Universitas Ahmad Dahlan Journal
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Enzymatic virgin coconut oil effect on urea and creatinine levels of hypercholesterolemia-diabetics induced Wistar male rats
Get PDFCoconut (Cocos nucifera) is an Indonesian commodity that has high economic value tall. Virgin Coconut Oil (VCO) is one of the processed coconut products whose selling value is very high, because The composition of VCO consists of medium-chain fatty acids that can maintain a healthy body and prevent various diseases. The process of making VCO used in This research is an enzymatic method using pineapple weevil as a bromelain enzyme. This study aims to determine the effect of enzymatic administration of VCO and an enzymatic dose of VCO which is effective in reducing urea and creatinine levels in hypercholesterolemic-diabetic male white rats (Rattus norvegicus). This study was an experimental laboratory with a modified pretest and posttest randomized controlled group design using 30 test animals which were divided into 6 treatment groups. Each group consisted of 5 test animals, namely normal control, negative control, and positive control, with doses of 0.2, 0.4, and 0.8 mL/kg BW. The data obtained were analyzed using a One Way Anova and non- parametric statistical test by Kruskal Wallis. test and followed by a further Mann Whitney test to determine differences between treatments. The results showed that enzymatic VCO at a dose of 0.8 mL/kg BW was an effective dose in reducing urea and creatinine levels with an average decrease of 17.40 mg/dL and 0.36 mg/dL. The novelty in this study showed that the enzymatic VCO had an effect on reducing urea and creatinine levels in diabetic hypercholesterolemic male white rats
Inhibition breast carcinogenesis via PI3K/AKT pathway using bioactive compounds of Strychnine tree (Strychnos nux-vomica): in silico study
Get PDFBreast cancer poses a significant global health challenge, with a notable prevalence in Indonesia. Given the intricate nature of breast cancer progression and classification, precise treatment strategies are imperative, particularly targeting signaling pathways like PI3K/AKT, pivotal in cell growth, proliferation, survival, and apoptosis. Bioactive compounds from the Strychnine tree demonstrate potential in enhancing apoptotic effects and inhibiting breast carcinogenesis. This potential is explored through in silico studies. This research aims to analyze potential targets of Strychnine tree compounds, along with binding energy and stability between ligands and receptors. Employing bioinformatics target analysis, molecular docking, and molecular dynamics simulation, the study reveals AKT1 as a potential target of Strychnine tree compounds. These compounds inhibit AKT1 at both active and allosteric sites, displaying notably low binding energy scores. For example, brucine exhibits a binding energy of -10.83 kJ/mol at the active site, surpassing the standard capivasertib. However, lupeol, with a binding energy of -11.14 kJ/mol, falls short of the MK-2206 standard at the allosteric site. Molecular dynamics simulations expose fluctuations in parameters like RMSD, RMSF, and binding energy within the initial 5 ns. In conclusion, Strychnine tree compounds, such as brucine and lupeol, showcase potential AKT1 inhibition at both active and allosteric sites, enhancing apoptotic effects. However, the stability of these compounds in binding to their receptors within the first 5 ns of the simulation warrants further investigation for prolonged interactions.
The redox titration of Fe (II) ions with K2Cr2O7 using a potentiometry method the effect of EDTA and SCN- ligands
Get PDFComplexometric titration is often used for determining the metal content, either through direct titration or back titration. This study aimed to investigate redox titration between Mohr salt solutions and potassium dichromate in an acidic atmosphere in the pH range 2. The results showed that the reaction proceeded effectively at pH 2, with Mohr's salt solution acting as titrant. Furthermore, experiments were conducted to compare the effectiveness of EDTA ligands and SCN- ligands in improving the sharpness of the Fe2+/ Cr2O72- redox titration curve at pH 2. Results show that EDTA ligands are more effective than SCN- ligands in improving the sharpness of the titration curve. However, it should be noted that the addition of EDTA ligands can shift the equivalent point volume earlier, so adjustments need to be made in redox titration analysis. Research has also shown that adding excess moles of EDTA to total Fe (II) ions can decrease redox potential in Fe2+/ Cr2O72- systems. These results provide additional insight into the use of EDTA ligands in redox titration analysis and their relevance to redox potential changes in the systems studied
Optimization of self-nano emulsifying drug delivery system of rifampicin for nebulization using cinnamon oil as oil phase
Get PDFLung delivery can overcome the problems related to the effectiveness of tuberculosis treatment by increasing the drug concentration at the target site. Rifampicin as the first-line antibiotic for tuberculosis has low water solubility and is unstable in gastric which hinders its effectiveness. Self-nanoemulsifying drug delivery system (SNEDDS) is a strategy known to improve the solubility and stability of such drugs. This study aimed to obtain the optimum formula of rifampicin SNEDDS intended for lung nebulization using essential oil as an oil phase. Several essential oils are known to have effective antibacterial on Mycobacterium tuberculosis. However, a high capability to solubilize the drug is required for SNEDDS formulation. Cinnamon oil, tween 80, and transcutol P were chosen as SNEDDS components for optimization using a D-optimal mixture based on the physicochemical characteristics. The optimum formula comprised 12.65% cinnamon oil, 75.00% tween 80, and 12.35% transcutol P which dispersed easily to form a highly transparent emulsion in normal saline under 1 minute. Upon dilution with saline, the optimal SNEDDS can produce a homogenous nanometer droplet (169.2±19.771 nm, PDI of 0.258±0.070) with acceptable pH for lung administration. It also has a viscosity similar to water (0.94±0.01 cP) which allows it to be nebulized easily (aerosol output rate of 0.14±0.02 g/min). Although the diluted SNEDDS has a zeta potential of -2.533±0.268 mV, it was stable for up to 4 hours during the nebulization. These results indicate the potential of cinnamon oil-based rifampicin SNEDDS to be an alternative in the pulmonary delivery of rifampicin via nebulization
Phytochemical constituent, α-amylase and α-glucosidase inhibitory activities of Black Soybean (Glycine soja (L.) Merr.) ethanol extract
Get PDFDiabetes is characterized as a hyperglycemic condition impacted by β-cell dysfunction and insulin deficiency. Black soybean (Glycine soja (L.) Merr.) is widely known as an origin of nutritious food that has shown activities in preventing cardiovascular disease and reducing hyperglycemia. This research aimed to evaluate the potential of black soybeans ethanol extract (BSEE) as an α-amylase and α-glucosidase activity inhibitor. Black soybean seeds were extracted using the Soxhlet method with 50% ethanol as a solvent. The extract Soybean seeds were screened for the presence of phytochemicals. Inhibitory activity of α-amylase and α-glucosidase enzymes was tested in vitro with acarbose as a control. The absorbance measurement was conducted at 565 nm and 400 nm, respectively. BSEE contained alkaloids, flavonoids, polyphenols, saponins, quinones, tannins, and terpenoids. The results indicated that BSEE exhibited a weak inhibitory effect of α-amylase enzyme activity, with an IC50 value of 360.37 ± 20.80 µg/ml, in contrast to acarbose, which showed a significantly lower IC50 of 4.02 ± 0.56 µg/ml. Meanwhile, BSEE was classified as an active inhibitor of α-glucosidase enzyme activity, presenting 25.67 ± 0.27 µg/mL IC50 value, while acarbose demonstrated 10.85 ± 0.5 µg/mL IC50 value. In conclusion, BSEE inhibits α-amylase and α-glucosidase
The dynamics of risk and protective factors that shape resilience in low socioeconomic students
Get PDFBidikmisi students typically originate from low socioeconomic backgrounds and encounter a variety of risk factors that impede their ability to meet the requirements of the Bidikmisi scholarship, particularly in financial terms. This study utilized a qualitative collective case study methodology to investigate the pathways leading to educational resilience by examining the interplay of protective and risk factors, which are hypothesized to differ between “resilient (n=15) and “non-resilient” (n=10) students, as determined by their Grade Point Average (GPA) and engagement in non-academic activities. Purposive sampling was employed to select Bidikmisi students based on specific criteria. Data were gathered through in-depth interviews with third-year Bidikmisi students and were analyzed using the Social Ecological and Doughnut Resilience frameworks. The findings revealed that, in addition to financial constraints, students faced several other risk factors, including inadequate learning facilities, social barriers, social pressure, familial issues, motivation deficits, personal traits, learning difficulties, and physical and psychological health challenges. Conversely, protective factors were identified within parental support, skill development, family and identity, education, peer relationships, community engagement, and financial resources
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning
Get PDFIn the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness
Implementation of Fisherface Algorithm for Eye and Mouth Recognition in Face-Tracking Mobile Robot
Get PDFFacial recognition is an artificial intelligence algorithm that distinguishes one face from another by capturing facial patterns visually. This recognition specifically detects and identifies individuals based on facial features by scanning the entire face. Several methods are used for facial detection, including facial landmarks points, Local Binary Patterns Histograms (LBPH), and Fisherface. In the context of this research, Fisherface is used to reduce the dimensionality of facial space in order to obtain image features. The method is insensitive to changes in expression and lighting, leading to better pattern classification and making it suitable for implementation on mobile devices such as robot vision. Therefore, this research aimed to measure the response time speed and accuracy level of pattern recognition when implemented on mobile robot devices. The results obtained from the accuracy testing showed that the highest accuracy for face detection process was 90%, while the lowest was 78.3%. In addition, the average execution time (AET) for the fastest process was 1.63 seconds and the slowest was 1.72 seconds. For pattern recognition, the statistics showed 90% accuracy, 100% precision, 81.81% recall, and F-1 score of 89.5%. Meanwhile, the longest execution time was 0.084 seconds and the fastest was 0.064 seconds. In face tracking process, the mobile robot movement was based on real-time pixel sizes, determining x and y values to produce the center of face region
Innovative Multimodal Approaches in Image-Based Analysis of Adipose Tissue Cells
Get PDFThis study addresses the limitations of traditional single-modality imaging techniques, such as optical microscopy, in effectively analyzing adipose tissue cells. A novel multimodal approach is introduced to overcome these challenges, combining MRI, CT, and microscopy to provide a more comprehensive and precise dataset. The system automates image processing, utilizing advanced segmentation methods to detect adipose cells more accurately while calculating cell dimensions and total image area. The results indicate that the maximum observed cell diameter reaches 10,466.64 µm, with a minimum diameter of 0.40 µm and an average diameter of 2,398.31 µm across the sample images. All measurements achieved 0% mean square error (MSE), highlighting the precision of the method. Comparative analysis reveals significant improvements in accuracy for both cell detection and quantification, outperforming conventional methods. Graphical representations further validate the reliability of this multimodal approach, demonstrating its capacity to capture intricate details of cellular structures. This innovative method holds considerable promise for enhancing medical diagnostics, particularly in metabolic disorders like obesity and diabetes, where adipose tissue plays a pivotal role. Integrating multiple imaging modalities offers a powerful tool for more informed clinical decisions, potentially leading to improved patient outcomes
Using Artificial Intelligence Algorithms to Recognize Osteoporosis: A Review
Get PDFOsteoporosis is a silent disease that usually occurs due to bone mineral deficiency (BMD), which leads to increased bone porosity, thus weakening the bones and increasing their porosity, which increases the risk of fractures in those with this disease. Bone porosity is defined as an increase in internal spaces in the bone structure, which reduces its density and strength and makes it more susceptible to fractures. Many parts of the skeleton are exposed to osteoporosis, such as the hip, thigh, jaw, knee, forearm, spine, and others. The incidence of osteoporosis increases in the elderly, and women are more affected by it than men. There are also other factors such as genetic predisposition and lifestyle. The use of artificial intelligence-based technical programs has received wide attention in the medical field to diagnose and classify various medical images, such as images of cancerous tumors, arthritis, osteoporosis, and others, as artificial intelligence provides accurate and rapid tools for the early detection of osteoporosis through the analysis of medical images, outperforming traditional methods, which improves treatment opportunities and reduces diagnostic costs. However, these techniques face challenges such as algorithmic bias and the need for diverse databases to ensure a balanced assessment of different cases.In addition, despite the advances in computer technologies for the early detection of osteoporosis, the disease remains a challenge in healthcare due to the absence of clear symptoms until fractures occur, the difficulty of early detection, the variability in disease progression, and the need for personalized treatment plans, which leads to increased mortality. The paper presents a review of studies that have addressed osteoporosis in skeletal parts such as the knee, spine, hip, and teeth. It also reviews the techniques and methods used in diagnosis, with a focus on the role of artificial intelligence in improving accuracy and speed of detection. The review shows how deep learning algorithms, especially convolutional neural networks (CNNs),have been effectively used to classify osteoporosis through the results and achieve high accuracy rates in different studies