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This study aims to analyze the differences in various extraction techniques and their relationship to the yield and biological activity of phytochemical compounds from natural materials. The research was conducted through a systematic literature review from various reliable scientific sources over the last decade. The results indicate that extraction methods such as maceration, soxhlet extraction, microwave-assisted extraction (MAE), and ultrasound-assisted extraction (UAE) have varying effectiveness. The effectiveness of these methods is highly dependent on the type of material, solvent used, and process parameters such as temperature, time, and solvent-to-material ratio. Modern extraction methods such as sonication, MAE, and UAE tend to produce higher yields and better biological activity. These modern methods have the advantage of being more time-efficient and requiring fewer solvents, thus allowing the extraction of active compounds more optimally. Additionally, technologies like microwave and ultrasonic waves help break down the cell walls of natural materials, enhancing the release of phytochemical compounds more effectively and rapidly. However, conventional methods such as maceration and soxhlet extraction remain relevant, especially for materials containing thermolabile compounds that may degrade at high temperatures. These methods are still preferred to maintain the stability of active compounds sensitive to thermal degradation. Choosing the appropriate extraction method is crucial in producing high-quality extracts that can potentially be used as raw materials for phytopharmaceuticals, supplements, or other natural products. This study provides a strong theoretical foundation for further experimental research and guidance in selecting extraction methods based on the required efficiency and effectiveness for industrial applications. Thus, this study contributes to the development of more efficient and high-quality natural products.

Pain is an unpleasant sensation that can interfere with an individual's quality of life and requires effective management. Synthetic analgesics such as ketorolac, codeine, etoricoxib, and celecoxib are commonly used to relieve pain, but long-term use can cause adverse side effects, including gastrointestinal disturbances, dependence, and cardiovascular risks. Therefore, it is important to find safer, natural alternatives. One promising alternative is the extract of the dragon’s tail leaf (Rhaphidophora pinnata), a plant traditionally used in Indonesian medicine as a pain reliever. This study aims to evaluate and compare the analgesic effects of Rhaphidophora pinnata leaf extract with several synthetic drugs using the writhing test on male mice (Mus musculus). The mice were induced with pain by injecting a 0.5% acetic acid solution. The treatments included ketorolac, codeine, etoricoxib, celecoxib, and Rhaphidophora pinnata leaf extract. The results showed that all treatments, including both synthetic drugs and Rhaphidophora pinnata leaf extract, produced significant analgesic effects compared to the negative control (Na CMC 0.5%). Ketorolac showed the highest effectiveness, followed by codeine, etoricoxib, and celecoxib. Although the Rhaphidophora pinnata leaf extract showed potential as a natural analgesic, its effectiveness varied among individual test animals. Some mice showed a better response to the leaf extract, but overall, the analgesic effect was still lower than that of synthetic drugs. These findings support the use of medicinal plants as a safer alternative to synthetic analgesic drugs. Moreover, this study provides a foundation for further research aimed at isolating active compounds from Rhaphidophora pinnata leaf extract to develop more effective and safer pain-relieving medications.

Arginine deiminase (ADI) is a promising enzyme with significant therapeutic potential, particularly for its anticancer effects through the depletion of arginine in cancer cells that are auxotrophs. In this study, we aimed to optimize the production of ADI using clinical Escherichia coli isolates and to evaluate its antioxidant activity. A total of 25 E. coli isolates were obtained from 45 hospital samples collected in Wasit Province, Iraq. Optimization of ADI production was performed by systematically testing various factors including culture media, pH, carbon and nitrogen sources, incubation temperature, and time. The antioxidant activity was assessed using the DPPH radical scavenging assay. The highest ADI production was achieved using a modified M9 medium supplemented with 1% w/v sucrose as the carbon source and 5% w/v yeast extract as the nitrogen source. The optimal enzyme activity of 1.6 U/mg protein was observed at pH 7.0, 37°C, and after 24 hours of incubation. The crude ADI extract exhibited high antioxidant activity, with 79.28 ± 1.06% DPPH scavenging at 200 µg/mL, comparable to ascorbic acid, which showed 86.11 ± 1.45% DPPH scavenging. The study successfully optimized the conditions for enhanced ADI production based on clinical E. coli isolates, demonstrating its potential as both an anticancer enzyme and an antioxidant. The dual therapeutic potential of ADI warrants further research, including clinical trials, to explore its application in cancer therapy and as an antioxidant in medical treatments, offering promising avenues for future drug development and improved therapeutic strategies, particularly for targeting cancer and oxidative stress-related diseases.

Improvement amount Distributed Denial of Service (DDoS) attacks in cloud infrastructure and edge computing demands solution adaptive, distributed, and efficient detection in a way computing. Research This propose an optimized Federated Learning (FL) based DDoS detection model using Centroid Opposition-Based Bacterial Colony Optimization (COBCO) to training the Elman Neural Network (ENN). The proposed architecture consists of of two components Main: on the edge node side, a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) model is used to extraction feature local from traffic data network, while on the server side, model parameters from each node are collected and used for training an optimized ENN with COBCO. Approach This aim increase accuracy detection at a time maintain efficiency local data communication and privacy. In progress experimental, model tested use three benchmark datasets: NSL-KDD, CICIDS2017, and CICDDoS2019. The preprocessing process includes feature encoding categorical, normalization numeric, class balancing using SMOTE, as well as validation cross (k-fold). Initial results show that combination of FL, CNN–GRU, and COBCO–ENN produces improvement significant in accuracy and time convergence compared to approach conventional such as PSO, GA, and non- federative models. In addition, the proposed model capable maintain performance detection tall although executed in edge environment with limitations source Power.  Study This give contribution important in development system scalable, privacy-preserving, and adaptive intelligent DDoS detection to dynamics Then cross modern network. Integration of FL and COBCO in ENN training shows potential big for used in implementation real in cloud-edge infrastructure. In addition, the proposed model demonstrates strong scalability and adaptability, making it highly suitable for dynamic and evolving network environments.

This study compares the performance of two deep learning models, namely Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network (LRCN), in the task of recognizing human activity from videos. Human activity recognition is an important field in computer vision with many applications, such as security monitoring, human-computer interaction, and social media-based video analysis. ConvLSTM is a model that combines convolution operations with long-term memory LSTM, thus capable of capturing spatial and temporal information simultaneously. This approach is ideal for processing video data sequences that have spatial and temporal dimensions. On the other hand, LRCN combines the power of spatial feature extraction from Convolutional Neural Network (CNN) and temporal sequence modeling through Recurrent Neural Network (RNN), specifically LSTM, to understand movement patterns in videos. The study used the UCF50 dataset consisting of 50 activity classes, but was limited to five classes for the focus of the experiment. The dataset was divided into 80% for training and 20% for testing, and the model was drilled for 50 epochs using early stopping to prevent overfitting. The results show that both models have high training performance. ConvLSTM achieved a training accuracy of around 98% and a validation accuracy of 90%, while LRCN achieved a training accuracy of 99.5% and a validation accuracy of 88%. Although ConvLSTM demonstrated good stability on the validation data, further testing using TikTok videos as real-world data showed that LRCN had a higher confidence level in recognizing activities, with most predictions achieving confidence scores above 80%. This difference in performance indicates that while ConvLSTM excels in generalizing on training data, LRCN is more robust to real-world data variations.

Ultraviolet (UV) radiation has been known to cause various adverse effects on the skin, including erythema (commonly referred to as sunburn), premature aging, and, with prolonged exposure, the risk of developing skin cancer. To counteract these harmful effects, sunscreen products are commonly used to protect the skin from UV radiation. The development of natural-based sunscreens is gaining attention due to their potential safety and antioxidant benefits. One promising natural ingredient is thyme (Thymus vulgaris L.), a medicinal herb that contains several secondary metabolites such as flavonoids, alkaloids, and tannins. These compounds possess antioxidant and free radical scavenging properties, making them suitable candidates as active ingredients in sunscreen formulations. This study aims to determine the Sun Protection Factor (SPF) value of thyme herb extract using the UV-Vis spectrophotometric method. A qualitative approach was used to assess the photoprotective potential of the extract. The SPF values were measured at three different concentrations of the extract: 0.5%, 0.75%, and 1.0%. The absorbance of each concentration was recorded at wavelengths ranging from 290 nm to 400 nm using a UV-Vis spectrophotometer. The SPF values were then calculated based on the absorbance data processed using Microsoft Excel. The results revealed that the thyme herb extract exhibited high SPF values at all tested concentrations. Specifically, the SPF values obtained were 36.027 for 0.5%, 36.244 for 0.75%, and 36.516 for 1.0% concentration. All three concentrations fall under the "ultra protection" category, indicating strong UV protective potential. These findings suggest that thyme herb extract can be effectively utilized as a natural active ingredient in the formulation of sunscreen products. Further studies are recommended to explore its stability, safety, and efficacy in topical applications.

Virgin Coconut Oil (VCO) is a high-quality coconut oil that is extracted from fresh coconut meat without the use of high heat or chemicals, preserving its natural nutrients and beneficial properties. The demand for VCO has increased in recent years due to its various health benefits, such as boosting the immune system, improving digestion, and providing antimicrobial effects. The oil is rich in medium-chain fatty acids, particularly lauric acid, which is known for its positive effects on cholesterol levels and cardiovascular health. This study focuses on the production of VCO using a natural fermentation method, a traditional technique that is gaining attention for its simplicity and environmental friendliness. In this method, mature coconut meat is grated and squeezed to extract the coconut milk, which is then fermented at room temperature for 24–48 hours. The fermentation process allows the separation of the oil from the milk, forming a clear oil layer on top, which retains the characteristic aroma of coconut. The quality of the produced VCO was analyzed by determining its acid value and saponification number. The saponification value, which indicates the amount of alkali required to saponify the fat, was found to be 50.49. The acid value, which reflects the free fatty acid content, was measured at 5.61. These results suggest that the VCO produced through natural fermentation is of relatively good quality, with a favorable acid value indicating lower rancidity. Overall, the study demonstrates that natural fermentation is an efficient, eco-friendly, and cost-effective alternative for VCO production. This method not only preserves the natural properties of the coconut oil but also offers a sustainable approach to producing high-quality oil for various health and cosmetic applications.

This research aims to develop a recommendation system that can help retail business owners design more effective, data-driven promotional strategies. This system utilizes data mining techniques and the Apriori algorithm to extract association rules from consumer transaction data, thereby identifying more specific and accurate consumer purchasing patterns. Based on these patterns, the system can provide relevant promotional recommendations, such as product bundling, buy-one-get-one offers, or special discounts, which can attract consumer interest and increase sales. The system's implementation process is presented in the form of an interactive dashboard, which allows business owners to upload their transaction data, adjust analysis parameters, and visualize the promotional recommendation results in a way that is easier to understand and can be directly applied to their marketing strategies. This system not only provides well-structured promotional recommendations but also enables retail business owners to make more informed and efficient decisions in determining the type of promotion to implement, based on insights gained from analyzing their own transaction data. By utilizing this system, business owners can optimize their promotional strategies more efficiently and effectively, because they can quickly identify promotions that best suit consumer purchasing patterns. This can increase impulse sales, as relevant promotions will encourage consumers to purchase more products. Furthermore, this system shows great potential in increasing consumer engagement, as the promotions provided are more personalized and tailored to each consumer's preferences. Therefore, the implementation of this recommendation system has the potential to drive significant sales growth and help retail business owners achieve greater profits, as well as accelerate their business decision-making process. This system, ultimately, not only benefits business owners but also enhances the consumer shopping experience with promotions that are more tailored to their needs and preferences.

This study aims to extract and purify secondary metabolite compounds from Coffea arabica leaves using various separation techniques, such as maceration, column chromatography, thin layer chromatography (KLT), and preparatory KLT, and followed by flavonoid content analysis using UV-Vis spectrophotometry. The process begins with drying the arabica coffee leaves, then extraction is carried out using 96% ethanol for three days. After that, the solvent is evaporated with a rotary evaporator at 60°C to obtain a viscous extract. The extracts were further separated using column chromatography with a mixed solvent of n-hexane: ethyl acetate (3:1), resulting in 12 fractions that showed color differences, which was an indication of the diversity of the compounds in the extract. Advanced identification was carried out with KLT using methanol solvents: chloroform (9:1) at UV wavelengths of 254 nm and 366 nm, indicating the presence of compounds with medium to high polarity. Furthermore, the isolated compounds were further separated using the preparative KLT (KLTP) method. To determine the flavonoid content, UV-Vis spectrophotometry was performed at a wavelength of 431 nm, using quercetin as standard. The results of the analysis showed that Arabica coffee leaf extract had a yield of 7.35% and a total flavonoid content of 19.0605 mgQE/g. These findings show that arabica coffee leaves are a source of flavonoid compounds that have the potential for the development of herbal or phytopharmaceutical products. The flavonoid content found may provide health benefits, particularly in increasing antioxidant activity, which can be used in the treatment of oxidative stress-related diseases. The results of this study also open up opportunities for the development of plant-based products, especially Arabica coffee, in the pharmaceutical and cosmetic industries as an efficacious natural active ingredient.

This study investigated the phytochemical screening and antibacterial activity against various microorganisms including E. coli, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, and Candida albicans. Active compounds were isolated and identified from Aleppo Oak Gallnut, obtained from the Iraqi plant Quercus Infectoria L. Plant extracts were prepared using a continuous extraction apparatus, Soxhlet, with a successive solvent system based on polarity differences, including petroleum ether (60-80°C), ethanol (78°C), and hot aqueous extracts. Acid hydrolysis was performed on the raw ethanol and hot water extracts to obtain free phenolic compounds, including Gallic acid, Apigenin, Rutin, Kaempferol, Chlorogenic acid, and Caffeic acid, using high-performance liquid chromatographic (HPLC) techniques. The inhibitory activity of Aleppo Oak Gallnut extracts (Ethanol and Hot Aqueous) after acid hydrolysis was tested using four concentrations (25%, 50%, 75%, 100%) against various microorganisms. The Ethanol extract exhibited a significant inhibitory effect on Enterococcus faecalis. The Hot Aqueous extract showed a modest inhibitory effect on Enterococcus faecalis. The Ethanol extract demonstrated strong inhibition against Klebsiella pneumoniae. The Hot Aqueous extract at 100% and 75% showed high inhibition. The Ethanol extract exhibited significant inhibitory effects against Staphylococcus aureus at 100%. The Hot Aqueous extract at 75% and 100%  demonstrated weaker inhibition against Staphylococcus aureus. The Ethanol extract demonstrated weaker inhibition against E. coli, and the Hot Aqueous extract showed no effect at 50%  and 25%, and slight inhibition at 100%  and 75%. For Candida albicans, the Ethanol extract showed minimal inhibition at all concentrations and no effect at 25%. The Aqueous extract had a slight effect at 100%  and no inhibition at 75%, 50%, and 25%.

The betel nut (Areca catechu) is a tropical plant that thrives in Southeast Asia, including Indonesia. This plant has long been known in traditional medicine and local culture for its content of various beneficial active compounds. One form of its utilization that has rarely been scientifically studied is fresh betel nut juice. Therefore, this study aims to conduct a preliminary assessment of the characteristics of fresh betel nut juice, particularly from a physical, chemical, and microbiological perspective. The juice production process is carried out simply by squeezing fresh betel nuts without the addition of chemicals or heating. Based on initial observations, the betel nut juice exhibits a distinctive reddish-brown color and a sharp, pungent aroma. This color and aroma likely originate from the phenolic and alkaloid compounds naturally present in the betel nut. pH measurements indicate that the juice has a fairly high acidity level, which can affect the stability of microorganisms within it. Qualitative tests of the chemical composition of the betel nut juice indicate the presence of bioactive compounds, particularly tannins and alkaloids. Tannins are known to have antimicrobial activity, while alkaloids act as physiologically active compounds that can affect the nervous system. However, the presence of these compounds does not completely inhibit the growth of microorganisms. Through simple isolation of microorganisms from the fresh juice, bacteria from the genus Lactobacillus spp., which are typically associated with natural fermentation processes, were found. The presence of these bacteria indicates that fresh areca nut juice can be a potential growth medium for certain microorganisms, particularly lactic acid bacteria. This finding opens up opportunities for further research into the use of areca nut juice as a natural fermentation agent that may have functional and probiotic value.

Pedada leaves (Sonneratia caseolaris), a type of mangrove plant that grows in tropical and subtropical coastal areas, have high ecological and pharmacological value. In addition to functioning as coastal protection from abrasion and seawater intrusion, this plant is also known to contain various secondary metabolite compounds such as flavonoids, tannins, saponins, and alkaloids. These compounds have been widely reported to have important biological activities, one of which is as an antimicrobial agent. This study aims to identify the effectiveness of pedada leaf ethanol extract in inhibiting the growth of pathogenic microorganisms, especially bacteria that cause infections. The extraction process was carried out using 96% ethanol solvent through the maceration method, namely soaking the material in the solvent for a certain time to optimally obtain active compounds. The obtained extract was then tested for its antimicrobial activity using the disc diffusion method against test bacteria, both Gram-positive and Gram-negative. The inhibition zone formed around the disc was measured as an indicator of the antibacterial effectiveness of the extract. The results showed that pedada leaf extract was able to produce inhibition zones with varying diameters depending on the concentration used. The higher the extract concentration, the larger the diameter of the inhibition zone formed. This indicates a positive relationship between extract concentration and its antibacterial strength. This activity is believed to originate from the ability of compounds such as flavonoids and tannins to damage bacterial cell walls or disrupt microbial metabolic processes. This study provides initial evidence that pedada leaves have potential as an environmentally friendly and sustainable source of natural antibacterials. This potential is highly relevant in efforts to develop alternative antibacterial materials based on local plants, especially amidst increasing bacterial resistance to synthetic antibiotics.

Beluntas (Pluchea indica L.) contains of flavonoids, which are beneficial for treating acne. Previous research has been conducted on peel-off gel masks made from ethanol extract of beluntas leaves. The aim of this study is to determine the variation in carbopol concentrations in the formulation of gel masks containing beluntas leaf extract, as well as to evaluate their physical stability. This research using three formulations with different carbopol concentrations: Formula I with 1% carbopol, Formula II with 1.5% carbopol, and Formula III with 2% carbopol. The physical evaluation of the beluntas leaf extract gel mask preparations included organoleptic, homogeneity, viscosity, pH, spreadability, and adhesiveness testing. All formulations underwent a stability test known as the cycling test (cycles 1 to 6). The results of the stability tests showed that Formulas II and III met the requirements for physical quality, whereas Formula I did not meet the viscosity and spreadability value ranges. The gel mask with the best results was Formula II with 1.5% carbopol concentration. However, based on the cycling stability test, there was an increase in viscosity and adhesiveness. Based on the cycling test results for organoleptic properties, homogeneity, and spreadability, Formula II did not show significant changes.

Silica gel is one of the most commonly used adsorbents in the fractionation of bioactive compounds from medicinal plants. This is due to its ability to separate compounds based on differences in polarity, thus simplifying the purification process of active components. Fractionation itself is a crucial step in phytochemical research, as it aims to obtain active compounds in a purer, standardized form, and ready for further testing, both biologically and pharmacologically. This study aims to review the effectiveness of silica gel in the fractionation of active compounds from various types of medicinal plants. The study was conducted through a systematic literature review method with a qualitative-descriptive approach to 35 scientific articles published in the last five years. The articles analyzed were those that presented primary data on the use of silica gel in the fractionation process of medicinal plants. The results of the study showed that silica gel was proven effective in separating various polar compounds such as flavonoids, alkaloids, and phenolic compounds from crude extracts of medicinal plants. Some studies have even succeeded in isolating pure compounds that have significant biological activities, such as antioxidants, antimicrobials, and anti-inflammatory. The effectiveness of fractionation with silica gel is influenced by several important factors, such as the type and composition of the solvent, the elution technique (gradient or isocratic), and the support of advanced analytical methods such as thin-layer chromatography (TLC) and spectroscopy (UV-Vis, FTIR, and NMR). However, the use of silica gel still has several limitations, including the relatively high solvent requirements and long elution times. From the results of this study, it can be concluded that silica gel has great potential in supporting the fractionation process and isolation of bioactive compounds from medicinal plants. Therefore, optimization of silica gel-based fractionation techniques is important to support research and development of more efficient, standardized, and sustainable phytopharmaceutical products.

Number of vegetarians in Indonesia is rising. This increase may be attributed to improvement of people knowledge regarding health benefits of high fiber plant-based diet especially to reducing the risks of degenerative diseases. However, high fiber plant-based food especially mushroom-based food that being accepted by majority of panelists are limited to oyster and straw mushroom. Hence, the goal of this research is to get the best formula based on organoleptic and crude fiber content from Auricularia polytricha and red dragon fruit peel extract as a food for vegetarian diet. This study used experimental study with Randomized Block Design (RBC) method with two experimental factors: the concentration and volume of red dragon fruit peel extract (25 ml and 50 ml) and the proportions of wheat flour and Auricularia polytricha (30:70, 50:50, 70:30). Organoleptic test results data were analyzed using the Krusskal Wallis test and the Man-Whitney U test to test it further. The results showed that there was a significant effect of the nugget ingredient formulation on color, taste, and texture of the nuggets (p<0,05), and there was no significant effect on the aroma of the nuggets (p>0,05). The highest crude fiber content was found in nuggets with a composition of 70% Auricularia polytricha and 30% wheat flour with the addition of 50 ml red dragon fruit peel extract, which was 36.88%, and anthocyanin content of 0,99 mg/100 g. The best formula for Auricularia polytricha nuggets with the addition of red dragon fruit peel extract is J3E2 with a composition of 70% Auricularia polytricha and 30% wheat flour and the addition of 50 ml of red dragon fruit skin extract.

Pharmacology is the study of drug interactions with living organisms. This study aims to evaluate the anti-inflammatory effects of several drug preparations, including Hemilon (methylprednisolone), Lupred (prednisolone), Kamathasone (dexamethasone), neem leaf infusion, and snakehead fish extract, on mice (Mus musculus) induced with inflammation using carrageenan solution via intraplantar injection. Edema volume was observed using a plethysmometer at 15, 30, 45, and 60 minutes post-induction. Data were analyzed using normality, homogeneity, ANOVA, and Tukey tests via SPSS. Results showed that Hemilon had the highest anti-inflammatory effect, followed by Kamathasone and Lupred, while neem leaf and fish extract showed weaker activity. Statistical tests revealed normal distribution (sig. 0.197 > 0.05), non-homogeneous data (sig. 0.022 < 0.05), and no significant difference between groups (ANOVA sig. 0.366 > 0.05). Despite this, descriptive results confirmed strong anti-inflammatory potential of synthetic drugs. This practicum provides valuable insights into drug mechanisms and emphasizes methodological accuracy in pharmacological testing.

The Indonesian manufacturing industry is currently facing intense pressure due to global economic fluctuations and domestic volatility, prompting a strategic reassessment of sustainability practices to maintain competitiveness. While firm value reflects investor confidence, discrepancies remain between operational performance and market valuation, particularly in highly profitable firms. This study aims to systematically investigate how internal corporate factors namely dividend policy, firm size, and green accounting influence firm value. Using a Systematic Literature Review (SLR) method, ten journal articles published between 2023 and 2025 were selected based on indexation (SINTA, Scopus, Copernicus), methodological clarity, and variable alignment. The articles were screened and analyzed using content analysis techniques, supported by Microsoft Excel and Mendeley for structured data extraction. The findings reveal that a stable dividend policy serves as a strong signal of financial stability, firm size reinforces strategic positioning and resource capacity, and green accounting strengthens legitimacy through sustainability disclosure. These factors jointly shape market perceptions and ultimately influence firm valuation. The synthesis supports both signal theory and legitimacy theory in explaining the transmission of value through internal policies. This study contributes theoretically by integrating financial and sustainability variables into a unified value framework and offers practical insights for corporate decision-makers seeking to align internal strategies with investor expectations. Limitations include reliance on secondary data and scope restricted to the manufacturing sector. Future studies should explore empirical validation through cross-sectoral analysis and primary data to enrich the findings.

This study explores the use of psychotropic substances, which affect the central nervous system and can alter mental functions, behavior, and individual perception. In the medical context, psychotropics have been widely utilized for treating mental disorders such as depression, anxiety, and schizophrenia. The research emphasizes the importance of understanding the mechanisms of psychotropics to optimize their therapeutic benefits while minimizing the risk of adverse effects. The study employed animal models, specifically rats, using the forced swimming test (FST) to assess the antidepressant and sedative effects of various substances, including Esilgan, Xanax, Zolmia, Syzygium malaccense leaf extract, and lelap (herbal), with NaCMC as a negative control. The results demonstrated varying sedative and antidepressant effects among the substances, with Xanax exhibiting the most significant antidepressant effect, while Syzygium malaccense leaf extract showed potential as a natural sedative agent. The study also underscores the importance of applying the 3R ethical principles (Replacement, Reduction, Refinement) in animal research and highlights the need for alternative methods to reduce reliance on animal testing. These findings are expected to serve as a foundation for developing safer, more effective, and ethical psychotropic therapies for mental disorders in the future.

This study analyzes Gopay user satisfaction in DKI Jakarta area. Amidst Indonesia's booming digital economy and the shift to cashless transactions, Gopay's role as a leading e-wallet is crucial, making user satisfaction vital for business sustainability. Employing a quantitative descriptive approach, data was collected via surveys from 170 Gopay users in DKI Jakarta, primarily young adults (18-29) and students. Data were analyzed using descriptive and exploratory factor analysis with SPSS version 25. Descriptive analysis showed respondents hold a highly positive perception across all five satisfaction dimensions: Content, Accuracy, Format, Ease of Use, and Timeliness. Factor analysis extracted five key factors—Completeness, Accurate, Appearance, Easy to Understand, and Information Availability—collectively explaining 68.601% of the total variance. These factors significantly contribute to overall user satisfaction. The findings offer valuable insights for Gopay to enhance services and foster user loyalty by focusing on these critical aspects of application quality.

Abstract. Background: Slimming drugs, such as phentermine, are widely used for weight reduction but are known to induce oxidative stress, particularly affecting hepatic and renal tissues. Selenium nanoparticles (SeNPs), especially those biosynthesized using natural sources like spirulina platensis, have demonstrated promising antioxidant and protective activities in biological systems. Objective: This study aimed to assess the protective effects of selenium nanoparticles synthesized from spirulina platensis extract on renal and hepatic functions in male rats exposed to phentermine induced oxidative stress. Methods: Twenty-six adult male wistar rats were divided into five experimental groups: control group (dosed with water n=5), SeNPs group (dosed with SeNPs n=5), phentermine group (dosed with phentermine n=5), SeNPs then phentermine group (dosed with SeNPs then phentermine n=5), phentermine then SeNPs group (dosed with phentermine then SeNPs n=5). The experiment lasted for 45 days, the measurement of cytokines IL-6, IL-12, TOS and TO-AC. Results: Phentermine administration significantly increased cytokines IL-6, IL-12 indicating marked oxidative stress and immune activation. Treatment with SeNPs alone or in combination with phentermine notably improved these parameters. The group dosed with SeNPs prior to phentermine (SeNPs then phentermine) showed the most prominent protective effects with values approaching those of the control. Conclusions: Selenium nanoparticles from spirulina platensis demonstrated significant protective, antioxidant, and immunomodulatory effects against phentermine induced immune disturbances. These findings support their potential