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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

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.

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

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

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.

Prognostication of differentiated thyroid cancer recurrence: An explainable machine learning approach Idroes, Ghazi M.; Noviandy, Teuku R.; Idroes , Ghalieb M.; Hardi, Irsan; Duta, Teuku F.; Hamoud, Lama MA.; Al-Gunaid , Hala T.
Narra X Vol. 2 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v2i3.183

Differentiated thyroid cancer (DTC) generally has a favorable prognosis, but recurrence remains a concern for a subset of patients, highlighting the need for accurate predictive tools. While traditional methods, such as the American Thyroid Association (ATA) guidelines, are widely used, they may not fully capture the complex patterns in clinical data. To address this, we developed a machine learning model using LightGBM and enhanced its interpretability with SHAP (SHapley Additive exPlanations). Our model, trained on data from 383 DTC patients, identified response to initial therapy as the most significant predictor of recurrence, alongside age and risk level. The model achieved an accuracy of 93.51%, with precision and sensitivity of 94.23% and 96.08%, respectively, using only five key features selected through Recursive Feature Elimination (RFE). SHAP analysis provided clear insights into how these features influenced predictions, offering a transparent and interpretable approach to risk stratification. These results highlight the potential of explainable machine learning to improve recurrence prediction, support personalized care, and build clinician trust, while laying the groundwork for further validation in diverse populations.

Prognostication of differentiated thyroid cancer recurrence: An explainable machine learning approach Idroes, Ghazi M.; Noviandy, Teuku R.; Idroes , Ghalieb M.; Hardi, Irsan; Duta, Teuku F.; Hamoud, Lama MA.; Al-Gunaid , Hala T.
Narra X Vol. 2 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v2i3.183

Differentiated thyroid cancer (DTC) generally has a favorable prognosis, but recurrence remains a concern for a subset of patients, highlighting the need for accurate predictive tools. While traditional methods, such as the American Thyroid Association (ATA) guidelines, are widely used, they may not fully capture the complex patterns in clinical data. To address this, we developed a machine learning model using LightGBM and enhanced its interpretability with SHAP (SHapley Additive exPlanations). Our model, trained on data from 383 DTC patients, identified response to initial therapy as the most significant predictor of recurrence, alongside age and risk level. The model achieved an accuracy of 93.51%, with precision and sensitivity of 94.23% and 96.08%, respectively, using only five key features selected through Recursive Feature Elimination (RFE). SHAP analysis provided clear insights into how these features influenced predictions, offering a transparent and interpretable approach to risk stratification. These results highlight the potential of explainable machine learning to improve recurrence prediction, support personalized care, and build clinician trust, while laying the groundwork for further validation in diverse populations.

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