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All Journal Jurnal Mandala Pharmacon Indonesia International Journal of Science and Society (IJSOC) Riset Informasi Kesehatan
Harfita, Nur Laily
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Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences

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Beta-sitosterol glycoside from Paraboea leuserensis and Cytotoxicity Test against MCF-7 Human Breast Cancer Cells Harfita, Nur Laily; Santoni, Adlis; Suryati, Suryati
Riset Informasi Kesehatan Vol 12 No 2 (2023): Riset Informasi Kesehatan
Publisher : Sekolah Tinggi Ilmu Kesehatan Harapan Ibu Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30644/rik.v12i2.826

Abstract

Background: Paraboea leuserensis, a plant endemic to the Leuser Mountain in the provinces of Aceh and North Sumatra, has been traditionally used for medicinal purposes by chewing or boiling, addressing conditions such as stomachaches and providing stamina enhancement. This study aimed to isolate secondary metabolite compounds from Paraboea leuserensis and conduct in vitro anticancer tests. Method: The ethyl acetate extract was isolated through column chromatography, yielding beta-sitosterol glycoside, and was tested for bioactivity against human breast cancer cells MCF-7 using the MTT method. Results: The compound demonstrated activity against breast cancer cells (MCF-7) with an IC50 value of 24.83 mg/L. Conclusion: Paraboea leuserensis exhibits potential anticancer activity. The isolation of beta-sitosterol glycoside from the plant and its demonstrated activity against MCF-7 human breast cancer cells suggest a promising avenue for further exploration of the plant's anticancer properties.
Analysis of Regression and Neural Network Models in Predicting Patient Visit Volume Harizahahyu; Friendly; Fathoni, Muhammad; Lase, Yuyun Yusnida; Prayudani, Santi; Harfita, Nur Laily
International Journal of Science and Society Vol 7 No 4 (2025): International Journal of Science and Society (IJSOC)
Publisher : GoAcademica Research & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/ijsoc.v7i4.1561

Abstract

Predicting patient visit volume plays a crucial role in supporting decision-making and resource allocation in healthcare services. This study aims to compare the performance of Multiple Linear Regression and an Artificial Neural Network (ANN) in forecasting patient visits at a dental clinic, using daily patient visit data and predictor variables such as holidays and promotional activities. Multiple regression was used to capture the linear relationship between the predictor and response variables, while ANN was applied to explore potential non-linear relationships. The results indicate that multiple regression outperformed the ANN, demonstrated by lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values, and provided clearer interpretability, making it more beneficial for healthcare practitioners, particularly in the context of a limited dataset. In contrast, the ANN tended to produce overestimates and was less responsive to short-term variations. Therefore, multiple regression can still be considered a reliable, efficient, and interpretable prediction method for clinical data with a moderate sample size, while future research is recommended to use larger datasets and test other machine learning algorithms to improve the accuracy and generalizability of the results.
QSAR Classification and Molecular Docking for Identifying BCL-2 Inhibitor Candidates Harfita, Nur Laily; Harizahayu, Harizahayu
Jurnal Mandala Pharmacon Indonesia Vol. 11 No. 2 (2025): Jurnal Mandala Pharmacon Indonesia 
Publisher : Program Studi Farmasi Universitas Mandala Waluya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35311/jmpi.v11i2.903

Abstract

B-cell lymphoma 2 (BCL-2) is an anti-apoptotic protein implicated in the progression and chemoresistance of multiple cancers. This study integrates QSAR-based machine learning (ML) classification and molecular docking to identify potential BCL-2 inhibitors. The Gradient Boosting classifier trained on PubChem fingerprints (881 bits) achieved the best predictive performance (accuracy: 83.52%, ROC-AUC: 0.8829). External virtual screening further identified high-probability active compounds, including Beclomethasone Dipropionate (0.9880) and Ulipristal Acetate (0.9866), highlighting their potential for drug repurposing. Docking analysis showed that Ulipristal Acetate exhibited the strongest binding affinity (–8.187 kcal/mol), forming a hydrogen bond with GLY A:145 and engaging key residues within the BH3-binding pocket of BCL-2. These findings demonstrate the effectiveness of QSAR-ML–assisted virtual screening in prioritizing repurposable candidates for BCL-2 inhibition.
Co-Authors Adlis Santoni Friendly Harizahahyu Harizahayu, Harizahayu Muhammad Fathoni Prayudani, Santi Suryati Suryati Yuyun Yusnida Lase