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Heterophyllin B (HB) is a cyclic peptide with anti-neoplastic effect on many cancers. However, its effect and mechanism of action in ovarian cancer cells are still unknown. The primary objective of this study was to assess the impact of HB on the proliferation of ovarian cancer (OC) cells and delve into the underlying mechanisms involved. We performed CCK-8 assays, HE staining, KI67 staining, clonogenic formation assays, Annexin V-FITC/PI staining, tumor invasion assays, and migration assays to detect the effects of HB on cell viability, proliferation, apoptosis, migration, and invasion in ovarian cancer cells. Additionally, real-time fluorescent quantitative PCR (qPCR) and Western blotting were utilized for verification. The expression of NF-E2-related factor 2 (NRF2) and heme oxygenase 1 (HMOX1/HO-1) signaling molecules was detected using qPCR and Western blotting. A specific inducer, Hemin, was used to activate HO-1 and Nrf2 overexpression, in order to verify the pharmacological mechanism of HB on ovarian cancer cells. The binding relationship between HB and NRF2 was investigated through molecular docking. HB treatment inhibited the viability of OC cells, meanwhile it showed suppressive effect on the proliferation, migration, and invasion of OC cells, Meanwhile, HB could promote the apoptosis of tumor cells. For the mechanisms, we found that HB treatment could significantly down-regulate the levels of NRF2/HO-1. Consistent with the results of in vitro experiments, administration of HB significantly delayed tumor growth in OVCAR8 xenografted nude mice, and inhibited the expression of Ki67, Nrf2 and HO-1. This study demonstrated that HB had anti-neoplastic effect on OC by inhibiting Nrf2/HO-1 signaling pathway and may be a potential drug for the treatment of OC. • Heterophyllin B inhibits proliferation, invasion, and migration of ovarian cancer cells. • Heterophyllin B promotes apoptosis in ovarian cancer cells. • Heterophyllin B inhibits ovarian tumor growth by suppressing Nrf2/HO-1 signaling. • HB enhances the expression of proinflammatory cytokines in ovarian tumors. [ABSTRACT FROM AUTHOR]

Building photovoltaic (PV) power generation is intermittent, volatile, random, and uncontrollable; thus, the use of solar grid-connected power generation can lead to a series of problems, such as grid voltage fluctuations and power imbalance. As a typical flexible load, the scheduling and regulation of the heating, ventilation, and air conditioning (HVAC) system load can help a grid-connected solar grid achieve balanced and flexible operation. This study proposes a PV capacity dynamic tracking model predictive control strategy for air-conditioning systems with flexible loads. This strategy aims to effectively address the issue of unstable grid-connected output of solar PV systems. In particular, a solar radiation prediction model based on a long short-term memory neural network optimized by the grey wolf optimization algorithm was established, which effectively improved the model prediction accuracy. The cost function of HVAC flexible load control considers the law of solar PV output and the human thermal comfort model. A genetic algorithm (GA) is used to obtain the optimal control parameters for dynamic regulation. The GA was employed to efficiently and effectively optimize the control of flexible HVAC loads, considering the variability of the solar PV output and ensuring human thermal comfort. A dynamic regulation model was developed for a typical office building with a grid-connected solar PV power generation system. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. A new evaluation metric, volatility, was proposed to evaluate net load fluctuation. The simulation platform based on the physical building yield results indicated a strong resemblance between the energy consumption curve under the predicted control conditions using the flexible model and the PV generation curve. Additionally, the net load volatility was measured to be 2.63. Compared with the predicted control condition without the flexible model, the net load volatility of the grid was reduced by 47.08%, and the energy-saving rate reached 10.89% in the summer. [Display omitted] • Tracking MPC strategy is proposed for fine-tuning control of HVAC system. • The law of photovoltaic output is considered in the proposed strategy. • Grey Wolf Optimization algorithm is used to tune LSTM neural network. • Control strategies comparisons exhibit the effectiveness of the proposed strategy. • A new metric-"volatility" is proposed to evaluate load fluctuations. [ABSTRACT FROM AUTHOR]

In medical research, publication bias (PB) poses great challenges to the conclusions from systematic reviews and meta-analyses. The majority of efforts in methodological research related to classic PB have focused on examining the potential suppression of studies reporting effects close to the null or statistically non-significant results. Such suppression is common, particularly when the study outcome concerns the effectiveness of a new intervention. On the other hand, attention has recently been drawn to the so-called inverse publication bias (IPB) within the evidence synthesis community. It can occur when assessing adverse events because researchers may favor evidence showing a similar safety profile regarding an adverse event between a new intervention and a control group. In comparison to the classic PB, IPB is much less recognized in the current literature; methods designed for classic PB may be inaccurately applied to address IPB, potentially leading to entirely incorrect conclusions. This article aims to provide a collection of accessible methods to assess IPB for adverse events. Specifically, we discuss the relevance and differences between classic PB and IPB. We also demonstrate visual assessment through contour-enhanced funnel plots tailored to adverse events and popular quantitative methods, including Egger's regression test, Peters' regression test, and the trim-and-fill method for such cases. Three real-world examples are presented to illustrate the bias in various scenarios, and the implementations are illustrated with statistical code. We hope this article offers valuable insights for evaluating IPB in future systematic reviews of adverse events. [ABSTRACT FROM AUTHOR]