Antitumor activity of dictamnine against colorectal cancer through induction of ferroptosis and inhibition of M2 macrophage polarization via the MAPK signaling.

Colorectal cancer (CRC) is an aggressive cancer type globally. Surgery and chemotherapy are often ineffective at curing CRC. Dictamnine is a natural product derived from Dictamnus dasycarpus Turcz. root bark and possesses multi-pharmacological properties, including anticancer effects. Nevertheless, the biological roles and the possible mechanism of dictamnine in CRC are still unclear. Here, we demonstrated that dictamnine blocked cell viability and proliferation in DLD-1 human colorectal adenocarcinoma cells and LoVo human colon cancer cells. Dictamnine triggered CRC cell ferroptosis, as evidenced by enhanced levels of reactive oxygen species, malondialdehyde, and Fe2+ levels, alongside downregulation of glutathione peroxidase 4 protein expression. In addition, CD163 (HPA ID: HPA046404) was highly expressed and CD68 (HPA ID: CAB000051) was lowly expressed in CRC tissues and CRC cell culture medium-cultured THP-1 monocytes-derived macrophages. The patients with CD163 low-expression lived much longer than those with CD163 high-expression, indicating that M2 polarization of macrophages was related to poor prognosis of CRC. Dictamnine markedly inhibited CD163 protein expression, transforming growth factor-ß and arginase 1 mRNA expressions and IL-10 production in macrophages with CRC cell co-culture, suggesting that dictamnine impeded M2 polarization of macrophages. Mechanistically, dictamnine repressed ERK phosphorylation in CRC cells. The treatment with the ERK activator tBHQ counteracted the effects of dictamnine on CRC cell proliferation and ferroptosis, as well as its inhibitory effect on M2 polarization of macrophages. Results of a xenograft model showed that dictamnine effectively hindered CRC tumor growth in vivo. Collectively, these data provide evidence for the clinical trials of dictamnine as a novel drug for CRC therapy.

Benzimidazole-based structure optimization to discover novel anti-gastric cancer agents targeting ROS/MAPK pathway.

Given the malignancy of gastric cancer, developing highly effective and low-toxic targeted drugs is essential to prolong patient survival and improve patient outcomes. In this study, we conducted structural optimizations based on the benzimidazole scaffold. Notably, compound 8 f presented the most potent antiproliferative activity in MGC803 cells and induced cell cycle arrest at the G0/G1 phase. Further mechanistic studies demonstrated that compound 8 f caused the apoptosis of MGC803 cells by elevating intracellular reactive oxygen species (ROS) levels and activating the mitogen-activated protein kinase (MAPK) signaling pathway, accompanied by corresponding markers change. In vivo investigations additionally validated the inhibitory effect of compound 8 f on tumor growth in xenograft models bearing MGC803 cells without obvious toxicity. Our studies suggest that compound 8 f holds promise as a potential and safe lead compound for developing anti-gastric cancer agents.

Super-resolution reconstruction for early cervical cancer magnetic resonance imaging based on deep learning.

This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.

Experiment and analysis of physical, mechanical, and viscoelastic properties of the roots and stalks of green leafy vegetables.

Green leafy vegetables are an essential component of Chinese leafy vegetables. Due to their crisp stems and tender leaves, orderly harvester generally causes significant mechanical clamping damage. The physical and mechanical properties of green leafy vegetables are one of the important basis to design the orderly harvester. At the same time, they provide important parameters for the simulation and optimization of harvester. So, this paper measured the physical characteristic parameters of roots and stems of green leafy vegetables. Then, based on the TMS-Pro texture analyzer, the elasticity modulus of the roots and stems of green leafy vegetables were measured. The static friction coefficient, dynamic friction coefficient, and restitution coefficient of green leafy vegetables root-root, stem-stem, root-steel, and stem-steel were measured separately using a combination method of inclined plane and high-speed photography. Uniaxial compression creep experiments were carried out on whole and single leaf of green leafy vegetables using the TA.XT plus C universal testing machine. The constitutive equation of the four-element Burgers model was used to fit the deformation curve of the sample with time during the constant-pressure loading stage. The fitting determination coefficients R2 were all higher than 0.996, which verified the reasonable validity of the selected model. The above experimental results provide a parameter basis and theoretical support for the design and discrete element simulation optimization of orderly harvester critical components of green leafy vegetables.

Insect-transmitted plant virus balances its vertical transmission through regulating Rab1-mediated receptor localization.

Rice stripe virus (RSV) establishes infection in the ovaries of its vector insect, Laodelphax striatellus. We demonstrate that RSV infection delays ovarian maturation by inhibiting membrane localization of the vitellogenin receptor (VgR), thereby reducing the vitellogenin (Vg) accumulation essential for egg development. We identify the host protein L. striatellus Rab1 protein (LsRab1), which directly interacts with RSV nucleocapsid protein (NP) within nurse cells. LsRab1 is required for VgR surface localization and ovarian Vg accumulation. RSV inhibits LsRab1 function through two mechanisms: NP binding LsRab1 prevents GTP binding, and NP binding LsRab1-GTP complexes stimulates GTP hydrolysis, forming an inactive LsRab1 form. Through this dual inhibition, RSV infection prevents LsRab1 from facilitating VgR trafficking to the cell membrane, leading to inefficient Vg uptake. The Vg-VgR pathway is present in most oviparous animals, and the mechanisms detailed here provide insights into the vertical transmission of other insect-transmitted viruses of medical and agricultural importance.