Valtrate is a principle compound isolated from Valeriana jatamansi Jones, a traditional Chinese folk medicine originally used to treat various nervous disorders. Here, we found that valtrate exhibited significant anti-cancer activity in vitro, especially in human breast cancer cells, while displayed relatively low cytotoxicity to normal human breast epithelial cells (MCF 10A). Valtrate induced cell cycle arrest at G2/M stage and apoptosis in MDA-MB-231 and MCF-7 cells, with reduced expression of p-Akt (Ser 473), cyclin B1 and caspase 8, and increased expression of p21, p-cdc2, cleaved-caspase 3, cleaved-caspase 7 and poly (ADP-ribose) polymerase (PARP). In addition, valtrate inhibited cell migration through down-regulation of MMP-9 and MMP-2 expression. These results demonstrate that valtrate possesses anti-breast cancer activities via cell cycle arrest, apoptosis, and inhibition of cell migration, thus supporting valtrate as a potential antitumor agent. Graphical Abstract

Background: Retinoids are promising agents in the treatment of different types of neoplasia including estrogen receptor-positive breast cancers, whereas refractoriness/low sensitivity is observed in triple-negative breast cancer (TNBC) subtype. However, the reason for these diverse retinoid-sensitivity remains elusive. Methods: Determinants of retinoid sensitivity were investigated using immunohistochemistry of primary patient samples, and identified retinoic acid receptor α (RARα) as a putative factor. The anti-tumor activity of hypo-phosphorylated RARα was investigated in TNBC cell models and a xenograft mouse model. Next, miRNA sequencing analysis was performed to identify the target miRNA of RARα, and luciferase reporter was used to confirm the direct target gene of miR-3074-5p. Results: We discovered that serine-77 residue of RARα was constantly phosphorylated, which correlated with TNBC’s resistance to retinoids. Overexpression of a phosphorylation-defective mutant RARαS77A mimicked activated RARα and repressed TNBC cell progression both in vitro and in vivo, via activating cell cycle arrest, apoptosis, and cytotoxic autophagy, independent of RARα agonists. We further revealed that the anti-tumor action of RARαS77A was, at least in part, mediated by the up-regulation of miR-3074-5p, which directly targeted DHRS3, a reductase negatively associated with TNBC patient survival. Our results suggest that the inhibition of RARαS77 phosphorylation by either expressing RARαS77A or inhibiting RARα’s phosphokinase CDK7, can bypass RA stimuli to transactivate tumor-suppressive miR-3074-5p and reduce oncogenic DHRS3, thus overcoming the RA-resistance of TNBC. Conclusion: The novel regulatory network, involving RARαS77 phosphorylation, miR-3074-5p, and DHRS3, emerges as a new target for TNBC treatment.

As global demand for clean energy continues to rise, hydrogen, as an ideal energy carrier, plays a crucial role in the energy transition. Traditional hydrogen production methods predominantly rely on fossil fuels, leading to environmental pollution and energy inefficiency. In contrast, plasma-assisted hydrogen production, as an emerging technology, has gained significant attention due to its high efficiency, environmental friendliness, and flexibility. Plasma technology generates high-energy electrons or ions by exciting gas molecules, which, under specific conditions, effectively decompose water vapor or hydrocarbon gases to produce hydrogen. This review systematically summarizes the basic principles, technological routes, research progress, and potential applications of plasma-assisted hydrogen production. It focuses on various plasma-based hydrogen production methods, such as water vapor decomposition, hydrocarbon cracking, arc discharge, and microwave discharge, highlighting their advantages and challenges. Additionally, it addresses key issues facing plasma-assisted hydrogen production, including energy efficiency improvement, reactor stability, and cost optimization, and discusses the future prospects of these technologies. With ongoing advancements, plasma-assisted hydrogen production is expected to become a mainstream technology for hydrogen production, contributing to global goals of zero carbon emissions and sustainable energy development.

In the process of steel strip production, the accuracy of defect detection remains a challenge due to the diversity of defect types, complex backgrounds, and noise interference. To improve the effectiveness of surface defect detection in steel strips, we propose an enhanced detection model known as YOLOv8-BSPB. First, we propose a novel pooling layer module, SCRD, which replaces max pooling with average pooling. This module introduces the receptive field block (RFB) and deformable convolutional network version 4 (DCNv4) to obtain learnable offsets, allowing convolutional kernels to flexibly move and deform on the input feature map, thus, more effectively extracting multi-scale features. Second, we integrate a polarized self-attention (PSA) mechanism to improve the model’s feature representation and enhance its ability to focus on relevant information. Additionally, we incorporate the BAM attention mechanism after the C2f module to strengthen the model’s feature selection capabilities. A bidirectional feature pyramid network is introduced at the neck of the model to improve feature transmission efficiency. Finally, the WIoU loss function is employed to accelerate the model’s convergence speed and enhance regression accuracy. Experimental results on the NEU-DET dataset demonstrate that the improved model achieves a classification accuracy of 81.3%, an increase of 4.9% over the baseline, with a mean average precision of 86.9%. The model has a parameter count of 5.5 M and operates at 103.1 FPS. To validate the model’s effectiveness, we conducted tests on the Kaggle steel strip dataset and our custom dataset, where the average accuracy improved by 2.3% and 5.5%, respectively. The experimental results indicate that the model meets the requirements for real-time, lightweight, and portable deployment.