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All Journal Journal of Food and Pharmaceutical Science JOIV : International Journal on Informatics Visualization Journal of Food and Pharmaceutical Sciences
Anasyua, Fairuz Khairunnisa
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience

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Effects of Fermented Red Rice Bran on Gut Microbiota Modulation and Colorectal Cancer Prevention in a DMBA-Induced Mouse Model Anasyua, Fairuz Khairunnisa; Cahya, Sella Nur; Hanifah, Azka Khansa; Kusuma, Rio Jati; Rahmawati, Dini; Widyaningsih, Wahyu
Journal of Food and Pharmaceutical Sciences Vol 13, No 3 (2025): J.Food.Pharm.Sci
Publisher : Integrated Research and Testing Laboratory (LPPT) Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jfps.20690

Abstract

Colorectal cancer prevention is closely linked to gut microbiota modulation, particularly by increasing lactic acid bacteria (LAB) and short-chain fatty acids (SCFA). This study investigates the effect of fermented red rice bran on colon microbiota improvement. Fermented bran was produced by mixing red rice bran with water and yeast, followed by an analysis of the proximate composition, dietary fiber, resistant starch, antioxidants, and phenols. A quasi-experimental study was conducted on 30 mice, assigned to normal, negative control, 10% bran, 20% bran, 10% fermented bran, and 20% fermented bran groups. Colorectal cancer was induced using DMBA (20 mg/kgBW) intraperitoneally four times over two weeks. Treatment groups received bran or fermented bran ad libitum for 14 days. Cecal analysis showed that 20% red rice bran and 10% fermented bran significantly increased SCFA levels (p < 0.05), suggesting improved microbiota function, but did not significantly alter pH or LAB counts. These findings highlight the potential of fermented red rice bran in modulating gut microbiota, though further studies are needed to elucidate its long-term effects and mechanisms in colorectal cancer prevention.
Effects of Fermented Red Rice Bran on Gut Microbiota Modulation and Colorectal Cancer Prevention in a DMBA-Induced Mouse Model Anasyua, Fairuz Khairunnisa; Cahya, Sella Nur; Hanifah, Azka Khansa; Kusuma, Rio Jati; Rahmawati, Dini; Widyaningsih, Wahyu
Journal of Food and Pharmaceutical Sciences Vol 13, No 3 (2025): J.Food.Pharm.Sci
Publisher : Integrated Research and Testing Laboratory (LPPT) Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jfps.20690

Abstract

Colorectal cancer prevention is closely linked to gut microbiota modulation, particularly by increasing lactic acid bacteria (LAB) and short-chain fatty acids (SCFA). This study investigates the effect of fermented red rice bran on colon microbiota improvement. Fermented bran was produced by mixing red rice bran with water and yeast, followed by an analysis of the proximate composition, dietary fiber, resistant starch, antioxidants, and phenols. A quasi-experimental study was conducted on 30 mice, assigned to normal, negative control, 10% bran, 20% bran, 10% fermented bran, and 20% fermented bran groups. Colorectal cancer was induced using DMBA (20 mg/kgBW) intraperitoneally four times over two weeks. Treatment groups received bran or fermented bran ad libitum for 14 days. Cecal analysis showed that 20% red rice bran and 10% fermented bran significantly increased SCFA levels (p < 0.05), suggesting improved microbiota function, but did not significantly alter pH or LAB counts. These findings highlight the potential of fermented red rice bran in modulating gut microbiota, though further studies are needed to elucidate its long-term effects and mechanisms in colorectal cancer prevention.
Attention Mechanism with Kalman Smoothing Improved Long Short-Term Memory Mechanism for Obesity Weight Forecasting Pranolo, Andri; Utami, Nurul Putrie; Anasyua, Fairuz Khairunnisa
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4633

Abstract

This study aims to evaluate and compare the performance of several variants of Long Short-Term Memory (LSTM) based models in predicting obesity weight data. The main contribution of this research was to perform an extensive assessment of the effectiveness of LSTM-based models, including the combination of Attention-LSTM with Kalman Smoothing (KS), using two different data normalization methods (Z-score and Min-Max). This research used a publicly available dataset on obesity levels based on eating habits and physical condition, available at the UCI Machine Learning Repository. The models evaluated include the standard LSTM, Attention-LSTM, KS-LSTM, and the proposed KS-Attention-LSTM. The evaluation is conducted using the Root Mean Square Error (RMSE), the Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results showed that the proposed KS-Attention-LSTM model with Min-Max normalization achieved the lowest MAPE (0.28372) and the highest R² (0.79527) among the models. This suggests that the proposed model offers advantages in terms of prediction accuracy and has a good ability to handle data variations. Therefore, the KS-Attention-LSTM model with Min-Max normalization is strongly recommended for practical implementation, particularly for time-series data prediction in the health sector. This research is beneficial and contributes an effective alternative model that improves prediction accuracy, supports decision-making in the health sector, and enriches forecasting methods. 
Co-Authors Andri Pranolo Cahya, Sella Nur Dini Rahmawati Hanifah, Azka Khansa Kusuma, Rio Jati Utami, Nurul Putrie Wahyu Widyaningsih