Search

Objective: The purpose of this research was to evaluate and select the best architecture among native convolutional neural network (CNN), MobileNetV2, ResNet50V2, and EfficientNetB0 for predicting the endpoint of the high shear wet granulation process, with accuracy as the main evaluation metric. Methods: The dataset was captured from an industrial camera using static image analysis and was manually labeled as “NOT READY” and “READY” according to the traditional endpoint method based on the mixer’s ampere point in the granulator. The dataset contained a total of 180 images, which were split between training and validation sets. Native CNN and TensorFlow Keras application programming interface (API) were utilized with MobileNetV2, EfficientNetB0, and ResNet50V2 as base feature encoders. Hyperparameters, such as final Fully Connected (FC) layer width, dropout rate, and learning rate, were optimized for binary classification using Keras hyper tuning. Results: The best was the native CNN, it was also the fastest among the three other models, taking only 20-30 ms per step for inference during runtime, though it requires 9000 ms time for training, the longest time among the models. It achieved an accuracy of 98%, and a validation accuracy of 97%. Conclusion: The system was able to determine when a wet granulation process has reached its endpoint based on live images from a camera after being trained on previously labeled data. The native CNN was the best model, offering the fastest runtime performance and the highest accuracy. [ABSTRACT FROM AUTHOR]

Pluchea indica (L.) Less is traditionally utilized to treat postpartum women in Indonesia. The plant has many pharmacological properties, so that it can be further developed as herbal medicine. In that development process, plant authentication is needed to ensure the quality of raw materials. A simple microscopical and thin-layer chromatography (TLC) analysis might be a way to authenticate the plant, but it has never been reported. So, this study evaluates the microscopical and TLC analysis for P. indica authentication in standardized herbal medicines production. Plant microscopic observation, fluorescence analysis, and polyphenol screening were conducted. n-Hexane, ethyl acetate, and methanol extracts of plant organs were then analyzed by TLC. Here, we reported that in microscopical analysis the simplicia of P. indica contains trichomes and tannin-containing cells. In addition, chlorogenic acid as a marker was present in TLC analysis by ethyl acetate-water-formic acid-acetic acid (8.5:1.5:1:1, v/v). The results of this evaluation might provide additional information in the identification, authentication, and quality control of P. indica as a raw material for herbal medicine. [ABSTRACT FROM AUTHOR]

The SIRT1 is overexpressed in a number of cancers. As a result, inhibiting SIRT1 may be used as a cancer treatment technique. Modification of 1-benzoyl-3-methylthiourea derivatives was carried out in this study by changing the aromatic side. The designed compounds (94) were subjected to in silico docking, pharmacokinetics, and preclinical testing. In the docking sample, the (4-decyl-N-(methylcarbamothioyl)benzamide (91), 2-(benzyloxy)-N-(methylcarbamo-thioyl)benzamide (93) and N-(methylcarbamothioyl)-2-naphthamide (94) were predicted to display better inhibition of SIRT1, so they were chosen for subsequent molecular dynamic studies. The compound 93 is proposed as a possible anticancer candidate that inhibits SIRT1 based on the screening results from molecular docking, pharmacokinetic predictions, and molecular dynamics. [ABSTRACT FROM AUTHOR]

In Indonesia, the number of cases infected with Covid-19 peaked between mid-June and August 2021, resulting in the denial of hospital treatment to some people due to insufficient capacity. Therefore, people treat themselves by using natural ingredients. This study aims to identify plants that can treat Covid-19 with self-medication. This study took a description approach to investigate people using traditional herbs in self-medication for COVID-19 recovery. Snowball sampling is the approach used. Interviews and an online system (gform) were used to collect data. The number of samples collected allows for a more thorough analysis of the 104 respondents. The results showed that women (51.0%), people aged between 56 and 65 (52.9%), Banten residents (32.7%), secondary education (57. 7%), and people with comorbidities (86.5). %) were all interested in using traditional herbal medicine. Reduction of symptoms and results of laboratory tests were used to measure recovery. The most common comorbidities were hypertension (7-14 days to recovery), diabetes mellitus (15-21 days to recovery), and cardiovascular disease (7-14 days and 15-21 days to recovery). The Zingiberaceae family dominates traditional herbs. The most widely used traditional ingredients are Empon-Empon, which consists of red ginger, turmeric, nutmeg, aromatic ginger, bay leaves, lemongrass, galangal, cinnamon, and cloves. The average healing time is 7-14 days (32.7%). In conclusion people who utilized herbal medicines reported a speedy recovery and reduction in the severity of their symptoms. [ABSTRACT FROM AUTHOR]