Dataset Information

Bioactive metabolites of Blumea lacera attenuate anxiety and depression in rodents and computer-aided model.

ABSTRACT: Blumea lacera is an edible plant with imperative medicinal values. However, the anxiolytic and antidepressant roles of B. lacera have not been well-explained. Therefore, the current study aims to explore the impending bioactive metabolites and roles of B. lacera methanol leaf extract (Me-BLL) in attenuating anxiety and depression through several experimental and computer-aided approaches. The chemical characterization of Me-BLL was performed through standard phytochemical and GC-MS analyses. To explore the neuropharmacological insights, Swiss albino mice were treated with Me-BLL at doses of 200-400 mg/kg, p.o. The anxiolytic effects were observed employing elevated plus maze (EPM), light-dark box (LDB), and hole-board (HBT) tests, while antidepressant effects were evaluated using forced swimming (FST) and tail suspension tests (TST). Diazepam (1 mg/kg, i.p.) and fluoxetine HCl (20 mg/kg, p.o.) were used as the reference standard. The phytochemical analyses revealed several bioactive metabolites, including higher contents of total phenolics and flavonoids. The EPM and LDB tests demonstrated an increased time spent in open arms and light box, and the HBT showed an increased number of head dipping, indicating the anxiolytic effects of Me-BLL. The TST and FST revealed a decrease in immobility time, meaning the persuasive antidepressant effects. The antioxidative effects of Me-BLL have also been observed prominently. Correspondingly, the computer-aided investigation confirmed several bioactive lead molecules. Specifically, thymol and cuminol revealed potential anxiolytic and antioxidant effects, while stigmast-5-en-3.beta.-ol and gamma-sitosterol possessed promising antidepressant effects. Taken these results as a base, the plant has imperative potentials in managing anxiety and depression-like disorders.

SUBMITTER: Hossen MA

PROVIDER: S-EPMC8269660 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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%.

Project description:Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others.We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package.The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Dataset Information

Bioactive metabolites of Blumea lacera attenuate anxiety and depression in rodents and computer-aided model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets