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All Journal HAYATI Journal of Biosciences Bulletin of Chemical Reaction Engineering & Catalysis INDONESIAN JOURNAL OF PHARMACY Indonesian Journal of Chemistry Unnes Journal of Public Health Narra J International Journal of Hydrological and Environmental for Sustainability Jurnal Natural Jurnal Kedokteran Hewan Heca Journal of Applied Sciences Malacca Pharmaceutics Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics Journal of Educational Management and Learning Infolitika Journal of Data Science Indonesian Journal of Case Reports Indatu Journal of Management and Accounting Grimsa Journal of Science Engineering and Technology International Journal of Renewable Energy Development Journal Of Carbazon JURNAL REKAYASA KIMIA & LINGKUNGAN
Rinaldi Idroes
Department Of Chemistry, Faculty Of Mathematics And Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Veterinary Other

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Journal : Narra J

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Clinical and oral microbiome pattern of halitosis patients with periodontitis and gingivitis Ningsih, Diana S.; Idroes, Rinaldi; Bachtiar, Boy M.; Khairan, Khairan; Tallei, Trina E.; Kemala, Pati; Maulydia, Nur B.; Idroes, Ghazi M.; Helwani, Zuchra
Narra J Vol. 3 No. 2 (2023): August 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i2.163

Abstract

Halitosis is caused by a bacterial proteolytic process that induces the production of volatile sulfur compounds, odor-causing gases. The aim of this study was to determine the clinical oral hygiene state and oral microbiome pattern of halitosis patients with periodontitis and gingivitis. The oral hygiene state of halitosis patients with periodontitis and gingivitis was assessed using the oral hygiene index simplified (OHI-S), decay missing filled teeth (DMFT), and tongue biofilm. The dorsum of the tongue and subgingival swabs were cultured for bacteria, and bacterial morphology was evaluated using Gram staining. Evaluation of the bacterial genus using the Bergey's systematic bacteriology diagram as a guide. A total of ten patients with periodontitis and gingivitis were included. Our data indicated that the scores of OHI-S and DMFT were different significantly between halitosis patients with periodontitis and gingivitis (both had p<0.001) while tongue biofilm score was not different between groups. On the dorsum of the tongue, periodontitis patients had a significant higher oral microbiome population (85.65x106 CFU/mL) compared to those with gingivitis (0.047x106 CFU/mL) with p=0.002. In contrast, the number of microbiomes in the subgingival had no significant different between periodontitis and gingivitis. On the dorsum of the tongue, six bacterial genera were isolated from periodontitis cases and seven genera were detected from gingivitis patients. On subgingival, 10 and 15 genera were identified from periodontitis and gingivitis, respectively. Fusobacterium, Propionibacterium, Eubacterium and Lactobacillus were the most prevalent among periodontitis cases while Porphyromonas was the most prevalent in gingivitis patients. In conclusion, although OHI-S and DMFT are different between periodontitis and gingivitis, overlapping of bacterial genera was detected between periodontitis and gingivitis cases.
Characterization of red algae (Gracilaria verrucosa) on potential application for topical treatment of oral mucosa wounds in Rattus norvegicus Hakim, Rachmi F.; Idroes, Rinaldi; Hanafiah, Olivia A.; Ginting, Binawati; Kemala, Pati; Fakhrurrazi, Fakhrurrazi; Putra, Noviandi I.; Shafira, Ghina A.; Romadhoni, Yenni; Destiana, Khaerunisa; Muslem, Muslem
Narra J Vol. 3 No. 3 (2023): December 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i3.422

Abstract

Wound healing in the mouth has its challenges due to masticatory movements and the presence of bacteria that can inhibit its process. The aim of this study was to analyze the contents of red algae (Gracilaria verrucosa) from Indonesia, and its potential as a wound-healing agent for oral wounds using animal model. Red algae content was determined by phytochemical tests and gas chromatography-mass spectroscopy (GC-MS). The wound was made by making an incision on the gingival mucosa of Rattus norvegicus and the parameters assessed were bleeding time, number of fibroblast cells, and time of wound closure. Three doses of G. verrucosa gel were used (2.5%, 5%, and 10%) and the gels were applied twice a day, at 6:00 and 18:00. Application was carried out topically by applying 0.1 ml of gel to the incision wound using a 1 mL syringe. Our phytochemical test indicated that the G. verrucosa contained alkaloids, steroids, flavonoids, and phenols. The dominant contains of the G. verrucosa were glycerol (36.81%), hexadecenoic acid (20.74%), and cholesterol (7.4%). The hemostasis test showed that the 2.5% G. verrucosa extract gel had the shortest bleeding time, 33.98±5.33 seconds. On the seventh day of the initial proliferation phase, the number of fibroblasts was not significantly different among groups. On day 14, the number of fibroblasts was only significantly different between 10% G. verrucosa and untreated group (p=0.007). On day 28, however, both 5% and 10% G. verrucosa were significantly higher compared to untreated group, both had p=0.010. Daily clinical examination showed that animals that were given 2.5% and 5% of G. verrucosa extract gel experienced wound closure on day 10. Animals treated with 10% of extract gel, all wounds healed on day 9. This study suggested that G. verrucosa extract could accelerate wound closure and wound healing.
Evaluation of atopic dermatitis severity using artificial intelligence Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Bulqiah, Mikyal; Idroes, Ghazi M.; Niode, Nurdjannah J.; Sofyan, Hizir; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 3 No. 3 (2023): December 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i3.511

Abstract

Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pre-trained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.
Optimizing antimicrobial synergy: Green synthesis of silver nanoparticles from Calotropis gigantea leaves enhanced by patchouli oil Kemala, Pati; Khairan, Khairan; Ramli, Muliadi; Helwani, Zuchra; Rusyana, Asep; Lubis, Vanizra F.; Ahmad, Khairunnas; Idroes, Ghazi M.; Noviandy, Teuku R.; Idroes, Rinaldi
Narra J Vol. 4 No. 2 (2024): August 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i2.800

Abstract

Silver nanoparticles (AgNPs) synthesized from plant extracts have gained attention for their potential applications in biomedicine. Calotropis gigantea has been utilized to synthesize AgNPs, called AgNPs-LCg, and exhibit antibacterial activities against both Gram-positive and Gram-negative bacteria as well as antifungal. However, further enhancement of their antimicrobial properties is needed. The aim of this study was to synthesize AgNPs-LCg and to enhance their antimicrobial and antifungal activities through a hybrid green synthesis reaction using patchouli oil (PO), as well as to characterize the synthesized AgNPs-LCg. Optimization was conducted using the response surface method (RSM) with a central composite design (CCD). AgNPs-LCg were synthesized under optimal conditions and hybridized with different forms of PO—crude, distillation wastewater (hydrolate), and heavy and light fractions—resulting in PO-AgNPs-LCg, PH-AgNPs-LCg, LP-AgNPs-LCg, and HP-AgNPs-LCg, respectively. The samples were then tested for their antibacterial (both Gram-positive and Gram-negative bacteria) and antifungal activities. Our data indicated that all samples, including those with distillation wastewater, had enhanced antimicrobial activity. HP-AgNPs-LCg, however, had the highest efficacy; therefore, only HP-AgNPs-LCg proceeded to the characterization stage for comparison with AgNPs-LCg. UV-Vis spectrophotometry indicated surface plasmon resonance (SPR) peaks at 400 nm for AgNPs-LCg and 360 nm for HP-AgNPs-LCg. The Fourier-transform infrared spectroscopy (FTIR) analysis confirmed the presence of O-H, N-H, and C-H groups in C. gigantea extract and AgNP samples. The smallest AgNPs-LCg were 56 nm, indicating successful RSM optimization. Scanning electron microscopy (SEM) analysis revealed spherical AgNPs-LCg and primarily cubic HP-AgNPs-LCg, with energy-dispersive X-ray spectroscopy (EDX) confirming silver's predominance. This study demonstrated that PO in any form significantly enhances the antimicrobial properties of AgNPs-LCg. The findings pave the way for the exploration of enhanced and environmentally sustainable antimicrobial agents, capitalizing on the natural resources found in Aceh Province, Indonesia.
Uncovering anti-inflammatory potential of Lantana camara Linn: Network pharmacology and in vitro studies Khairan, Khairan; Maulydia, Nur B.; Faddillah, Vira; Tallei, Trina E.; Fauzi, Fazlin M.; Idroes, Rinaldi
Narra J Vol. 4 No. 2 (2024): August 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i2.894

Abstract

Lantana camara Linn contains a diverse array of metabolites that exhibit therapeutic potential. The aim of this study was to evaluate the potential of L. camara leaves, which were collected at the Ie-Seu'um geothermal area in Aceh, Indonesia, as an anti-inflammatory through network pharmacology and in vitro analysis. The ethanolic extract derived from L. camara underwent identification utilizing gas chromatography-mass spectrometry (GC-MS) to verify chemical constituents for drug-likeness properties. The evaluation of anti-inflammatory activity included network pharmacology and a series of in vitro investigations using two methods: protein inhibition and albumin denaturation assays. The findings revealed that the extract contained a domination of terpenoids and fatty acids class, which met the evaluation criteria of drug-likeness. Network pharmacology analysis identified the top five key proteins (peroxisome proliferator-activated receptor gamma, prostaglandin G/H synthase 2, epidermal growth factor receptor, hypoxia-inducible factor 1-alpha, and tyrosine protein kinase-Janus kinase 2) involved in inflammation-related protein-protein interactions. Gene ontology enrichment highlighted the predominance of inflammatory responses in biological processes (BP), cytoplasm in cellular components (CC), and oxidoreductase activity in molecular functions (MF). In vitro analysis showed that the extract inhibited protein activity and protein denaturation with inhibitory concentration (IC50) values of 202.27 and 223.85 ppm, respectively. Additionally, the extract had antioxidant activity with DPPH- and ABTS-scavenging IC50 values of 140 ppm and 163 ppm, respectively. Toxicological assessment by brine shrimp lethality assay (BSLA), yielding a lethal concentration (LC50) value of 574 ppm (essentially non-toxic) and its prediction via ProTox 3.0 that indicated non-active in hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity, and cytotoxicity. These results suggested that L. camara holds noteworthy effectiveness as a potential candidate for complementary medicine in the realm of inflammatory agents, warranting further investigation in clinical settings.
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.
Co-Authors - Fakhrurrazi - Mahmud Abas, Abdul Hawil Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Agus Winarsih Ahmad, Khairunnas Ahmad, Noor Atinah Ahsya, Yahdina Akyuni, Qurrata Amirah, Kelsy Andri Yadi Paembonan Arini, Musfira Asep Rusyana Azhar, Fauzul Azharuddin Azharuddin BAKRI, TEDY KURNIAWAN Binawati Ginting Boy M. Bachtiar Claus Jacob Claus Jacob Claus Jacob, Claus Cundaningsih, Nurvita Deni Saputra Destiana, Khaerunisa Dharma, Aditia Dharma, Dian Budi Diah, Muhammad Dian Handayani Dian Lestari, Nova Diana Setya Ningsih, Diana Earlia, Nanda EKA SAFITRI Eka Safitri Eka Safitri El-Shazly, Mohamed Elisa Purwaendah Emran, Talha Bin Enitan, Seyi Samson Erkata Yandri Essy Harnelly Estevam, Ethiene Castellucci Ethiene Castellucci Estevam Eti Rohaeti Evi Yufita Ezzat, Abdelrahman O. Faddillah, Vira Faisal Abdullah Faisal, Farassa Rani Faradilla Faradilla FARADILLA, FARADILLA Farnida Farnida Fatimawali . Fauzi, Fazlin M. Fauzi, Fazlin Mohd Fazlin Mohd Fauzi Firaihanil Jannah Ghalieb Mutig Idroes Ghani, Azman Abdul Ghazi Mauer Idroes Haerul Anwar Hakim, Rachmi F. Hanafiah, Olivia A. Harera, Cheariva Firsa Hartono Hartono Hesti Meilina Hizir Sofyan Husdayanti, Noviana Ida Zahrina Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Idroes, Ghifari Maulana Iin Shabrina Hilal Ilham Maulana Ilham Maulana Imelda, Eva Imran Imran Ira Maya Irma Sari Irsan Hardi Irvanizam, Irvanizam Isa, Illyas Md Ismail Ismail Isnaini, Nadia Isra Firmansyah, Isra Jannah, Firaihanil Jannah, Rizka Auliatul Jasin, Faisal M Kairupan, Tara S. Karl Herbert Schaefer Karl Herbert Schaefer, Karl Herbert Karomah, Alfi Hudatul Kemala, Pati Khairan . Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan Khairan KHAIRI SUHUD Khairi Suhud Khalijah Awang Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lelifajri Lelifajri Lelifajri Lelifajri Lubis, Vanizra F. M. Rafi M. Yogi Riyantama Isjoni Madya, Muhammad Miftahul Mahmudi Mahmudi Maimun Syukri, Maimun Malahayati Malahayati MARIA BINTANG Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur B. Maulydia, Nur Balqis Maysarah, Hilda Md Sani, Nor Diyana Mikyal Bulqiah, Mikyal Mirda, Erisna Misbullah, Alim Misrahanum Misrahanum Mohamed Yusof, Nur Intan Saidaah Mohd Fauzi, Fazlin Mohsina Patwekar Mubaraq, Farhil Muhammad Bahi Muhammad Bahi Muhammad Bahi Muhammad Bahi Muhammad Diah Muhammad Ridha Adhari, Muhammad Ridha Muhammad Subianto Muhammad Yanis Muhammad Yusuf Mukhlisuddin Ilyas Muliadi Ramli Munawar, Agus Murniana Murniana Mursal Mursal Mursyida, Waliam Musdalifah, Annisa Muslem Muslem, Muslem Muzakir N. Nazaruddin Nabila, Fiki Farah Nainggolan, Sarah Ika Nanda Earlia Nasrullah Idris Nasrullah Idris NAZARUDDIN NAZARUDDIN Nazaruddin Nazaruddin Neonufa, Godlief Frederick Ningsih, Diana S. Niode, Nurdjannah Jane Nor Diyana Md Sani Novi Reandy Sasmita Noviandy, Teuku R. Nugraha, Gartika Nur Balqis Maulydia Nur, Adrian Rahmat Nurdjannah J. Niode Nurleila, Nurleila Nurul Khaira Oesman, Frida Patwekar, Faheem Patwekar, Mohsina Prakoeswa, Cita RS. Purwaendah, Elisa Putra, Noviandi I. Qurrata Akyuni Rahmadi Rahmadi Rahmadi Rahmadi Rahman, Isra Farliadi Rahman, Sunarti Abd Raihan Raihan Raihan Raihan, Raihan Raudhatul Jannah Razief Perucha Fauzie Afidh Ringga, Edi Saputra Rizka Auliatul Jannah Rizkia, Tatsa Romadhoni, Yenni Rusdi Andid Safhadi, Aulia Al-Jihad Saiful . Saiful Saiful Salaswati, Salaswati Salsabila, Indah Sasmita, Novi Reandy Satrio, Justinus Septaningsih, Dewi Anggraini Shafira, Ghina A. Siti Aisyah Solly Aryza Souvia Rahimah Sufriadi, Elly sufriani, sufriani Sugara, Dimas Rendy Suhendra, Rivansyah Suhud, Khairi Supriatno Supriatno Supriatno Suryadi Suryadi Suryawati Suryawati Taopik Ridwan Taufik Ridwan Taufiq Karma Teuku Rizky Noviandy Teuku Zulfikar Thomas Schneider Thomas Schneider, Thomas Triana Hertiani Trina E. Tallei, Trina E. TRINA EKAWATI TALLEI Trina Ekawati Tallei Tuti Fadlilah Zahraty, Ifrah Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulfiani, Utari Zulkarnain Jalil Zulkarnain Jalil