Lonicerin alleviates intestinal myenteric neuron injury induced by hypoxia/reoxygenation treated macrophages by downregulating EZH2.

Intestinal ischemia-reperfusion (IR) injury is a common gastrointestinal disease that induces severe intestinal dysfunction. Intestinal myenteric neurons participate in maintaining the intestinal function, which will be severely injured by IR. Macrophages are widely reported to be involved in the pathogenesis of organ IR injury, including intestine, which is activated by NLRP3 signaling. Lonicerin (LCR) is a natural extracted monomer with inhibitory efficacy against the NLRP3 pathway in macrophages. The present study aims to explore the potential protective function of LCR in intestinal IR injury. Myenteric neurons were extracted from mice. RAW 264.7 cells were stimulated by H/R with or without 10 µM and 30 µM LCR. Remarkable increased release of IL-6, MCP-1, and TNF-α were observed in H/R treated RAW 264.7 cells, along with an upregulation of NLRP3, cleaved-caspase-1, IL-1ß, and EZH2, which were sharply repressed by LCR. Myenteric neurons were cultured with the supernatant collected from each group. Markedly decreased neuron number and shortened length of neuron axon were observed in the H/R group, which were signally reversed by LCR. RAW 264.7 cells were stimulated by H/R, followed by incubated with 30 µM LCR with or without pcDNA3.1-EZH2. The inhibition of LCR on NLRP3 signaling in H/R treated RAW 264.7 cells was abolished by EZH2 overexpression. Furthermore, the impact of LCR on neuron number and neuron axon length in myenteric neurons in the H/R group was abated by EZH2 overexpression. Collectively, LCR alleviated intestinal myenteric neuron injury induced by H/R treated macrophages via downregulating EZH2.

The influence of near-infrared carbon dots on the conformational variation and enzymatic activity of glucose oxidase: A multi-spectroscopic and biochemical study with molecular docking.

In recent years, the exceptional biocatalytic properties of glucose oxidase (GOx) have spurred the development of various GOx-functionalized nanocatalysts for cancer diagnosis and treatment. Carbon dots, renowned for their excellent biocompatibility and distinctive fluorescence properties, effectively incorporate GOx. Given the paramount importance of GOx's enzymatic activity in therapeutic efficacy, this study conducts a thorough exploration of the molecular-level binding dynamics between GOx and near-infrared carbon dots (NIR-CDs). Utilizing various spectrometric and molecular simulation techniques, we reveal that NIR-CDs form a ground-state complex with GOx primarily via hydrogen bonds and van der Waals forces, interacting directly with amino acid residues in GOx's active site. This binding leads to conformational change and reduces thermal stability of GOx, slightly inhibiting its enzymatic activity and demonstrating a competitive inhibition effect. In vitro experiments demonstrate that NIR-CDs attenuate the GOx's capacity to produce H2O2 in HeLa cells, mitigating enzyme-induced cytotoxicity and cellular damage. This comprehensive elucidation of the intricate binding mechanisms between NIR-CDs and GOx provides critical insights for the design of NIR-CD-based nanotherapeutic platforms to augment cancer therapy. Such advancements lay the groundwork for innovative and efficacious cancer treatment strategies.

A penalized variable selection ensemble algorithm for high-dimensional group-structured data.

This paper presents a multi-algorithm fusion model (StackingGroup) based on the Stacking ensemble learning framework to address the variable selection problem in high-dimensional group structure data. The proposed algorithm takes into account the differences in data observation and training principles of different algorithms. It leverages the strengths of each model and incorporates Stacking ensemble learning with multiple group structure regularization methods. The main approach involves dividing the data set into K parts on average, using more than 10 algorithms as basic learning models, and selecting the base learner based on low correlation, strong prediction ability, and small model error. Finally, we selected the grSubset + grLasso, grLasso, and grSCAD algorithms as the base learners for the Stacking algorithm. The Lasso algorithm was used as the meta-learner to create a comprehensive algorithm called StackingGroup. This algorithm is designed to handle high-dimensional group structure data. Simulation experiments showed that the proposed method outperformed other R2, RMSE, and MAE prediction methods. Lastly, we applied the proposed algorithm to investigate the risk factors of low birth weight in infants and young children. The final results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.508 and a root mean square error (RMSE) of 0.668. The obtained values are smaller compared to those obtained from a single model, indicating that the proposed method surpasses other algorithms in terms of prediction accuracy.