RESUMEN

Ischemic cardiomyopathy (ICM) is a serious cardiac disease with a very high mortality rate worldwide, which causes myocardial ischemia and hypoxia as the main damage. Further understanding of the underlying pathological processes of cardiomyocyte injury is key to the development of cardioprotective strategies. Ferroptosis is an iron-dependent form of regulated cell death characterized by the accumulation of lipid hydroperoxides to lethal levels, resulting in oxidative damage to the cell membrane. The current understanding of the role and regulation of ferroptosis in ICM is still limited, especially in the absence of evidence from large-scale transcriptomic data. Through comprehensive bioinformatics analysis of human ICM transcriptome data obtained from the Gene Expression Omnibus database, the present study identified differentially expressed ferroptosis-related genes (DEFRGs) in ICM. Subsequently, their potential biological mechanisms and cross-talk were analyzed, and hub genes were identified by constructing protein-protein interaction networks. Ferroptosis features such as reactive oxygen species generation, changes in ferroptosis marker proteins, iron ion aggregation and lipid oxidation, were identified in the H9c2 anoxic reoxygenation injury model. Finally, the diagnostic ability of Gap junction alpha-1 (GJA1), Solute carrier family 40 member 1 (SLC40A1), Alpha-synuclein (SNCA) were identified through receiver operating characteristic curves and the expression of DEFRGs was verified in an in vitro model. Furthermore, potential drugs (retinoic acid) that could regulate ICM ferroptosis were predicted based on key DEFRGs. The present article presents new insights into the role of ferroptosis in ICM, investigating the regulatory role of ferroptosis in the pathological process of ICM and advocating for ferroptosis as a potential novel therapeutic target for ICM based on evidence from the ICM transcriptome.

RESUMEN

Accumulating evidence shows that the abnormal proliferation and migration of vascular smooth muscle cells (VSMCs) can significantly affect the long-term prognosis of coronary artery bypass grafting. This study aimed to explore the factors affecting the proliferation, migration, and phenotypic transformation of VSMCs. First, we stimulated VSMCs with different platelet-derived growth factor-BB (PDGF-BB) concentrations, analyzed the expression of phenotype-associated proteins by Western blotting, and examined cell proliferation by scratch wound healing and the 5-ethynyl-2-deoxyuridine (EdU) assay. VSMC proliferation was induced most by PDGF-BB treatment at 20 ng/mL. miR-200a-3p decreased significantly in A7r5 cells stimulated with PDGF-BB. The overexpression of miR-200a-3p reversed the downregulation of α-SMA (p < 0.001) and the upregulation of vimentin (p < 0.001) caused by PDGF-BB. CCK8 and EdU analyses showed that miR-200a-3p overexpression could inhibit PDGF-BB-induced cell proliferation (p < 0.001). However, flow cytometric analysis showed that it did not significantly increase cell apoptosis. Collectively, the overexpression of miR-200a-3p inhibited the proliferation and migration of VSMCs induced by PDGF-BB, partly by affecting phenotypic transformation-related proteins, providing a new strategy for relieving the restenosis of vein grafts.

Asunto(s)

MicroARNs , Músculo Liso Vascular , Becaplermina/farmacología , Proliferación Celular , Miocitos del Músculo Liso , Fenotipo , MicroARNs/genética , Movimiento Celular , Células Cultivadas

RESUMEN

In prior research, 6,12-diphenyl-3,9-diazatetraasterane-1, 5, 7, 11-tetracarboxylate (DDTC) has been shown to be an effective inhibitor of the growth of the SKOV3 and A2780 ovarian cancer (OC) cell lines. Flow cytometry analyses indicated that DDTC was able to suppress P-CNA expression at the protein level within OC cells, while RNA-seq indicated that DDTC treatment was associated with marked changes in gene expression profiles within A2780 cells. Molecular docking analyses suggested that DDTC has the potential to readily dock with key signaling proteins including PI3K, AKT, and mTOR. In line with these findings, DDTC treatment inhibited the growth of xenograft tumors in a mouse model system. Such treatment was also associated with reduced p-PI3K/PI3K, p-AKT/AKT, p-mTOR/mTOR, and CyclinD1 (CCND1) expressions and with the increased expression of PTEN in vitro and in vivo. Together, these results suggest that DDTC is capable of readily inhibiting OC development at least in part via targeting and modulating signaling via the PI3K/AKT/mTOR axis.

Asunto(s)

Neoplasias Ováricas , Proteínas Proto-Oncogénicas c-akt , Animales , Apoptosis , Carcinoma Epitelial de Ovario , Línea Celular Tumoral , Proliferación Celular , Femenino , Humanos , Ratones , Simulación del Acoplamiento Molecular , Neoplasias Ováricas/patología , Fosfatidilinositol 3-Quinasas/metabolismo , Proteínas Proto-Oncogénicas c-akt/metabolismo , Transducción de Señal , Serina-Treonina Quinasas TOR/metabolismo

RESUMEN

The development and utilization of solid waste is an effective way to solve the severe environmental and energy crisis. In this study, Thermogravimetry - Fourier transform infrared spectrometry (TG-FTIR) was used to carry out the co-combustion experiment of coal slime and rice husk under different mixing ratios. With the increase of the mass percentage of rice husk in the sample, the initial ignition temperature and burnout of the sample decreased, and the comprehensive combustion performance improved gradually. The dominant reaction in the co-combustion of coal slime and rice husk was determined by statistical method. When the mass percentage of rice husk in the mixture is between 30 and 90 %, it can inhibit the release of NOx and SO2. Taking Kissinger-Akahira-Sunose method as an example, the calculated average activation energies of coal slime and rice husk combustion are 105.66 and 148.93 kJ/mol respectively. With the increase of the mixing ratio of rice husk in the blend, the combustion mechanism of the sample changed. Finally, the mean absolute error, root mean square error and determination coefficient of the artificial neural network model are 0.52697, 0.67866 and 0.99941 respectively.

Asunto(s)

Carbón Mineral , Oryza , Biomasa , Carbón Mineral/análisis , Cinética , Redes Neurales de la Computación , Análisis de Componente Principal , Espectroscopía Infrarroja por Transformada de Fourier

RESUMEN

Background: Claudins (CLDNs) are a family of closely related transmembrane proteins that have been linked to oncogenic transformation and metastasis across a range of cancers, suggesting that they may be valuable diagnostic and/or prognostic biomarkers that can be used to evaluate patient outcomes. However, CLDN expression patterns associated with colorectal cancer (CRC) remain to be defined. Methods: The mRNA levels of 21 different CLDN family genes were assessed across 20 tumor types using the Oncomine database. Correlations between these genes and patient clinical outcomes, immune cell infiltration, clinicopathological staging, lymph node metastasis, and mutational status were analyzed using the GEPIA, UALCAN, Human Protein Atlas, Tumor Immune Estimation Resource, STRING, Genenetwork, cBioportal, and DAVID databases in an effort to clarify the potential functional roles of different CLDN protein in CRC. Molecular docking analyses were used to probe potential interactions between CLDN4 and TGFß1. Levels of CLDN4 and CLDN11 mRNA expression in clinical CRC patient samples and in the HT29 and HCT116 cell lines were assessed via qPCR. CLDN4 expression levels in these 2 cell lines were additionally assessed following TGFß1 inhibitor treatment. Results: These analyses revealed that COAD and READ tissues exhibited the upregulation of CLDN1, CLDN2, CLDN3, CLDN4, CLDN7, and CLDN12 as well as the downregulation of CLDN5 and CLDN11 relative to control tissues. Higher CLDN11 and CLDN14 expression as well as lower CLDN23 mRNA levels were associated with poorer overall survival (OS) outcomes. Moreover, CLDN2 and CLDN3 or CLDN11 mRNA levels were significantly associated with lymph node metastatic progression in COAD or READ lower in COAD and READ tissues. A positive correlation between the expression of CLDN11 and predicted macrophage, dendritic cell, and CD4+ T cell infiltration was identified in CRC, with CLDN12 expression further being positively correlated with CD4+ T cell infiltration whereas a negative correlation was observed between such infiltration and the expression of CLDN3 and CLDN15. A positive correlation between CLDN1, CLDN16, and neutrophil infiltration was additionally detected, whereas neutrophil levels were negatively correlated with the expression of CLDN3 and CLDN15. Molecular docking suggested that CLDN4 was able to directly bind via hydrogen bond with TGFß1. Relative to paracancerous tissues, clinical CRC tumor tissue samples exhibited CLDN4 and CLDN11 upregulation and downregulation, respectively. LY364947 was able to suppress the expression of CLDN4 in both the HT29 and HCT116 cell lines. Conclusion: Together, these results suggest that the expression of different CLDN family genes is closely associated with CRC tumor clinicopathological staging and immune cell infiltration. Moreover, CLDN4 expression is closely associated with TGFß1 in CRC, suggesting that it and other CLDN family members may represent viable targets for antitumor therapeutic intervention.

RESUMEN

We investigate semisupervised learning (SL) and pool-based active learning (AL) of a classifier for domains with label-scarce (LS) and unknown categories, i.e., defined categories for which there are initially no labeled examples. This scenario manifests, e.g., when a category is rare, or expensive to label. There are several learning issues when there are unknown categories: 1) it is a priori unknown which subset of (possibly many) measured features are needed to discriminate unknown from common classes and 2) label scarcity suggests that overtraining is a concern. Our classifier exploits the inductive bias that an unknown class consists of the subset of the unlabeled pool's samples that are atypical (relative to the common classes) with respect to certain key (albeit a priori unknown) features and feature interactions. Accordingly, we treat negative log- p -values on raw features as nonnegatively weighted derived feature inputs to our class posterior, with zero weights identifying irrelevant features. Through a hierarchical class posterior, our model accommodates multiple common classes, multiple LS classes, and unknown classes. For learning, we propose a novel semisupervised objective customized for the LS/unknown category scenarios. While several works minimize class decision uncertainty on unlabeled samples, we instead preserve this uncertainty [maximum entropy (maxEnt)] to avoid overtraining. Our experiments on a variety of UCI Machine learning (ML) domains show: 1) the use of p -value features coupled with weight constraints leads to sparse solutions and gives significant improvement over the use of raw features and 2) for LS SL and AL, unlabeled samples are helpful, and should be used to preserve decision uncertainty (maxEnt), rather than to minimize it, especially during the early stages of AL. Our AL system, leveraging a novel sample-selection scheme, discovers unknown classes and discriminates LS classes from common ones, with sparing use of oracle labeling.