Biomedicine (Journal)
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\u3ci\u3eGynura bicolor\u3c/i\u3eaqueous extract attenuated H\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e2\u3c/sub\u3einduced injury in PC12 cells
No full textBackground: Protective effects ofGynura bicoloraqueous extract (GAE) at three concentrations upon nerve growth factor (NGF) differentiated-PC12 cells against H2O2induced injury were examined.
Methods: NGF differentiated-PC12 cells were treated with GAE at 0.25%, 0.5% or 1%. 100 μM H2O2was used to treat cells with GAE pre-treatments. After incubating at 37 °C for 12 hr, experimental analyses were processed.
Results: H2O2exposure decreased cell viability, increased plasma membrane damage, suppressed Bcl-2 mRNA expression and enhanced Bax mRNA expression. GAE pre-treatments reversed these changes. H2O2exposure reduced mitochondrial membrane potential, lowered Na+-K+-ATPase activity, and increased DNA fragmentation and Ca2+release. GAE pre-treatments attenuated these alterations. H2O2stimulated the production of reactive oxygen species (ROS), interleukin (IL)-1beta, IL-6 and tumor necrosis factor-alpha, lowered glutathione content, and reduced glutathione peroxidase (GPX) and catalase activities. GAE pretreatments maintained GPX and catalase activities; and concentration-dependently diminished the generation of ROS and inflammatory cytokines. H2O2enhanced mRNA expression of nuclear factor kappa (NF-κ) B and p38. GAE pre-treatments decreased mRNA expression of NF-κB and p38. Conclusion: These findings suggested that GAE might be a potent neuronal protective agent
The potential therapeutic effect for melatonin and mesenchymal stem cells on hepatocellular carcinoma
No full textBackground/aim: Herein, we investigated the potential therapeutic effect of Melatonin (Mel) and/or mesenchymal stem cells (MSCs) on rat model of HCC.
Materials and Methods: Female mature rats were divided into 5 groups (n= 10/group): normal (Nor), HCC group intraperitoneally injected with 200 mg/kg DEN, and 3 treated groups; HCC + Mel (Mel) group given Mel intraperitoneally 20 mg/kg, twice a week, HCC + MSCs (MSCs) group intravenously injected by 1 × 106 cells, and HCC + MSCs (Mel +MSCs) group.
Results: Rats in HCC group showed most deteriorated effect in form of increased mortality and relative liver weight, elevated serum levels of ALT, AST, ALP, AFP and GGT in addition to increased pre-neoplastic nodules in liver tissues. Liver tissues of HCC group also exhibited lower level of apoptosis as indicated by decreased DNA fragmentation and expression ofp53caspase 9 and caspase 3 genes and increased PCNA immunoreactivity. Moreover, in this group the expression ofIL6andTGFβ1genes was significantly upregulated. All these deleterious effects induced by DEN were reversed after administration of Mel and/ or MSCs with best improvement for the combined group (MSCs + Mel).
Conclusions: These findings reveal a better therapeutic effect for MSCs when given with Mel and we attribute this beneficial effect, at least in part, to triggering apoptosis and targeting inflammation in HCC. Therefore, combined treatment with Mel and MSCs is recommended to enhance the therapeutic potential against HCC
Immunohistochemical expression of P53, Ki-67, and CD34 in psoriasis and psoriasiform dermatitis
No full textBackground: Psoriasis is the prime example of psoriasiform tissue pattern and should be differentiated from other psoriasiform dermatoses both clinically and histopathologically.
Aim: To evaluate immunohistochemical expression of P53, Ki-67, and CD34 in psoriasis and psoriasiform dermatitis for diagnostic purposes.
Methods: An analytical cross-sectional study was performed on the paraffin blocks of 60 psoriasis and 31 psoriasiform dermatitis patients between 2014 and 2017. The selected formalin-fixed paraffin-embedded tissues from each biopsy specimen were cut into 4-micron sections. Initial sections were stained by hematoxylin and eosin staining. Primary antihuman antibodies against P53, Ki-67, and CD34 were applied. Positive control samples for biomarkers were received from former strongly positive samples of papillary endothelial hyperplasia, high grade lymphoma, and breast ductal carcinoma for CD34, Ki-67, and P53, respectively.
Results: Out of 60 psoriasis patients, 56.7% were men, with the mean age of 36.8 years. From 31 psoriasiform patients, 45.2% were men, with the mean age of 37.5 years. Both groups were matched in terms of sex and age. The mean staining of three markers was more significant in psoriasiform dermatitis than psoriasis.
Conclusion: In spite of some other researches, the present study showed expression of P53, Ki-67, and CD34 biomarkers were significantly higher in psoriasiform dermatitis than psoriasis
The RNA degradation pathway is involved in PPARα-modulated anti-oral tumorigenesis
No full textBackground: The activation of peroxisome proliferator-activated receptor alpha (PPARα) has been shown to reprogram tumor metabolism and exhibits great potential for treating anti-oral tumorigenesis.
Methods: In this study, we used a pathway-based strategy to explore possible functional pathways involved in the anticancer activity of PPARα in oral cancer cells through next-generation sequencing (NGS) and bioinformatic approaches.
Results: We found that 3919 genes were upregulated and 1060 genes were downregulated through PPARα activation. These genes were mainly involved in the proteasomal, mRNA surveillance, spliceosomal, RNA transport, and RNA degradation pathways, as indicated by GO and KEGG enrichment analysis. Importantly, a total of 13 upregulated genes in the RNA degradation pathway were identified including 3 core exosome factor genes (RRP43, RRP42,andCSL4), 2 TRAMP complex genes (TRF4andMtr4), 2 exosome cofactor genes (RRP6andMPP6), 2 CCR4-NOT complex genes (CNOT2andCNOT3), 2 Ski complex genes (SKI2andSki3), 1 decapping complex gene (EDC4), and 1 gene involved in 5’ exoribonuclease activity (XRN1).
Conclusion: Our findings suggest that the activation of PPARα to upregulate the RNA degradation pathway might provide a new strategy for oral cancer treatment
Machine Learning-Guided Differential Gene Expression Analysis Identifies A Highly-Connected Seven-Gene Cluster in Triple-Negative Breast Cancer
No full textBackground: One of the most challenging cancers is triple-negative breast cancer, which is subdivided into many molecular subtypes. Due to the high degree of heterogeneity, the role of precision medicine remains challenging. With the use of machine learning (ML)-guided gene selection, the differential gene expression analysis can be optimized, and eventually, the process of precision medicine can see great advancement through biomarker discovery.
Purpose: Enhancing precision medicine in the oncology field by identification of the most representative differentially-expressed genes to be used as biomarkers or as novel drug targets.
Methods: By utilizing data from the Gene Expression Omnibus (GEO) repository and The Cancer Genome Atlas (TCGA), we identified the differentially expressed genes using the linear model for microarray analysis (LIMMA) and edgeR algorithms, and applied ML-based feature selection using several algorithms.
Results: A total of 27 genes were selected by merging features identified with both LIMMA and ML-based feature selection methods. The models with the highest area under the curve (AUC) are CatBoost, Extreme Gradient Boosting (XGBoost), Random Forest, and Multi-Layer Perceptron classifiers. ESR1, FOXA1, GATA3, XBP1, GREB1, AR, and AGR2 were identified as hub genes in a highly interconnected cluster.
Conclusion: ML-based gene selection shows a great impact on the identification of hub genes. The ML models built can improve precision oncology in diagnosis and prognosis. The identified hub genes can serve as biomarkers and warrant further research for potential drug target development
Artificial intelligence-driven prediction and validation of blood-brain barrier permeability and absorption, distribution, metabolism, excretion profiles in Natural Product Research Laboratory compounds
No full textIntroduction: Our previous research demonstrated that a large language model (LLM) based on the transformer architecture, specifically the MegaMolBART encoder with an XGBoost classifier, effectively predicts the blood-brain barrier (BBB) permeability of compounds. However, the permeability coefficients of compounds that can traverse this barrier remain unclear. Additionally, the absorption, distribution, metabolism, and excretion (ADME) characteristics of substances obtained from the Natural Product Research Laboratory (NPRL) at China Medical University Hospital (CMUH) have not yet been determined.
Objectives: The study aims to investigate the pharmacokinetic ADME properties and BBB permeability coefficients of NPRL compounds.
Materials and Methods: A combined model using a transformer-based MegaMolBART encoder and XGBoost classifier was employed to predict BBB permeability. Machine learning (ML) tools from Discovery Studio were used to assess the ADME characteristics of the NPRL compounds. The CCK-8 assay was conducted to evaluate the cytotoxic effects of NPRL compounds on bEnd.3 brain endothelial cells after exposure to 10 μg/mL of the compounds. We assessed the permeability coefficient by subjecting bEnd.3 cell monolayers to the test compounds and measuring the permeability of FITC-dextran.
Results: There were 4,956 compounds that could cross the blood-brain barrier
(BBB+) and 2,851 that could not (BBB−) in the B3DB dataset that was utilized for training. A total of 2,461 BBB+ and 2,184 BBB− compounds were used in the
NPRL-CMUH dataset for testing. The permeability coefficient of temozolomide (TMZ) and 21 other BBB+ compounds exceeded 10×10-7 cm/s. Computational analysis revealed that NPRL compounds exhibited a variety of ADME characteristics.
Conclusion: Computer-based predictions for the NPRL of CMUH compounds regarding their capacity to traverse the BBB are verified by the findings. Artificial intelligence (AI) prediction models have effectively identified the potential ADME characteristics of various compounds
Unveiling the Power of Flavonoids: A Dynamic Exploration of Their Impact on Cancer through Matrix Metalloproteinases Regulation
No full textCancer is a prominent contributor to mortality rates globally, with its progression and spread being the primary factors underlying this assertion. Despite advancements in cancer therapy, the existing therapeutic strategies are rendered useless as a result of their toxicity and the development of chemoresistance. Hence, it is imperative to assess alternate therapy modalities, and natural compounds present a promising prospect due to their demonstrated anticancer capabilities in several research models. This article provides an overview of the regulatory mechanisms involved in the expression of metalloproteinases (MMPs) and explores the potential therapeutic function of flavonoids as agents with anticancer activity that specifically target MMPs. Multiple mechanisms indicate that flavonoids are chemopreventive and cytotoxic against a wide range of cancer types, according to data gathered from cell lines and in vivo models. Involved in cancer cell invasion, migration, and metastasis, flavonoids also modulate critical signaling pathways including signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase (MAPK), NFkB, and PI3/AKT. All of these findings reestablish flavonoids as outstanding candidates for cancer treatment
Comparative analysis of Doxycycline and Ayurvedic herbs to target metastatic breast cancer: An in-silico approach
No full textBackground: Metastasis of breast cancer cells to distant sites including lungs, liver, lymph node, brain and many more have substantially affected the overall survival outcome and distant metastasis free survival rate amongst the diseased individuals. Several pre-clinical and clinical studies were carried out to determine the potency of vigorous inhibitors but they extensively deteriorated the patient’s quality of life. Hence, there exists an urgent need to explore potent natural remedy to fight against metastatic breast cancer.
Methods: Ayurvedic medicinal plants documented in literature for their ability to fight against breast cancer was screened and their respective active moieties were evaluated to exert inhibitory effect against MMP9. Drug like efficacy of phytochemicals were determined using Molecular docking, MD Simulation, ADMET and MM-PBSA and were further compared with synthetic analogs i.e. Doxycycline.
Results: Out of 1000 phytochemicals, 12 exerted highest binding affinity (BA) even more than -9.0 Kcal/mol that was significantly higher in comparison to Doxycycline which exhibited BA of -7.3 Kcal/mol. In comparison to 37 × 30 × 37 Å, 53 × 45 × 66 Å offered best binding site and the highest BA was exhibited by Viscosalactone at LYS104, ASP185, MET338, LEU39, ASN38. During MD Simulation, ViscosalactoneMMP9 complex remained stable for 20 ns and the kinetic, electrostatic and potential energies were observed to be better than Doxycycline. Furthermore, Viscosalactone obtained from Withania somnifera justified the Lipinski’s Rule of 5.
Conclusion: Viscosalactone obtained from Withania somnifera may act as promising drug candidate to fight against metastatic breast cancer
