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All Journal PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS (COMORBID) TRANSPUBLIKA INTERNATIONAL RESEARCH IN EXACT SCIENCES (TIRES) Jurnal Pengabdian Masyarakat Bangsa 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 Grimsa Journal of Science Engineering and Technology Grimsa Journal of Business and Economics Studies
Ghazi Mauer Idroes
Department Of Occupational Health And Safety, Faculty Of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
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Journal : malacca pharmaceutics

 1 
A Review of the Ethno-dentistry Activities of Calotropis gigantea Ningsih, Diana Setya; Celik, Ismail; Abas, Abdul Hawil; Bachtiar, Boy Muhclis; Kemala, Pati; Idroes, Ghazi Mauer; Maulydia, Nur Balqis
Malacca Pharmaceutics Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i1.31

Abstract

Calotropis gigantea is a medicinal herb that thrives in arid climates. All parts of this plant are rich in secondary metabolites, which are very beneficial for health. Phytochemicals of this plant include flavonoid, alkaloids, steroids, cardiac glycosides, and terpenoids, which have a wide range of pharmacological effects. The potential of metabolit compound from C. gigantea can be used in dental treatment. This review describes the potential use of C. gigantea in ethno-dentistry, specifically as anti-caries, soft tissue inflammation (periodontitis and gingivitis), degenerative diseases (tumor/cancer), and wound healing. This review provides general perspectives and basic literature on the use of C. gigantea in the field of etno-dentistry.
Antimicrobial Properties of Medicinal Plants in the Lower Area of Ie Seu-um Geothermal Outflow, Indonesia Fakri, Fajar; Harahap, Saima Putri; Muhni, Akmal; Khairan, Khairan; Hewindati, Yuni Tri; Idroes, Ghazi Mauer
Malacca Pharmaceutics Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i2.44

Abstract

The lower area of the Ie Seu-um manifestation, located in Ie Seu-um village, Aceh Besar District, harbors several medicinal plants that exhibit potential for the treatment of infectious diseases. This study aims to assess the secondary metabolite content and in vitro antimicrobial activity against Staphylococcus aureus, Escherichia coli, and Candida albicans of medicinal plants inhabiting the geothermal region. Medicinal plants, namely Pluchea indica (L.) Less., Acrostichum aureum L., Acacia mangium L., and Calotropis gigantea (L.) Dryand., were collected within a range of 100-150 meters from the hot springs in the lower area. Methanol extracts of these medicinal plants underwent phytochemical screening and were tested for antimicrobial activity using the Kirby-Bauer disc diffusion method at a concentration of 50%. The results of phytochemical screening demonstrated positive variations in alkaloids, flavonoids, saponins, steroids, triterpenoids, and tannins for each medicinal plant. The antimicrobial activity of the methanol extracts noticeably inhibited the growth of S. aureus compared to E. coli and C. albicans. The largest inhibition zones were observed for the leaf part of A. mangium (12.70 ± 2.30 mm) against S. aureus, the aerial part of A. aureum (11.57 ± 2.01 mm) against E. coli, and the aerial part of P. indica (9.89 ± 1.11 mm) against C. albicans. Based on the research findings, medicinal plants originating from the lower area of the Ie Seu-um manifestation exhibit potential as antimicrobial agents, particularly against gram-positive bacteria.
Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Maulydia, Nur Balqis; Patwekar, Mohsina; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i2.60

Abstract

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.
Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma Noviandy, Teuku Rizky; Imelda, Eva; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 1 (2025): March 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i1.271

Abstract

Glaucoma is a leading cause of irreversible blindness, primarily managed by lowering intraocular pressure (IOP). Carbonic Anhydrase-II (CA-II) inhibitors play a crucial role in this treatment by reducing aqueous humor production. However, existing CA-II inhibitors often suffer from poor selectivity, side effects, and limited bioavailability, highlighting the need for more efficient and targeted drug discovery approaches. This study uses machine learning-driven Quantitative Structure-Activity Relationship (QSAR) modeling to predict CA-II inhibition based on molecular descriptors, significantly enhancing screening efficiency over traditional experimental methods. By evaluating multiple machine learning models, including Support Vector Machine, Gradient Boosting, and Random Forest, we identify SVM as the most effective classifier, achieving the highest accuracy (83.70%) and F1-score (89.36%). Class imbalance remains challenging despite high sensitivity, necessitating further improvements through resampling and hyperparameter optimization. Our findings underscore the potential of machine learning-based virtual screening in accelerating CA-II inhibitor identification and advocate for integrating AI-driven approaches with traditional drug discovery techniques. Future directions include deep learning enhancements and hybrid machine learning-docking frameworks to improve prediction accuracy and facilitate the development of more potent and selective glaucoma treatments.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Ahmad, Noor Atinah Akmal Muhni Alfizar Alfizar Ali Bakri Anggi, Tiara Aprianto . Arkadinata, Teguh Asep Rusyana Azhar, Fauzul Azis, Haikal Bachtiar, Boy Muhclis Bahri, Ridzky Aulia Bako, Winanda Celik, Ismail Delya, Mussa Isaack Delya, Mussa Issack Diah, Muhammad Diana Setya Ningsih, Diana Diana Setya Ningsih, Diana Setya Diki, Diki Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Erkata Yandri Faisal, Farassa Rani Fajar Fakri Fauziah, Niken Fazli, Qalbin Salim Hafni Zahara Harahap, Saima Putri Harera, Cheariva Firsa Hewindati, Yuni Tri Hizir Sofyan Idroes, Ghalieb Mutig Ifandi, Ilham Imelda, Eva Irvanizam, Irvanizam Irwana, Salman Jainury, Aldi Jauna, Jauna Kemala, Pati Khairan Khairan Khalijah Awang Kihwili, Erick Hironimus Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lukman Hakim Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Maysarah, Hilda Medyan Riza Mirda, Erisna Mirja, Mirja Misbullah, Alim Mohsina Patwekar Muhammad Adam, Muhammad Muhammad Ichsan Muhammad Ichsan Muhammad Subianto Muhammad Yanis Muhammad Yusuf Mukhlisuddin Ilyas Muliadi Ramli Muslem Muslem, Muslem Musvira, Intan Natasya Natasya Nizamuddin Nizamuddin Nova Yanti Pasyamei Rembune Kala Patwekar, Mohsina Pebriani, Liska Nova Prasetio, Rasi Purnama, M. Risky Putri Raisah Raisah, Putri Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Rizkia, Tatsa Sasmita, Novi Reandy Shofi, Shofi Siti Maulina Rukmana Souvia Rahimah Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Surna, Muhammad Ipan Susanna Susanna Syamsiar, Syamsiar Taufiq Karma Teuku Rizky Noviandy Teuku Rizky Noviandy Teuku Zulfikar Tjut Chamzurni TRINA EKAWATI TALLEI Wahyuni, Srie Wangi, Putri Ayu Sekar Widya FATRIASARI Wildan Seni, Wildan Wiranatakusuma, Dimas Bagus Wiwik Handayani Yustiana Yustiana, Yustiana Zahriah, Zahriah Zuchra Helwani, Zuchra Zulkarnain Jalil