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All Journal e-CliniC JURNAL BIOMEDIK Medical Scope Journal (MSJ) Narra J
Kairupan, Tara S.
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Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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Journal : Narra J

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Psoriasis severity assessment: Optimizing diagnostic models with deep learning Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Prakoeswa, Cita RS.; Kairupan, Tara S.; Idroes, Ghifari M.; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 4 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i3.1512

Abstract

Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis severity. A dataset containing 1,546 clinical images was subjected to pre-processing techniques, including cropping and applying noise reduction through median filtering. The dataset was categorized into four severity classes: none, mild, moderate, and severe, based on the Psoriasis Area and Severity Index (PASI). It was split into 1,082 images for training (70%) and 463 images for validation and testing (30%). Five modified deep convolutional neural networks (DCNN) were evaluated, including ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The data were validated based on accuracy, precision, sensitivity, specificity, and F1-score, which were weighted to reflect class representation; Pairwise McNemar's test, Cochran's Q test, Cohen’s Kappa, and Post-hoc test were performed on the model performance, where overall accuracy and balanced accuracy were determined. Findings revealed that among the five deep learning models, ResNet50 emerged as the optimum model with an accuracy of 92.50% (95%CI: 91.2–93.8%). The precision, sensitivity, specificity, and F1-score of this model were found to be 93.10%, 92.50%, 97.37%, and 92.68%, respectively. In conclusion, ResNet50 has the potential to provide consistent and objective assessments of psoriasis severity, which could aid dermatologists in timely diagnoses and treatment planning. Further clinical validation and model refinement remain required.
Mechanistic insights into the anticancer, anti-inflammatory, and antioxidant effects of yellowfin tuna collagen peptides using network pharmacology Kairupan, Tara S.; Kapantow, Nova H.; Tallei, Trina E.; Niode, Nurdjannah J.; Sanggelorang, Yulianty; Rotty, Linda WA.; Wungouw, Herlina IS.; Kawengian, Shirley ES.; Fatimawali, Fatimawali; Maulydia, Nur B.
Narra J Vol. 5 No. 1 (2025): April 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i1.1885

Abstract

Marine-derived collagen peptides have been acknowledged for their therapeutic potential, especially in cancer therapy and inflammation management. The aim of this study was to investigate the molecular mechanisms that contribute to the anticancer, anti-inflammatory and antioxidant properties of yellowfin tuna collagen peptides (YFTCP) utilizing a network pharmacology approach. The YFTCP was extracted from the bones of yellowfin tuna (Thunnus albacares) and subsequently hydrolyzed with trypsin. Seventeen peptides were discovered using liquid chromatography in conjunction with high-resolution mass spectrometry (LC-HRMS). A network pharmacology method was utilized to investigate the interactions between the discovered peptides and their biological targets. Additionally, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify pertinent biological pathways involved in the anticancer, antioxidant, and anti-inflammatory effects of these peptides. GO analysis revealed key associations between YFTCP and critical cancer- and inflammation-related genes encoding proteins such as CCND1, SRC, AKT1, IL-1β, TNF, and PPARG, which exhibited significant interactions. These proteins are essential for the regulation of the cell cycle, the development of tumors, and the response to inflammatory stimuli. The KEGG analysis also revealed that YFTCP was involved in a number of critical pathways, such as endocrine resistance, cancer pathways, Kaposi sarcoma-associated herpesvirus infection, proteoglycans in cancer, and human cytomegalovirus infection. These findings highlight the potential use of YFTCP as a multifaceted therapeutic agent, indicating their role in regulating important biological pathways associated with cancer development and inflammation. This study provides new valuable insights into the pharmacological properties of YFTCP, paving the way for future studies and drug development focused on these bioactive peptides.
Co-Authors Earlia, Nanda Fatimawali . Ferra O. Mawu, Ferra O. Hartono, Lusiane M. Idroes, Ghifari M. Kawengian, Shirley ES. Marlyn G. Kapantow Maulana, Aga Maulydia, Nur B. Muhammad Subianto Ngantung, Hana N. E. N. Nova Hellen Kapantow Noviandy, Teuku R. Nurdjannah J. Niode Pieter L. Suling Prakoeswa, Cita RS. Rinaldi Idroes Rotty, Linda WA. Siregar, Angel R. F. Sole, Fifin R. T. Suhendra, Rivansyah Trina E. Tallei, Trina E. Warouw, Mathilda W. Wungouw, Herlina IS. Yulianty Sanggelorang