A geochemistry study has been done in four geothermal manifestations—Ie-Seu’um, Ie-Brôuk, Ie-Jue and the Van-Heutz crater—located in the north zone of Seulawah Agam mountain (Aceh Besar District, Indonesia). The study was performed through water and gas analysis. Water analysis were done for all geothermal manifestations, but gas analysis was only done for the Ie-Jue manifestation that has fumaroles. Cation and anion contents were analyzed by ion chromatography, ICP-OES, alkalimetry titrations, and spectrophotometry, meanwhile isotopes were measured by a Liquid Water Isotope Analyzer. The resulting data were used for fluid and gas geothermometry calculations, and plotted in a FT-CO₂ Cross-Plot and a CH₄-CO₂-H₂S triangle diagram to obtain reservoir temperatures. The data were also plotted by a Cl-HCO₃-SO₄ triangle and Piper diagram to obtain the water type and dominant chemical composition, a Na-K-Mg triangle diagram to obtain fluid equilibria, the isotope ratio in the stable isotope plot to obtain the origin of water, and a N₂-He-Ar triangle diagram to establish the origin of fumaroles. The water analysis results showed that (1) Ie-Seu’um has an average reservoir temperature of 241.9 ± 0.3 °C, a chloride water type, a dominant Na-K-Cl chemical composition, a mature water fluid equilibrium, and water of meteoric origin; (2) Ie-Brôuk has an average reservoir temperature of 321.95 ± 13.4 °C, a bicarbonate water type, a dominant Na-Ca-HCO₃chemical composition, an immature water fluid equilibrium, and water of meteoric origin; (3) Ie-Jue has an average reservoir temperature of 472.4 ± 91.4 °C, a sulphate water type, a dominant Ca-SO₄ chemical composition, an immature water fluid equilibrium and water of meteoric origin; and (4) the Van-Heutz crater has an average reservoir temperature of 439.3 ± 95.3 °C, a sulphate water type, a dominant Ca-SO₄ chemical composition, an immature water fluid equilibrium and water of magmatic origin. The results of our gas analysis showed that Ie-Jue has an average reservoir temperature of 258.85 °C, and water of meteoric origin. Based on the reservoir temperatures, the geothermal manifestation of the north zone of Seulawah Agam mountain is considered as a high-temperature geothermal system suitable for power plant development.

This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC₅₀ values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.

Analysis of fatty acid contents and pharmacological properties of Pliek U oil was performed. Fatty acids were analyzed by gas chromatography-mass spectrometry (GC-MS), and pharmacological properties based on its potential on filament-aggregating protein (filaggrin) were studied with bioinformatics approach by the reverse docking technique using palmitic acid as a control compound. Two Pliek U extracts, namely, Pliek U oil (PUO) and ethanolic Pliek U oil extract (EPUOE), were prepared. The GC-MS results revealed that lauric acid, myristic acid, palmitic acid, and oleic acid are the predominant fatty acids, with lauric acid being the abundant one in all Pliek U oil extracts. The reverse docking technique results showed that oleic acid had the most stable interaction to filaggrin with the lowest binding affinity (−6.1 kcal/mol). Oleic acid and palmitic acid have one same side binding to filaggrin on amino acid LEU D75. These findings indicated that oleic acid has the best potential to be used as a drug candidate in atopic dermatitis treatment.

Hepatitis C virus (HCV) infection affects over 71 million people worldwide, leading to severe liver diseases, including cirrhosis and hepatocellular carcinoma. The virus’s high mutation rate complicates current antiviral therapies by promoting drug resistance, emphasizing the need for novel therapeutics. Traditional high-throughput screening (HTS) methods are costly, time-consuming, and prone to false positives, underscoring the necessity for more efficient alternatives. Machine learning (ML), particularly quantitative structure–activity relationship (QSAR) modeling, offers a promising solution by predicting compounds’ biological activity based on chemical structures. However, the “black-box” nature of many ML models raises concerns about interpretability, which is critical for understanding drug action mechanisms. To address this, we propose an explainable multi-model stacked classifier (MMSC) for predicting hepatitis C drug candidates. Our approach combines random forests (RF), support vector machines (SVM), gradient boosting machines (GBM), and k-nearest neighbors (KNN) using a logistic regression meta-learner. Trained and tested on a dataset of 495 compounds targeting HCV NS3 protease, the model achieved 94.95% accuracy, 97.40% precision, and a 96.77% F1-score. Using SHAP values, we provided interpretability by identifying key molecular descriptors influencing the model’s predictions. This explainable MMSC approach improves hepatitis C drug discovery, bridging the gap between predictive performance and interpretability while offering actionable insights for researchers.

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.

Cardiovascular Disease (CVD), a prevalent global health concern involving heart and blood vessel disorders, prompts this research's focus on accurate prediction. This study explores the predictive capabilities of the Gradient Boosting Classifier (GBC) in cardiovascular disease across two datasets. Through meticulous data collection, preprocessing, and GBC classification, the study achieves a noteworthy accuracy of 97.63%, underscoring the GBC's effectiveness in accurate CVD detection. The robust performance of the GBC, evidenced by high accuracy, highlights its adaptability to diverse datasets and signifies its potential as a valuable tool for early identification of cardiovascular diseases. These findings provide valuable insights into the application of machine learning methodologies, particularly the GBC, in advancing the accuracy of CVD prediction, with implications for proactive healthcare interventions and improved patient outcomes.