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Abstract Background Remnant cholesterol (RC) and nonhigh-density lipoprotein cholesterol (nonHDL-C) are key risk factors for atherosclerotic cardiovascular disease (ASCVD), with apolipoprotein B (apoB) and lipoprotein(a) [Lp(a)] also contributing to its residual risk. However, real-world population-based evidence regarding the impact of current clinical LDL-C-centric lipid-lowering therapy (LLT) on achieving RC and nonHDL-C goals, as well as on modifying residual CVD risk factors is limited. Methods This prospective observational study enrolled 897 CVD patients from September, 2020 to July, 2021. All participants had previously received low-/moderate-intensity LLT and were discharged with either low-/moderate-intensity LLT or high-intensity LLT. After a median follow-up of 3 months, changes in RC, nonHDL-C, and other biomarkers were assessed. Multivariate logistic regression was performed to analyze the impact of the LLT on goal attainment. Results Among all patients, 83.50% transitioned to high-intensity LLT from low or moderate. After follow-up, the high-intensity group saw significantly greater reductions in RC (-20.51% vs. -3.90%, P = 0.025), nonHDL-C (-25.12% vs. 0.00%, P

Background: The association between prior percutaneous coronary intervention (PCI) and prognosis after coronary artery bypass grafting (CABG) remains uncertain. We aimed to evaluate the aforementioned association in a meta-analysis. Methods: PubMed, Cochrane’s Library, and Embase databases were searched for potential studies. A random-effects model was used for the meta-analysis. Meta-regression was performed to evaluate the influence of study characteristics on the outcomes. Results: Thirty-six follow-up studies with 308,284 patients were included, and 40,892 (13.3%) patients had prior PCI. Pooled results showed that prior PCI was associated with higher risks of early (in-hospital or within 1 month) all-cause mortality [odds ratio (OR): 1.26, 95% confidence interval (CI): 1.11–1.44, p = 0.003; I 2 = 64%] and major adverse cardiovascular events (MACEs; OR: 1.36, 95% CI: 1.12–1.66, p = 0.002, I 2 = 79%), but not with late (follow-up durations from 1 to 13 years) mortality (OR: 1.03, 95% CI: 0.95–1.13, p = 0.44, I 2 = 46%) or MACEs (OR: 1.03, 95% CI: 0.97–1.09, p = 0.38, I 2 = 0%). Meta-regression showed that the study characteristics of patient number, age, sex, diabetic status, and proportion of patients with prior PCI did not affect the outcomes. Sensitivity analyses limited to multivariate studies excluding patients with acute PCI failure showed similar results (early mortality, OR: 1.25, p = 0.003; early MACE, OR: 1.50, p = 0.001; late mortality, OR: 1.03, p = 0.70). Conclusion: The current evidence, mostly from retrospective observational studies, suggests that prior PCI is related to poor early clinical outcomes, but not to late clinical outcomes, after CABG.

Electric load forecasting has always been a key component of power grids. Many countries have opened up electricity markets and facilitated the participation of multiple agents, which create a competitive environment and reduce costs to consumers. In the electricity market, multi-step short-term load forecasting becomes increasingly significant for electricity market bidding and spot price calculation, but the performances of traditional algorithms are not robust and unacceptable enough. In recent years, the rise of deep learning gives us the opportunity to improve the accuracy of multi-step forecasting further. In this paper, we propose a novel model multi-scale convolutional neural network with time-cognition (TCMS-CNN). At first, a deep convolutional neural network model based on multi-scale convolutions (MS-CNN) extracts different level features that are fused into our network. In addition, we design an innovative time coding strategy called the periodic coding strengthening the ability of the sequential model for time cognition effectively. At last, we integrate MS-CNN and periodic coding into the proposed TCMS-CNN model with an end-to-end training and inference process. With ablation experiments, the MS-CNN and periodic coding methods had better performances obviously than the most popular methods at present. Specifically, for 48-step point load forecasting, the TCMS-CNN had been improved by 34.73%, 14.22%, and 19.05% on MAPE than the state-of-the-art methods recursive multi-step LSTM (RM-LSTM), direct multi-step MS-CNN (DM-MS-CNN), and the direct multi-step GCNN (DM-GCNN), respectively. For 48-step probabilistic load forecasting, the TCMS-CNN had been improved by 3.54% and 6.77% on average pinball score than the DM-MS-CNN and the DM-GCNN. These results show a great promising potential applied in practice.

Background: Green tea has been shown to improve cholesterol metabolism in animal studies, but the molecular mechanisms underlying this function have not been fully understood. Long non-coding RNAs (lncRNAs) have recently emerged as a major class of regulatory molecules involved in a broad range of biological processes and complex diseases. Our aim was to identify important lncRNAs that might play an important role in contributing to the benefits of epigallocatechin-3-gallate (EGCG) on cholesterol metabolism. Methods: Microarrays was used to reveal the lncRNA and mRNA profiles in green tea polyphenol(-)-epigallocatechin gallate in cultured human liver (HepG2) hepatocytes treated with EGCG and bioinformatic analyses of the predicted target genes were performed to identify lncRNA-mRNA targeting relationships. RNA interference was used to investigate the role of lncRNAs in cholesterol metabolism. Results: The expression levels of 15 genes related to cholesterol metabolism and 285 lncRNAs were changed by EGCG treatment. Bioinformatic analysis found five matched lncRNA-mRNA pairs for five differentially expressed lncRNAs and four differentially expressed mRNA. In particular, the lncRNA AT102202 and its potential targets mRNA-3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) were identified. Using a real-time polymerase chain reaction technique, we confirmed that EGCG down-regulated mRNA expression level of the HMGCR and up-regulated expression of AT102202. After AT102202 knockdown in HepG2, we observed that the level of HMGCR expression was significantly increased relative to the scrambled small interfering RNA control (P < 0.05). Conclusions: Our results indicated that EGCG improved cholesterol metabolism and meanwhile changed the lncRNAs expression profile in HepG2 cells. LncRNAs may play an important role in the cholesterol metabolism.

Abstract The linear ubiquitin chain assembly complex (LUBAC) mediates the linear ubiquitination of various proteins and is involved in NF-κB signaling and immune regulation. However, the function and mechanism of linear ubiquitination in regulating oncogenic signaling and tumor growth have remained poorly understood. Herein, we identified Gli proteins, key transcription factors in the Hedgehog (Hh) signaling pathway, as novel substrates of LUBAC. Linear ubiquitination stabilizes Gli proteins, leading to the noncanonical activation of Hh signaling in CRC cells. Furthermore, LUBAC facilitates tumor growth in CRC cells. Additionally, elevated expression of LUBAC components in CRC tissues was observed, and higher expression levels of these components correlated with poor prognosis in CRC patients. Interestingly, inhibition of LUBAC using either a small molecule agonist or RNA silencing specifically suppressed cell growth in CRC cells but had no effect on normal intestinal cells. Taken together, aberrant expression of LUBAC components activates Hh signaling noncanonically by mediating linear ubiquitination, promoting tumor growth in CRC, demonstrating the novel function of linear ubiquitination in regulating the protein stability of its substrates and highlighting the potential of targeting LUBAC as a therapeutic strategy in CRC.

Background. Seizures and the subsequent development of epilepsy after stroke may not only hinder patient's recovery but also increase the risk of complications. Interleukin (IL)-1ß has been shown to be acutely upregulated after ischemic stroke and play a role in the recurrence of seizures following the first epileptic seizure in patients suffering an ischemic stroke. Meanwhile, variants of the IL-1B gene encoding IL-1ß are involved in the stimulation of febrile seizures. Objectives. To study the potential associations of the 5 polymorphisms of the IL-1B gene with seizure susceptibility in ischemic stroke patients, and to explore the possible mechanisms. Materials and methods. A total of 856 ischemic stroke patients were allocated into the control group (patients without post-stroke seizures) and the case group (patients with post-stroke seizures). The IL-1B polymorphisms rs10490571 (T/C), rs114363 (C/T), rs1143623 (G/C), rs16944 (T/C), and rs2853550 (A/G) were detected using TaqMan SNP genotyping assays, and serum IL-1ß levels were measured using the enzymelinked immunosorbent assay (ELISA). Demographic data, clinical characteristics and cerebrovascular disease risk factors at admission were collected. Multivariate analysis was performed to determine independent associations, and IL-1ß levels were compared using analysis of variance (ANOVA) followed by a post hoc test. Results. In 74 patients (8.6%, 74/856), post-stroke seizures occurred within 1 year of stroke onset. The multivariate analysis showed that the rs16944 polymorphism of IL-1B, cortical involvement and National Institutes of Health Stroke Scale (NIHSS) score on admission were correlated with post-stroke seizures after adjusting for stroke laterality, thrombolysis, use of statins, and IL-1B rs10490571. The IL-1B rs16944 TT (odds ratio (OR): 1.923, 95% confidence interval (95% CI): 1.257-4.185) and TC genotypes (OR: 1.469, 95% CI: 1.130-2.974) were associated with a significantly increased risk of post-stroke seizures compared to the CC genotype. One-way ANOVA for IL-1ß levels demonstrated a tendency for higher levels in TT > TC > CC genotypes (6.41 compared to 4.53 compared to 2.10 pg/mL, respectively). Conclusions. The IL-1B rs16944 polymorphism had an independent association with seizure susceptibility after ischemic stroke. The mechanism might be associated with the regulation of IL-1ß levels. [ABSTRACT FROM AUTHOR]

Abstract Background Menopausal status has a known relationship with the levels of estrogen, progesterone, and other sex hormones, potentially influencing the activity of ER, PR, and many other signaling pathways involved in the initiation and progression of breast cancer. However, the differences between premenopausal and postmenopausal breast cancer patients at the molecular level are unclear. Methods We retrieved eight datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with menopausal status in breast cancer patients were identified using the MAMA and LIMMA methods. Based on these validated DEGs, we performed Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein–protein interaction (PPI) networks were constructed. We used DrugBank data to investigate which of these validated DEGs are targetable. Survival analysis was performed to explore the influence of these genes on breast cancer patient prognosis. Results We identified 762 DEGs associated with menopausal status in breast cancer patients. PPI network analysis indicated that these genes are primarily involved in pathways such as the cell cycle, oocyte meiosis and progesterone-mediated oocyte maturation pathways. Notably, several genes played roles in multiple signaling pathways and were associated with patient survival. These genes were also observed to be targetable according to the DrugBank database. Conclusion We identified DEGs associated with menopausal status in breast cancer patients. The association of these genes with several key pathways may promote understanding of the complex characterizations of breast cancer. Our findings offer valuable insights for developing new therapeutic strategies tailored to the menopausal status of breast cancer patients.