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Characterizing the Size Distribution of Silver Nanoparticles Biofabricated Using Calotropis gigantea from Geothermal Zone Kemala, Pati; Khairan, Khairan; Ramli, Muliadi; Mauer Idroes, Ghazi; Mirda, Erisna; Setya Ningsih, Diana; Tallei, Trina Ekawati; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i2.21

Abstract

This research aims to synthesize silver nanoparticles (AgNPs) using an aqueous leaf extract of Calotropis gigantea obtained from the geothermal manifestation Ie Seu-Um, Aceh Besar, Aceh Province, Indonesia. The C. gigantea leaf extract was mixed with AgNO3 solutions at concentrations of 2, 5, and 9 mM, respectively. The mixture was stirred at 80 rpm by a magnetic stirrer for 48 hours in the dark. The change in solution color indicated the reduction of Ag+ to Ag0. The resulting AgNPs synthesized using C. gigantea leaf extract (AgNPs-LCg) exhibited cloudy grey, reddish dark brown, and light brown colors when synthesized with AgNO3 concentrations of 2, 5, and 9 mM, respectively. The particle sizes of AgNPs-LCg had maximum frequencies at 246.98 nm (synthesized using AgNO3 2 mM), 93.02 nm (synthesized using AgNO3 5 mM), and 171.25 nm (synthesized using AgNO3 9 mM). The zeta potential values of AgNPs-LCg using 2, 5, and 9 mM AgNO3 were -41.9, -40.1, and -31.4 mV, respectively. Based on the solution color, nanoparticle size, and stability value of AgNPs, it can be concluded that the use of AgNO3 at 5 mM is optimal for the green synthesis process of AgNPs-LCg.
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.
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.
Geothermal Flora and AgNPs Synergy: A Study on the Efficacy of Lantana camara and Acrostichum aureum-Infused Hand Sanitizers Harera, Cheariva Firsa; Maysarah, Hilda; Kemala, Pati; Idroes, Ghazi Mauer; Maulydia, Nur Balqis; Patwekar, Mohsina; Idroes, Rinaldi
Grimsa Journal of Science Engineering and Technology Vol. 2 No. 2 (2024): October 2024 (In Press)
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v2i2.38

Abstract

Hand hygiene is an important factor that needs to be observed in controlling the spread of diseases transmitted through hand-to-hand contact. Synthesis of silver nanoparticles from tembelekan (Lantana camara) and paku laut (Acrostichum aureum) using the green synthesis method has good antibacterial activity against Staphylococcus aureus and Escherichia coli bacteria. Therefore, a preparation formulation was made, namely hand sanitizer, which is still rarely used. Formulations that have successfully entered the evaluation stage include organoleptic tests, homogeneity tests, spreadability tests, adhesion tests, viscosity tests, pH tests, accelerated stability tests, and irritation tests. Antibacterial activity was evaluated against bacteria Staphylococcus aureus and Escherichia coli. The hand sanitizer is formulated to contain 5% tembelekan AgNPs (F1); paku laut AgNPs 5% (F2); and a combination of 2.5% paku laut AgNPs and 2.5% tembelekan AgNPs. The resulting hand sanitizer has good organoleptic characteristics, except for the color of the preparation, which changed during the accelerated stability test. Test results for pH, adhesion, spreadability, viscosity, and homogeneity of hand sanitizer meet the requirements of a good test. Irritation tests on ten volunteers showed no irritation reaction. Antibacterial tests show that hand sanitizer has bacterial antibacterial activity with an average ± standard deviation of the inhibition zone Staphylococcus aureus is 6.605±0.459(F1); 6.665±0.615(F2); 6.380±0.282(F3) dan Escherichia coli namely 6.575 ± 0.219 (F1); 6.860 ± 0.155 (F2); 6.810 ± 0.056 (F3). Making hand sanitizer AgNPs-based ingredients from plants can be used as hand sanitizer, but stabilizers are required to prevent color changes during storage.
Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Maulana, Aga; Idroes, Ghazi Mauer; Kemala, Pati; Maulydia, Nur Balqis; Sasmita, Novi Reandy; Tallei, Trina Ekawati; Sofyan, Hizir; Rusyana, Asep
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.132

Abstract

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.