nur Diana; Paula Magna Pablo-Rodriguez; Nadia Khoirina
Sriwijaya Journal of Radiology and Imaging Research Vol. 3 No. 2 (2025): Sriwijaya Journal of Radiology and Imaging Research
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjrir.v3i2.291

Abstract

Introduction: Cerebral vascular malformations (CVMs) are the leading cause of spontaneous intracranial haemorrhage, yet their detection on CT/MR angiography is operator-dependent and existing machine-learning models derive almost exclusively from Caucasian or East Asian cohorts. We developed and internally validated a population-specific radiomics pipeline for CVM detection and rupture-risk stratification in a Southeast Asian population. Methods: In this retrospective diagnostic-and-prognostic cohort (STARD 2015; CLAIM) at a tertiary hospital in Palembang, Indonesia (2020–2025), 486 adults with diagnostic-quality CTA or TOF-MRA were analysed. After resampling and normalisation, 1,218 PyRadiomics features were reduced by LASSO and used to train Random Forest, SVM and XGBoost models (70/30 split). The reference standard was blinded consensus segmentation by two consultant neuroradiologists. Diagnostic accuracy (Wilson CI), AUC (DeLong), likelihood ratios, Cohen κ, McNemar test and a multivariable rupture model were computed. Results: CVM prevalence was 42.8% (208/486). XGBoost was the best detector (AUC 0.963 (95% CI 0.950–0.977); sensitivity 88.5 (95% CI 83.4–92.1)%; specificity 92.4 (95% CI 88.7–95.0)%; LR+ 11.71 (95% CI 7.74–17.72)), outperforming a single radiologist (ΔAUC 0.128, p<0.001; McNemar χ2=9.72, p=0.002 (discordant pairs b=73, c=39)). Inter-reader agreement was almost perfect (κ 0.845 (95% CI 0.797–0.893)). A radiomics-clinical model stratified rupture (AUC 0.846 (95% CI 0.792–0.901)), with lesion size (OR 4.26 (95% CI 2.60–6.98)) and hypertension (OR 2.46 (95% CI 1.18–5.14)) dominant and good calibration (χ2=14.87, df=8, p=0.062). Conclusion: A population-specific radiomics machine-learning pipeline achieved high diagnostic accuracy for CVM detection and clinically useful rupture-risk stratification, supporting operator-independent neurovascular triage in Southeast Asian settings. External validation is warranted.
Deep learning-assisted digital VIA via telemedicine and HPV self-sampling for CIN2+ detection in remote archipelago women: a prospective diagnostic accuracy study Theresia Putri Sinaga; Nur Diana; Firman Hadi; Gayatri Putri
Sriwijaya Journal of Obstetrics and Gynecology Vol. 4 No. 1 (2026): Sriwijaya Journal of Obstetrics & Gynecology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjog.v4i1.302

Abstract

Background: Cervical cancer disproportionately burdens women in low- and middle-income countries, where archipelagic geography and colposcopist shortages obstruct screening. Human papillomavirus (HPV) self-sampling is highly sensitive but poorly specific, generating colposcopy referrals that exceed remote-area capacity. Objective: To evaluate whether deep-learning (DL)-assisted digital visual inspection with acetic acid (VIA), delivered by telemedicine, can triage HPV-positive women and detect high-grade cervical intraepithelial neoplasia (CIN2+). Methods: In a prospective, double-blind, STARD-compliant diagnostic accuracy study, 642 women aged 30–50 years at an urban tertiary referral hospital (Center A) and five remote community health centers (Region B) in an Indonesian archipelago province underwent HPV-DNA self-sampling and smartphone-captured digital VIA analyzed by a MobileNetV2 convolutional neural network. All participants received colposcopy-directed biopsy as the reference standard, eliminating verification bias. Metrics used Wilson 95% confidence intervals (CI); multivariable logistic regression and decision-analytic triage metrics were derived. Results: CIN2+ prevalence was 13.1% (95% CI 10.7–15.9). DL-assisted VIA achieved sensitivity 91.7% (95% CI 83.8–95.9), specificity 88.4% (85.4–90.8), and AUC 0.93, exceeding human-read VIA (sensitivity 67.9%). HPV self-sampling was most sensitive (95.2%) but least specific (81.5%). A sequential HPV→DL-VIA pathway raised specificity to 95.7% and positive predictive value to 75.5%, reducing colposcopy referrals by 46.4% and unnecessary referrals by 76.7% (number-needed-to-screen 7.6). HPV positivity dominated the multivariable model (adjusted OR 91.5, 95% CI 32.7–256.2, p<0.001; Nagelkerke R² 0.50). Conclusion: Telemedicine-delivered, DL-assisted VIA is an accurate triage for HPV-positive women that conserves scarce colposcopy capacity while preserving CIN2+ detection. This decentralized two-step pathway is a scalable strategy for advancing cervical-cancer elimination in geographically isolated populations.
Targeted Metabolic Engineering of Saccharomyces cerevisiae for High-Efficiency Valorization of Lignocellulosic Biomass into Superior-Quality Bioplastics Nur Diana; Zaki Ahmad; Selma Fajic
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.v5i2.70

Abstract

The global transition towards a sustainable circular bioeconomy urgently requires innovative platforms for converting renewable waste streams into value-added products. Lignocellulosic biomass, particularly agricultural residue like rice straw, stands as a vast, underutilized carbon source. This study details the systematic metabolic engineering of Saccharomyces cerevisiae for the high-efficiency production of poly(3-hydroxybutyrate) (PHB), a biodegradable bioplastic, from rice straw hydrolysate. A multi-faceted synthetic biology approach was implemented in S. cerevisiae CEN.PK2-1C. A robust xylose co-utilization pathway was integrated using codon-optimized genes from Scheffersomyces stipitis. The PHB biosynthesis pathway from Cupriavidus necator was introduced using a cassette of strong, constitutive yeast promoters (pTDH3, pTEF1, pPGK1). To maximize carbon flux towards PHB, key competing pathways were eliminated via CRISPR-Cas9-mediated gene knockouts of the primary alcohol dehydrogenase (ADH1) and glycerol-3-phosphate dehydrogenase (GPD1) genes. The performance of the final engineered strain was evaluated in high-cell-density fed-batch fermentation using detoxified rice straw hydrolysate sourced from Palembang, Indonesia. The final engineered strain, YL-PHB-05 (Δadh1 Δgpd1), demonstrated superior performance. In fed-batch bioreactor cultivation, it achieved a final cell dry weight of 33.8 ± 1.5 g/L and a PHB titer of 15.2 ± 0.7 g/L, with an intracellular PHB accumulation of 45.0 ± 1.2% of cell dry weight. This corresponds to a high yield of 0.28 g PHB per gram of consumed sugars. Crucially, the produced PHB exhibited a superior weight-average molecular weight (Mw) of 1.2 x 10⁶ Da with a polydispersity index of 2.1. In conclusion, this work successfully demonstrates a robust strategy for engineering S. cerevisiae into an efficient cell factory for producing high-quality bioplastics from a globally relevant agricultural waste stream. The high titers, yields, and superior polymer properties achieved present a significant advancement towards establishing an economically viable and sustainable process for bioplastic production within a circular bioeconomy.
Co-Authors Firman Hadi Gayatri Putri Hesti Putri Nadia Khoirina Paula Magna Pablo-Rodriguez Selma Fajic Theresia Putri Sinaga Zaki Ahmad