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Curcumin-Loaded Solid Lipid Nanoparticles from Curcuma longa Attenuate Inflammatory Cytokine Cascades in a Rat Model of Acute Peritonitis Oliva Azalia Putri; Dedi Sucipto
Eureka Herba Indonesia Vol. 6 No. 1 (2025): Eureka Herba Indonesia
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/ehi.v6i1.137

Acute peritonitis remains a life-threatening intra-abdominal inflammatory condition with significant morbidity and mortality, particularly in resource-limited settings across Southeast Asia. Curcumin, the principal polyphenol of Curcuma longa L. (Zingiberaceae), has potent anti-inflammatory and antioxidant properties, but its clinical use is limited by poor oral bioavailability, rapid hepatic metabolism, and low aqueous solubility. This study evaluated the anti-inflammatory and antioxidant efficacy of curcumin-loaded solid lipid nanoparticles (Cur-SLNs) in a cecal ligation and puncture (CLP)-induced acute peritonitis rat model. Thirty male Wistar rats were randomized into five groups (n=6): sham, CLP+vehicle, CLP+free curcumin (100 mg/kg), CLP+Cur-SLN low dose (50 mg/kg), and CLP+Cur-SLN high dose (100 mg/kg). Cur-SLNs prepared by hot homogenization-ultrasonication had a mean particle size of 152.4±8.7 nm, polydispersity index 0.218±0.03, zeta potential −28.6±2.1 mV, and entrapment efficiency 87.3±3.2%. At 24 hours, Cur-SLN high dose significantly reduced TNF-α (89.6±18.3 versus 287.5±45.2 pg/mL, p<0.001), IL-6 (102.4±22.7 versus 312.4±52.8 pg/mL, p<0.001), and IL-1β (78.5±16.8 versus 245.6±41.3 pg/mL, p<0.001) compared with CLP-vehicle, with large effect sizes (Cohen's d 4.50–5.72), alongside attenuated oxidative stress, reduced bacterial burden, and preserved peritoneal histology. Cur-SLNs from Curcuma longa represent a promising herbal nanomedicine strategy for attenuating inflammatory cascades in acute peritonitis.

Tinospora crispa Phytosome Enhances Oral Bioavailability and Glycemic Control in Streptozotocin-Induced Diabetic Rats Dedi Sucipto; Taufiq Indera Jayadi; Bryan Helsey
Eureka Herba Indonesia Vol. 7 No. 1 (2026): Eureka Herba Indonesia
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/ehi.v7i1.143

Diabetes mellitus remains a major global health challenge with rising prevalence in Southeast Asia, where traditional herbal remedies continue to play a significant role in disease management. Tinospora crispa (L.) Hook. f. & Thomson (Menispermaceae), locally known as brotowali in Indonesian jamu medicine, exhibits anti-diabetic properties attributed to its alkaloid and diterpenoid constituents; however, the oral bioavailability of its key bioactive compound berberine remains limited at approximately 5%. This study evaluated the pharmacokinetic enhancement and anti-diabetic efficacy of a novel Tinospora crispa phytosome in streptozotocin (STZ)-induced diabetic rats. Thirty male Wistar rats were allocated to five groups (n = 6): normal control, diabetic control, diabetic plus metformin (200 mg/kg), diabetic plus T. crispa free extract (400 mg/kg), and diabetic plus T. crispa phytosome (400 mg/kg), given orally for 28 days. The phytosome achieved a 3.14-fold enhancement in relative oral bioavailability (AUC0–24: 1524.7 ± 185.4 versus 486.3 ± 62.8 ng·h/mL, p < 0.001) and a higher peak plasma berberine concentration (Cmax: 387.2 ± 42.3 versus 124.5 ± 18.7 ng/mL, p < 0.001). After 28 days, the phytosome group showed significant reductions in fasting blood glucose (148.6 ± 19.2 versus 328.4 ± 42.5 mg/dL, p < 0.001) and HbA1c (6.1 ± 0.6 versus 9.2 ± 1.1%, p < 0.001), with an improved lipid profile comparable to metformin and large effect sizes (Cohen's d: 3.51–6.22). These findings indicate that phytosome technology effectively enhances the bioavailability and anti-diabetic efficacy of T. crispa, supporting its development as a standardized herbal complementary therapy for diabetes mellitus.

Diagnostic Accuracy of a Federated Learning Algorithm for Proliferative Diabetic Retinopathy Detection: A Multicenter Indonesian Study Rachmat Hidayat; Dedi Sucipto; Alexander Mulya; Ifah Shandy
Sriwijaya Journal of Ophthalmology Vol. 8 No. 2 (2025): Sriwijaya Journal of Ophthalmology
Publisher : Department of Opthalmology, Faculty of Medicine, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/sjo.v8i2.136

Introduction: Proliferative diabetic retinopathy (PDR) remains a leading cause of preventable blindness in resource-limited settings. Federated learning (FL) enables collaborative artificial intelligence (AI) model training without sharing patient data. This study evaluated the diagnostic accuracy of an FL-based algorithm for PDR detection across three Indonesian ophthalmology centers. Methods: This multicenter, prospective, diagnostic accuracy study enrolled 512 eyes from 289 patients with type 2 diabetes at three private hospital ophthalmology clinics in Palembang, Jakarta, and Surabaya, Indonesia (January 2023–December 2024). All eyes underwent standardized fundus photography, spectral-domain optical coherence tomography, and comprehensive ophthalmic examination. The FL-based deep learning algorithm was evaluated against two independent retinal specialists using the International Clinical Diabetic Retinopathy classification. Primary outcomes were sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The FL-based AI achieved a sensitivity of 94.6% (95% CI 81.8–99.3), specificity of 91.2% (95% CI 88.2–93.6), and AUC of 0.962 (95% CI 0.943–0.981) for PDR detection. Agreement with retinal specialists was substantial (κ = 0.87). Performance was consistent across centers (AUC 0.955–0.968; p = 0.841). Media opacity was the strongest predictor of misclassification (OR 3.42; 95% CI 1.87–6.25; p < 0.001). Conclusion: The FL-based AI demonstrated high diagnostic accuracy for PDR detection comparable to retinal specialists across multiple Indonesian centers. This privacy-preserving approach may facilitate scalable diabetic retinopathy screening in resource-limited ophthalmology settings.

Longitudinal MRI-PDFF Quantification of Hepatic Steatosis Reversibility During SGLT2-Inhibitor versus GLP-1 Receptor Agonist Therapy: A Prospective Cohort Study Arsan Saliha; Dedi Sucipto; Mischa Chantal Adella; Ericca Dominique Perez
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.v3i2.292

Introduction: Precise quantification of hepatic steatosis is central to managing metabolic dysfunction-associated steatotic liver disease (MASLD). MRI proton density fat fraction (MRI-PDFF) has displaced biopsy as a reproducible biomarker, yet real-world comparative monitoring data and formal early-response prediction remain scarce in Indonesian practice. We quantified steatosis reversibility on serial MRI-PDFF during SGLT2-inhibitor versus GLP-1 receptor agonist therapy and tested whether an early scan predicts response. Methods: In this prospective cohort at a tertiary hospital in Palembang, Indonesia, 90 adults with MASLD (baseline PDFF ≥5.5%) were grouped by prescribed therapy (SGLT2i, n=45; GLP-1 RA, n=45) and imaged at baseline, Month 3 and Month 6 on a 3.0 T scanner using a confounder-corrected 3D multi-echo spoiled gradient-echo sequence. Two blinded radiologists measured PDFF across Couinaud segments V/VI/VIII; response was a ≥30% relative reduction. Analyses (STARD 2015) included linear mixed-effects modelling, κ and intraclass correlation, ROC (DeLong), likelihood ratios and multivariable logistic regression. Results: PDFF fell in both arms (both p<0.001; partial η²=0.66 and 0.79). Responder rate was 75.6% (95% CI 61.3–85.8) with GLP-1 RA versus 40.0% (27.0–54.5) with SGLT2i (OR 4.64; p=0.001). A Month-3 decline ≥16.9% predicted Month-6 response with AUC 0.895 (0.823–0.967), sensitivity 96.2%, specificity 78.9%, LR+ 4.57, LR− 0.05. Inter-reader agreement was near-perfect (ICC 0.986; κ 0.861). GLP-1 RA therapy and higher baseline PDFF independently predicted response. Conclusion: Serial MRI-PDFF reliably quantified pharmacologically-induced steatosis reversibility, with GLP-1 RA producing greater fat loss and an early scan accurately triaging responders. MRI-PDFF is a robust, decision-useful monitoring biomarker deployable in tertiary settings.

Diagnostic Accuracy of a Federated Learning Algorithm for Proliferative Diabetic Retinopathy Detection: A Multicenter Indonesian Study Rachmat Hidayat; Dedi Sucipto; Alexander Mulya; Ifah Shandy
Sriwijaya Journal of Ophthalmology Vol. 8 No. 2 (2025): Sriwijaya Journal of Ophthalmology
Publisher : Department of Ophthalmology, Faculty of Medicine, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/sjo.v8i2.136

Introduction: Proliferative diabetic retinopathy (PDR) remains a leading cause of preventable blindness in resource-limited settings. Federated learning (FL) enables collaborative artificial intelligence (AI) model training without sharing patient data. This study evaluated the diagnostic accuracy of an FL-based algorithm for PDR detection across three Indonesian ophthalmology centers. Methods: This multicenter, prospective, diagnostic accuracy study enrolled 512 eyes from 289 patients with type 2 diabetes at three private hospital ophthalmology clinics in Palembang, Jakarta, and Surabaya, Indonesia (January 2023–December 2024). All eyes underwent standardized fundus photography, spectral-domain optical coherence tomography, and comprehensive ophthalmic examination. The FL-based deep learning algorithm was evaluated against two independent retinal specialists using the International Clinical Diabetic Retinopathy classification. Primary outcomes were sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The FL-based AI achieved a sensitivity of 94.6% (95% CI 81.8–99.3), specificity of 91.2% (95% CI 88.2–93.6), and AUC of 0.962 (95% CI 0.943–0.981) for PDR detection. Agreement with retinal specialists was substantial (κ = 0.87). Performance was consistent across centers (AUC 0.955–0.968; p = 0.841). Media opacity was the strongest predictor of misclassification (OR 3.42; 95% CI 1.87–6.25; p < 0.001). Conclusion: The FL-based AI demonstrated high diagnostic accuracy for PDR detection comparable to retinal specialists across multiple Indonesian centers. This privacy-preserving approach may facilitate scalable diabetic retinopathy screening in resource-limited ophthalmology settings.

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