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Journal : Malacca Pharmaceutics

A Review of the Ethno-dentistry Activities of Calotropis gigantea Diana Setya Ningsih; Ismail Celik; Abdul Hawil Abas; Boy Muhclis Bachtiar; Pati Kemala; Ghazi Mauer Idroes; Nur Balqis Maulydia
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 Fajar Fakri; Saima Putri Harahap; Akmal Muhni; Khairan Khairan; Yuni Tri Hewindati; Ghazi Mauer Idroes
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 Teuku Rizky Noviandy; Aga Maulana; Ghazi Mauer Idroes; Nur Balqis Maulydia; Mohsina Patwekar; Rivansyah Suhendra; Rinaldi Idroes
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.