Involvement of Wnt/β-catenin pathway in the inhibition of invasion and epithelial-mesenchymal transition in ovarian cancer cells

Purpose: To investigate the effects of zerumbone on cell invasion, epithelial-mesenchymal transition (EMT) and the potential signaling pathway involved in ovarian cancer cells.Methods: Caov-3 cell proliferation was assessed using 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-diphenytetrazoliumromide (MTT) assay. Wound healing assay was used to determine Caov-3 cell migration while cell invasion was evaluated using Transwell assay. Protein expression was determinedby western blot.Results: Cell viability was reduced by 5, 10, 20, and 50 μM zerumbone (p < 0.05) in a concentrationdependent manner while cell migration and invasion were inhibited by 10 and 20 μM zerumbone (p < 0.05). Protein expression levels of E-cadherin and cytoplasm β-catenin were upregulated by zerumbone (p < 0.05) in a concentration-dependent manner. On the other hand, protein expression levels of Ncadherin, vimentin, ZEB1, nuclear β-catenin, and c-Myc were suppressed by zerumbone (p < 0.05) also in a concentration-dependent manner.Conclusion: The results demonstrate that zerumbone inhibits cell proliferation, migration and invasion, but represses the EMT process via inactivation of Wnt/β-catenin signaling pathway. Keywords: Zerumbone, Ovarian cancer, Wnt/β-catenin pathway, Epithelial-mesenchymal transitio

Assessment Method for Health State of Li-Ion Batteries Based on Sparrow Search Algorithm

[Introduction] Accurate estimation of the Li-ion batteries' State of Health (SoH) is essential for future intelligent battery management systems. To solve the problems of poor quality of data features and difficulties in adjusting model parameters, this study proposes a method for estimating the SoH of lithium batteries based on singular value fixed-order noise reduction and the sparrow search algorithm which can optimize the gated recurrent unit (GRU) neural network.[Method] Firstly, three indicators highly correlated with SoH decay were extracted from the battery charge and discharge data. Noise reduction was applied to the features using singular value decomposition techniques to improve their correlation with SoH. Next, using the sparrow search algorithm to optimize the model structure and parameters of the GRU neural network improve the accuracy of estimation of SoH. Finally, the battery data sets from Centre for Advanced Life Cycle Engineering (CALCE) were used to verify the validity of the proposed model. [Result] The experimental results show that the model proposed in this study applies to the battery SoH estimation, with a maximum root mean square error (RMSE) of only 0.018 4. After data noise reduction and algorithm optimization, the RMSE of the GRU model is reduced by 55.41% compared to the initial model. [Conclusion] The method proposed in this paper accurately estimates SoH and can be used as a reference for practical engineering applications

Targeting mitophagy for depression amelioration: a novel therapeutic strategy

Major depressive disorder is a global psychiatric condition characterized by persistent low mood and anhedonia, which seriously jeopardizes the physical and mental well-being of affected individuals. While various hypotheses have been proposed to explicate the etiology of depression, the precise pathogenesis and effective treatment of this disorder remain elusive. Mitochondria, as the primary organelles responsible for cellular energy production, possess the ability to meet the essential energy demands of the brain. Research indicated that the accumulation of damaged mitochondria is associated with the onset of depression. Mitophagy, a type of cellular autophagy, specifically targets and removes excess or damaged mitochondria. Emerging evidence demonstrated that mitophagy dysfunction was involved in the progression of depression, and several pharmacological interventions that stimulating mitophagy exerted excellent antidepressant actions. We provided an overview of updated advancements on the regulatory mechanism of mitophagy and the mitophagy abnormality in depressed patients and animals, as well as in cell models of depression. Meanwhile, various therapeutic strategies to restore mitophagy for depression alleviation were also discussed in this review

Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia

Introduction: Preeclampsia is a disease that affects both the mother and child, with serious consequences. Screening the characteristic genes of preeclampsia and studying the placental immune microenvironment are expected to explore specific methods for the treatment of preeclampsia and gain an in-depth understanding of the pathological mechanism of preeclampsia.Methods: We screened for differential genes in preeclampsia by using limma package. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, disease ontology enrichment, and gene set enrichment analyses were performed. Analysis and identification of preeclampsia biomarkers were performed by using the least absolute shrinkage and selection operator regression model, support vector machine recursive feature elimination, and random forest algorithm. The CIBERSORT algorithm was used to analyze immune cell infiltration. The characteristic genes were verified by RT-qPCR.Results: We identified 73 differential genes, which mainly involved in reproductive structure and system development, hormone transport, etc. KEGG analysis revealed emphasis on cytokine–cytokine receptor interactions and interleukin-17 signaling pathways. Differentially expressed genes were dominantly concentrated in endocrine system diseases and reproductive system diseases. Our findings suggest that LEP, SASH1, RAB6C, and FLT1 can be used as placental markers for preeclampsia and they are associated with various immune cells.Conclusion: The differentially expressed genes in preeclampsia are related to inflammatory response and other pathways. Characteristic genes, LEP, SASH1, RAB6C, and FLT1 can be used as diagnostic and therapeutic targets for preeclampsia, and they are associated with immune cell infiltration. Our findings contribute to the pathophysiological mechanism exploration of preeclampsia. In the future, the sample size needs to be expanded for data analysis and validation, and the immune cells need to be further validated

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Measurement of t(t)over-bar normalised multi-differential cross sections in pp collisions at root s=13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions

Peer reviewe