Eurycoma (E.) longifolia Jack (Tongkat Ali) is a widely applied medicine that has been reported to boost serum testosterone and increase muscle mass. However, its actual biological targets and effects on an in vitro level remain poorly understood. Therefore, the present study aimed to investigate the effects of a standardised E. longifolia extract (F2) on the growth and its associated gene expression profile in mouse Leydig cells. F2, even at lower doses, was found to induce a high level of testosterone by ELISA. The level was as high as the levels induced by eurycomanone and formestane in Leydig cells. However, Leydig cells treated with F2 demonstrated reduced viability, which was likely due to the diminished cell population at the G0/G1 phase and increased cell population arrested at the S phase in the cell cycle, as assessed by MTT assay and flow cytometry, respectively. Cell viability was revived when the treatment time‑point was prolonged to 96 h. Genome‑wide gene analysis by reverse transcription‑quantitative PCR of F2‑treated Leydig cells at 72 h, when the cell growth was not revived, and 96 h, when the cell growth had started to revive, revealed cyclin‑dependent kinase‑like 2 (CDKL2) to be a potential target in regulating the viability of F2‑treated Leydig cells. Functional analysis, as analysed using GeneMANIA Cytoscape program v.3.6.0 (https://genemania.org/), further suggested that CDKL2 could act in concert with Casitas B‑lineage lymphoma and sphingosine kinase 1 interactor‑A‑kinase anchoring protein domain‑containing genes to regulate the viability of F2‑treated Leydig cells. The findings of the present study provide new insights regarding the potential molecular targets associated with the biological effect of E. longifolia extract on cell growth, particularly on the cell cycle, which could aid in enhancing the bioefficacy and reducing the toxicity of this natural product in the future.
According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.
Chalcone derivatives are an extremely valuable class of compounds, primarily due to the keto-ethylenic group, CO-CH[double bond, length as m-dash]CH-, they contain. Moreover, the presence of a reactive α,β-unsaturated carbonyl group confers upon them a broad range of pharmacological properties. Recent developments in heterocyclic chemistry have led to the synthesis of chalcone derivatives, which have been biologically investigated for their activity against certain diseases. In this study, we investigated the binding of new chalcone derivatives with COX-2 (cyclooxygenase-2) and HSA (Human Serum Albumin) using spectroscopic and molecular modeling studies. COX-2 is commonly found in cancer and plays a role in the production of prostaglandin E (2), which can help tumors grow by binding to receptors. HSA is the most abundant protein in blood plasma, and it transports various compounds, including hormones and fatty acids. The conformation of chalcone derivatives in the HSA complex system was established through fluorescence steady and excited state spectroscopy techniques and FTIR analyses. To gain a more comprehensive understanding, molecular docking, and dynamics were conducted on the target protein (COX-2) and transport protein (HSA). In addition, we conducted density-functional theory (DFT) and single-point DFT to understand intermolecular interaction in protein active sites.
Over recent years, carbon quantum dots (CQDs) have advanced significantly and gained substantial attention for their numerous benefits. These benefits include their simple preparation, cost-effectiveness, small size, biocompatibility, bright luminescence, and low cytotoxicity. As a result, they hold great potential for various fields, including bioimaging. A fascinating aspect of synthesizing CQDs is that it can be accomplished by using biomass waste as the precursor. Furthermore, the synthesis approach allows for control over the physicochemical characteristics. This paper unequivocally examines the production of CQDs from biomass waste and their indispensable application in bioimaging. The synthesis process involves a simple one-pot hydrothermal method that utilizes biomass waste as a carbon source, eliminating the need for expensive and toxic reagents. The resulting CQDs exhibit tunable fluorescence and excellent biocompatibility, making them suitable for bioimaging applications. The successful application of biomass-derived CQDs has been demonstrated through biological evaluation studies in various cell lines, including HeLa, Cardiomyocyte, and iPS, as well as in medaka fish eggs and larvae. Using biomass waste as a precursor for CQDs synthesis provides an environmentally friendly and sustainable alternative to traditional methods. The resulting CQDs have potential applications in various fields, including bioimaging.