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All Journal Universa Medicina Smart Medical Journal Indonesia Journal of Halal Amerta Nutrition Jurnal Ilmu Kefarmasian Indonesia Jurnal Pengabdian Al-Ikhlas Jurnal Sosial dan Sains Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Global Health Research
Indra Kusuma
Departemen Fisiologi Fakultas Kedokteran Universitas YARSI , Jakarta, Indonesia
Religion Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Potensi Kefir Susu Kambing dengan Penambahan Madu Randu dalam Mencegah Stress Oksidatif pada Tikus Diabetes : Potential of Randu Honey Kefir to Prevent Oxidative Stress in Diabetic Rats Ibnu Malkan Bakhrul Ilmi; Avliya Quratul Marjan; Erna Harfiani; Indra Kusuma; Utami Wahyuningsih; Chika Nur Fikriana; Amelia Luthfiyah Mulyadewi
Amerta Nutrition Vol. 9 No. 1SP (2025): AMERTA NUTRITION SUPPLEMENTARY EDITION Special 5th Amerta Nutrition Conferenc
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/amnt.v9i1SP.2025.31-40

Abstract

Background: More than 90% of adults with type 2 diabetes are overweight or obese. This condition is known to increase oxidative stress, which contributes to cellular damage and disease complications. As a functional food, kefir has long been recognized for its various health benefits, including anti-inflammatory, antioxidant, and antidiabetic properties. Objectives: This study aimed to analyze the potential of randu honey kefir in preventing oxidative stress in diabetic rats. Methods: This study employed a randomized block design with a 21-day intervention period. A total of 42 male Sprague Dawley rats aged 6–8 weeks were divided into six groups: KS (standard), KN (negative control), K1 (quercetin), K2 (metformin), P1 (randu honey kefir 1.8 mL/200g BW), and P2 (preventive group). Diabetes was induced using streptozotocin at 40 mg/kgBW combined with a high-fat diet. Malondialdehyde (MDA) levels were analyzed using spectrophotometry, while interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) levels were measured using ELISA kits. Results: Significant differences (p-value<0.05) were observed in MDA, TNF-α, and IL-6 levels between the P1 and P2 groups compared with the KN group. Randu honey kefir groups showed mean oxidative stress marker levels comparable to those of quercetin and metformin treatments, including MDA (P1: 32.9 ± 29.35 nmol/mL; P2: 23.77 ± 11.63 nmol/mL), TNF-α (P1: 45.37 ± 31.25 pg/mL; P2: 37.81 ± 27.00 pg/mL), and IL-6 (P1: 64.81 ± 21.35 pg/mL; P2: 59.23 ± 14.95 pg/mL). Conclusions: Randu honey kefir intervention demonstrates potential in suppressing oxidative stress in diabetic rats.
The Potential of Probiotics from Fermented Rice Water on Fibroblast Proliferation and Migration: A Systematic Review Nury Nur Melati Tanjung; Dian Widiyanti; Indra Kusuma; Dicky Budiman
Indonesian Journal of Global Health Research Vol. 8 No. 4 (2026): Indonesian Journal of Global Health Research
Publisher : GLOBAL HEALTH SCIENCE GROUP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37287/ijghr.v8i4.1715

Abstract

Fermented rice water (FRW) has been traditionally used for skin care, but its biological mechanisms on fibroblasts, key cells in wound healing, have not been fully characterized. This systematic review aims to evaluate the efficacy of FRW on fibroblast proliferation and migration and to identify the molecular pathways involved. A literature search was conducted through PubMed, Scopus, and Google Scholar using the keywords "fermented rice water," "fibroblast," and "wound healing" in publications from 2015 to 2025. Studies were included if they evaluated the effects of FRW on fibroblasts in vitro or in vivo. Risk of bias was assessed using SYRCLE RoB (Risk Of Bias). Four studies met the inclusion criteria. FRW increased fibroblast viability by up to 116% and accelerated diabetic wound closure from 27% to 71% compared to controls. Molecular analysis showed upregulation of Collagen I mRNA by 17%, Collagen III by 47%, and Elastin by 159%. FRW activated the NRF2 pathway, which increased the expression of antioxidant enzymes (SOD, CAT, HO-1) and decreased inflammatory mediators including TNF-α, NF-κB, and MMP-1. Bioactive metabolites from Lactobacillus plantarum, Bacillus cereus, and Aspergillus oryzae played a role in these therapeutic effects. FRW shows potential as a fibroblast bio-stimulator through multi-pathway modulation involving extracellular matrix synthesis, antioxidant, and anti-inflammatory effects, with prospects for application in chronic wounds and anti-aging that require further clinical validation.
Characterization of Fibroblast Secretome: Extracelular Vesicles (EVs) Particle Count, Molecular Size and Total Protein Levels Marisa Riliani; Indra Kusuma; Yurika Sandra; Zakiyah Zakiyah; Aditya Ramansyah
Smart Medical Journal Vol 8, No 3 (2025): December
Publisher : Faculty of Medicine Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/smj.v8i3.109896

Abstract

Introduction: The secretome of human dermal fibroblasts consists of diverse bioactive molecules including proteins and extracellular vesicles (EVs), playing a key role in skin regeneration and homeostasis. Molecular weight-based fractionation provides insights into the functional heterogeneity of the conditioned medium (CM). This study aimed to characterize size-fractionated CM from fibroblasts to reveal its molecular properties and potential bioactivity.Methods: A descriptive laboratory study was conducted using conditioned medium from passage 5 human dermal fibroblasts cultured in serum-free conditions. Tangential flow filtration (TFF) was applied to fractionate the CM into low (LMW), medium (MMW), and high molecular weight (HMW) components. Nanoparticle tracking analysis (NTA) quantified EV particle concentration and size, while total protein content was assessed by BCA assay. Experiments were performed in triplicate (n=3).Results: Particle concentrations increased with molecular weight, with HMW fractions showing the highest counts but slightly smaller average particle sizes compared to LMW and MMW fractions. Total protein concentration also rose with molecular weight, peaking in HMW fractions, indicating enrichment of larger protein complexes and vesicles. Fractionation maintained consistent particle size distribution across fractions, supporting effective separation.Conclusion: Fibroblast secretome exhibits heterogeneous EV and protein populations distinguishable by molecular weight fractionation. The enriched higher molecular weight fractions contain more EVs and proteins, suggesting distinct biological roles. Combining TFF with further purification steps may enhance separation purity, facilitating therapeutic applications of secretome components.
Optimizing Image Preprocessing for AI-Driven Cervical Cancer Diagnosis Chandra Prasetyo Utomo; Neng Suhaeni; Nashuha Insani; Elan Suherlan; Nunung Ainur Rahmah; Ahmad Rusdan Utomo; Indra Kusuma; Muhamad Fathurachman; Dewa Nyoman Murti Adyaksa
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1128

Abstract

Cervical cancer ranks among the top causes of cancer-related deaths in women globally. Early detection is vital for improving patient survival rates. The multiclass classification of cervical cell images presents challenges primarily due to the notable variations in cell sizes across different classes. Conventional AI methods for diagnosing cervical cancer often rely on image-resizing techniques that overlook crucial features like relative cell dimensions, which impairs the models' ability to distinguish between classes effectively. This paper presents a novel AI-driven approach that employs constant padding to maintain the natural size differences among cells. Our method utilizes deep learning for both feature extraction and multiclass classification. We assessed the method using the publicly accessible SIPaKMeD dataset. Experimental findings indicate that our approach surpasses traditional image-resizing methods, especially in classes that are more challenging to predict. This strategy highlights AI's potential to improve cervical cancer diagnosis, offering a more precise and dependable tool for early detection. A reliable and precise AI model for diagnosing cervical cancer is crucial for promoting widespread screening and ensuring timely and effective treatment, which can ultimately lower mortality rates. By aiding early and accurate diagnosis, this approach aligns with global health efforts to alleviate the burden of cancer and other diseases, especially in areas with limited access to advanced healthcare services facilities.
Co-Authors Aditya Ramansyah AGUNG ERU WIBOWO Ahmad Rusdan Utomo Amelia Luthfiyah Mulyadewi Anna P Roswiem Avliya Quratul Marjan Budiman, Dicky Chandra Prasetyo Utomo Chika Nur Fikriana CHURIYAH CHURIYAH Dewa Nyoman Murti Adyaksa Dian Widiyanti Elan Suherlan Erna Harfiani FAIZA KARA FABIOLA Ilmi, Ibnu Malkan Bakhrul Juniarti . Marisa Riliani Muhamad Fathurachman Nashuha Insani Nazwirman Nazwirman Neng Suhaeni Nunung Ainur Rahmah Nurfatimah Aprilianda Simatupang Nurmasithah Nurmasithah Nury Nur Melati Tanjung Restu Syamsul Hadi SISKA A KUSUMASTUTI Siti Nur Riani Utami Wahyuningsih Wahyu Fitrah Darwanto Nugroho Yolandha Tannia Yurika Sandra Yurika Sandra Zakiyah Zakiyah