Dataset Information

Pharmacological and computer-aided studies provide new insights into Millettia peguensis Ali (Fabaceae)

ABSTRACT: Millettia peguensis, popular for its ethnopharmacological uses, was employed to evaluate its different pharmacological properties in this study. The analgesic studies of the plant have been performed by acetic acid-induced writhing and formalin-induced licking tests respectively, whereas the antidiarrheal experiment was done by castor oil-induced diarrheal test. Besides, antioxidant, cytotoxic, antimicrobial, thrombolytic evaluations were performed by DPPH scavenging with phenol content determination, brine shrimp lethality, disc diffusion and clot lysis methods respectively. Moreover, in silico study of the phytoconstituents was carried out by molecular docking and ADME/T analysis. The methanol extract of Millettia peguensis (MEMP) revealed significant biological activity in the analgesic and antidiarrheal test (p < 0.001) compared to the standards. Antioxidant assay displayed promising IC50 values (15.96 ?g/mL) with the total phenol content (65.27 ± 1.24 mg GAE/g). In the cytotoxicity study, the LC50 value was found to be 1.094 ?g/mL. Besides, MEMP was highly sensitive to the bacteria but less liable to clot lysis. Furthermore, phytoconstituents exposed potential binding affinity towards the selected receptors, whereas the ADME/T properties indicated the drug likeliness of the plant. The outcomes of these findings suggest the therapeutic potential of this plant against pain, diarrhea, inflammation, and tissue toxicity.

SUBMITTER: Alam S

PROVIDER: S-EPMC7783235 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The current study aimed to qualitatively and quantitatively determine the phytochemical components of Cycas pectinata methanol extract (MECP), along with its antioxidant, anti-inflammatory, thrombolytic, locomotor, anxiolytic, analgesic, and antidiarrheal activities. The in vitro antioxidant activity was evaluated by DPPH scavenging assay and the total phenol and total flavonoid contents, while the anti-inflammatory activity was evaluated by a protein denaturation assay. The in vivo locomotor effects were examined using the open field test and hole-cross test. The anxiolytic effect was examined using the elevated plus maze (EPM) test, hole-board test (HBT), and light-dark test (LDT), while the analgesic activity was investigated using the acetic acid-induced writhing test. The antidiarrheal effect was evaluated by castor oil-induced diarrhea and gastrointestinal motility. Ten bioactive compounds were selected on the basis of their biological activities and further investigated using in silico molecular docking simulation to correlate with the identified pharmacological properties. Additionally, the ADME properties of the compounds were evaluated according to their drug-likeness profile. MECP had a maximum total phenol content of 209.85 ± 3.40 gallic acid equivalents/g extract and a total flavonoid content of 105.17 ± 3.45 quercetin equivalents/g extract, with an IC50 value of 631.44 μg/mL. MECP (62.5-500 μg/mL) elicited 20.96-38.12% decreased protein denaturation compared to diclofenac sodium (65.40-83.50%), while a 35.72% (P < 0.001) clot lysis activity was observed for the 10 mg/mL concentration. MECP induced a dose-dependent reduction in locomotor activity, with a significant anxiolytic effect. In the analgesic test, MECP (200, 400 mg/kg) showed a 45.12% and 58.82% inhibition in analgesia, and the 400 mg/kg dose elicited a 27.5% inhibition in intestinal motility. These findings suggest that MECP might be effective in treating antioxidant, anti-inflammatory, and neuropharmacological defects, but this requires further study.

Project description:Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.

Dataset Information

Pharmacological and computer-aided studies provide new insights into Millettia peguensis Ali (Fabaceae)

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets