Heca Journal of Applied Sciences

Investigating Scabies Pathogenesis and Therapeutic Potential of Nutmeg Extract in Experimental Animals

Erizal Erizal — 2026-01-12

Nutmeg (Myristica fragrans) is one of Indonesia’s agricultural commodities with recognized health benefits as a traditional medicine. In addition, nutmeg has potential as a natural treatment for scabies. This study aimed to evaluate the effectiveness of nutmeg fruit bioactive compounds in scabies treatment. Nutmeg extraction was conducted using three different solvents: ethanol, ethyl acetate, and n-hexane. The extract with the most dominant phytochemical composition was further analyzed for bioactive compounds using GC-MS and antioxidant activity using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. Subsequently, the extract was tested against Sarcoptes scabiei mites obtained from 15 stray cats. The effectiveness of nutmeg extract was evaluated in a spray formulation by observing mite mortality and lesion reduction. The results demonstrated that the ethanol extract of nutmeg fruit contained the most abundant phytochemicals, with 3-Methyl-2,5-Furandione (21.26%) and Maleic Anhydride (14.21%) as the dominant compounds. The ethanol extract also exhibited strong antioxidant activity with an IC₅₀ value of 21.41 ppm. In vitro testing showed 100% mite mortality at a 25% extract concentration within 24 hours, while in vivo testing on scabies-infected cats treated with the nutmeg spray extract revealed a significant reduction in scab lesions compared to the control group. These findings indicate that nutmeg extract possesses potent acaricidal and antioxidant properties, making it a promising alternative treatment for scabies. Further studies are required to refine the formulation and explore its clinical applications.

Structural Feasibility Assessment of an Adjustable-Height Photovoltaic Mounting System Using Conceptual Design and Finite Element Simulation

Muhammad Ihsan Nur Faizin — 2026-02-03

The performance of photovoltaic (PV) systems is influenced not only by module efficiency but also by the flexibility and structural reliability of mounting systems, particularly those allowing height and tilt adjustments to accommodate site-specific and seasonal variations. While automatic tracking systems can increase energy yield, their high cost and mechanical complexity limit widespread adoption, underscoring the need for simpler, more economical alternatives. This study evaluates the structural feasibility of an adjustable-height PV mounting system that improves installation flexibility while maintaining mechanical integrity. A conceptual engineering design approach was employed to develop a modular mounting structure with a mechanical height-adjustment mechanism. Structural performance was assessed using finite element–based static simulations under gravitational loading representative of a commercial bifacial PV module. The evaluation focused on Von Mises stress distribution, total deformation, and safety factor as indicators of mechanical reliability at the conceptual design stage. The results indicate that maximum Von Mises stress remains well below the assumed material yield strength, while total deformation is negligible relative to overall structural dimensions. The calculated safety factor confirms an adequate structural safety margin, indicating that integrating a height adjustment mechanism does not compromise structural stability. The proposed mounting system demonstrates sufficient structural feasibility and mechanical simplicity for early-stage development, offering a practical, adaptable solution for ground-mounted and rooftop PV installations.

The Evolving Landscape of the Colorectal Cancer Vaccines: From Biological Mechanisms to Translational Therapeutics

Sabrina Brigitta Valerie Setiono — 2026-02-27

Colorectal cancer (CRC) remains a major global health burden, and despite substantial advances in cancer immunotherapy, the clinical efficacy of therapeutic cancer vaccines in CRC has been limited. This review critically examines the biological, immunological, and translational factors that shape CRC vaccine development, with a particular focus on tumor immunopathology, antigen selection, vaccine platforms, and emerging combination strategies. We summarize current knowledge on CRC-associated tumor antigens, including selected tumor-associated antigens and neoantigen-based approaches, alongside major vaccine modalities evaluated in preclinical and early-phase clinical studies. Across the literature, vaccine-induced immunogenicity frequently exceeds demonstrated clinical benefit, highlighting a persistent translational gap. Synthesis of available evidence suggests that this gap is driven primarily by CRC-specific immune constraints, including immune exclusion, dominance of immunologically cold MSS/pMMR tumors, and tolerogenic pressures within metastatic niches, particularly the liver. We further discuss how rational combination strategies, especially those integrating cancer vaccines with immune checkpoint inhibitors (ICIs), may partially overcome these barriers. In addition, the review outlines the conceptual role of bioinformatics and immunoinformatics in supporting antigen prioritization, neoantigen discovery, and patient stratification in CRC vaccine research. Overall, this review emphasizes that future progress will depend on CRC-tailored antigen selection, mechanistically informed vaccine design, rational combination regimens, and rigorous clinical evaluation to define the realistic clinical role of therapeutic cancer vaccines in CRC.

Integrative Network Pharmacology Study of Cordyceps militaris Compounds for Prostate Cancer Treatment

Sarah Cecilia Astrid Laihad — 2026-03-30

Prostate cancer remains one of the leading causes of cancer-related mortality in men, while adverse effects and the development of drug resistance often limit current therapeutic strategies. Natural products have gained increasing attention as potential sources of novel anticancer agents due to their multitarget properties and relatively low toxicity. Cordyceps militaris, a medicinal fungus rich in bioactive compounds, has been reported to exhibit anticancer activity; however, its compound-target interactions in prostate cancer have not been comprehensively elucidated. This study aimed to explore the interactions between C. militaris bioactive compounds and prostate cancer-associated targets using a pharmacology network-based in silico approach. A total of 50 bioactive compounds were collected from metabolite profiling studies, of which 19 compounds were selected based on high predicted TP53 expression enhancer activity (Pa ≥ 0.7) using WAY2DRUG PASS analysis. Protein targets were predicted using SwissTargetPrediction and the Similarity Ensemble Approach, and then intersected with prostate cancer-associated proteins retrieved from GEPIA2, GeneCards, and OMIM, yielding 499 overlapping targets. Protein interaction network analysis was performed using STRING and visualized in Cytoscape, enabling the identification of key hub proteins based on the applied parameters, highlighting ten key proteins, including SRC, ESR1, MAPK1, AKT1, HSP90AA1, MAPK3, HSP90AB1, EGFR, GRB2, and PRKACA, within the interaction network. Pathway enrichment analysis indicated that these targets were predominantly involved in cancer-associated signaling pathways, such as the EGFR tyrosine kinase inhibitor resistance pathway. Furthermore, the results revealed that the selected compounds interact with these key prostate cancer-associated proteins. Pharmacokinetic and toxicity evaluation predicted favorable drug-likeness and acceptable safety profiles for selected compounds. Overall, this study highlights the potential of C. militaris bioactive compounds as promising alternative for prostate cancer through multitarget modulation of clinically relevant signaling pathways. Further experimental validation is still required to confirm these findings.

Comparative Analysis of Ensemble Machine Learning Models for QSAR-Based Prediction of Anticoagulant Activity in Thrombotic Disorders

Teuku Rizky Noviandy — 2026-03-31

Thrombotic disorders remain a major cause of global morbidity and mortality, with dysregulation of blood coagulation pathways playing a central role in disease progression. In particular, Thrombin is a key therapeutic target for anticoagulant drug development, making accurate prediction of inhibitory activity highly relevant for accelerating discovery efforts. Despite advances in computational drug discovery, there is still a need for systematic evaluation of machine learning approaches for QSAR-based prediction of anticoagulant activity. Many existing studies focus on single models or lack consistent comparison frameworks, limiting insights into the relative performance of different ensemble techniques. To address this gap, this study explores the application of multiple ensemble machine learning methods, including Random Forest, XGBoost, Gradient Boosting, and Extra Trees, combined with hyperparameter optimization using random search. The main objective of this work is to conduct a comparative analysis of these ensemble models to predict pIC50 values for thrombin inhibitors using molecular descriptors derived from chemical structures. The results show that the Extra Trees model achieved the best overall performance, with an R² of 0.697, RMSE of 0.851, and MAE of 0.615 after tuning. Additionally, Gradient Boosting and XGBoost demonstrated significant improvement following hyperparameter optimization, highlighting the importance of model tuning in QSAR tasks. Overall, the study confirms that ensemble learning methods yield reliable, accurate predictions of anticoagulant activity, with Extra Trees emerging as the most effective approach for this dataset.