Therapeutic effects of Saikosapoin D on bleomycininduced pulmonary fibrosis in mice via regulation of IL- 33/ST2 pathway

Purpose: To investigate the therapeutic effects of saikosapoin D (SSD) on bleomycin (BLM)-induced pulmonary fibrosis (PF) in mice and its probable mechanisms.Methods: PF mice were prepared by intraperitoneal (i.p.) injection of BLM (5 mg/kg). Twenty-four hours later, 72 mice in SSD group were administered SSD (1.8 mg/kg, ip). After 3, 7, 14 and 28 days of injection, the mice were sacrificed. Blood samples and lung tissues were collected from 6 mice in each group. The lung tissues were subjected to histological examination. In addition, expressions of MyD88, TRAF6, IL-33 and ST2 in lung tissue were determined by western blotting assay. Serum levels of hydroxyproline (HYP), interleukin (IL)-4, IL-13 and interferon (IFN)-γ were measured by enzyme-linked immunosorbent assay (ELISA).Results: Pathological results showed that SSD treatment alleviated alveolitis and lung fibrosis (p < 0.05) in lung tissues of PF mice at 14 and 28 days post-BLM injection. HYP and IL-13 levels of mice in SSD group were significantly lower than that in BLM group at days 14 and 28 post-BLM injection (p < 0.05). Levels of IL-4 and IFN-γ were significantly lower when compared with values in BLM group on day 28 (p < 0.05). Western blotting results revealed that expressions of MyD88, TRAF6, IL-33 and ST2 proteins were significantly decreased by SSD treatment (p < 0.05).Conclusion: SSD exerts therapeutic effects on BLM-induced experimental PF in mice via regulation ofIL-33/ST2 pathway.Keywords: Saikosapoin D, Idiopathic pulmonary fibrosis, Myeloid differentiation factor, Hydroxyproline, Interleukin, Interferon, IL-33/ST2 pathwa

Secondary Impact of Manganese on the Catalytic Properties of Nitrogen-Doped Graphene in the Hydrogen Evolution Reaction

Catalysts play a key role in hydrogen production as green energy carriers. We show herein for the first time that manganese impurities in graphene can improve the catalytic activity of synthesized N‐doped graphene (NG) for the hydrogen evolution reaction in acid media by influencing the ratio of different N‐functionalities. A 122 mV improvement in the overpotential was found following the Mn impregnation of graphene. Transmission electron microscopy images confirmed the formation of manganese oxide nanoparticles on the NG sheets. X‐ray photoelectron spectroscopy revealed structural alteration in favor of higher quantities of quaternary and pyrrolic nitrogen functionalities, from approximately 37 % in NG to 84 % in the Mn‐inserted‐doped graphene catalyst. This enhanced catalytic performance, based on density functional theory calculations in the literature, was attributed to an increase in the number of active sites with higher activity

Plant-Derived Alkaloids: The Promising Disease-Modifying Agents for Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) represents a group of intestinal disorders with self-destructive and chronic inflammation in the digestive tract, requiring long-term medications. However, as many side effects and drug resistance are frequently encountered, safer and more effective agents for IBD treatment are urgently needed. Over the past few decades, a variety of natural alkaloids made of plants or medicinal herbs have attracted considerable interest because of the excellent antioxidant and anti-inflammatory properties; additionally, these alkaloids have been reported to reduce the colonic inflammation and damage in a range of colitic models. In this review paper, we summarize the recent findings regarding the anti-colitis activity of plant-derived alkaloids and emphasize their therapeutic potential for the treatment of IBD; obvious improvement of the colonic oxidative and pro-inflammatory status, significant preservation of the epithelial barrier function and positive modulation of the gut microbiota are the underlying mechanisms for the plant-derived alkaloids to treat IBD. Further clinical trials and preclinical studies to unravel the molecular mechanism are essential to promote the clinical translation of plant-derived alkaloids for IBD

AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation

Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. However, natural language exhibits a far more pronounced sequential dependency in comparison to images, and the majority of existing language models are trained with a left-to-right auto-regressive approach. To account for the inherent sequential characteristic of natural language, we introduce Auto-Regressive Diffusion (AR-Diffusion). AR-Diffusion ensures that the generation of tokens on the right depends on the generated ones on the left, a mechanism achieved through employing a dynamic number of denoising steps that vary based on token position. This results in tokens on the left undergoing fewer denoising steps than those on the right, thereby enabling them to generate earlier and subsequently influence the generation of tokens on the right. In a series of experiments on various text generation tasks, including text summarization, machine translation, and common sense generation, AR-Diffusion clearly demonstrated its superiority over existing diffusion language models and that it can be $100\times\sim600\times$ faster when achieving comparable results. Our code is available at https://github.com/microsoft/ProphetNet/tree/master/AR-diffusion.Comment: Accept By NIPS 202

A Novel Non-Volatile Inverter-based CiM: Continuous Sign Weight Transition and Low Power on-Chip Training

In this work, we report a novel design, one-transistor-one-inverter (1T1I), to satisfy high speed and low power on-chip training requirements. By leveraging doped HfO2 with ferroelectricity, a non-volatile inverter is successfully demonstrated, enabling desired continuous weight transition between negative and positive via the programmable threshold voltage (VTH) of ferroelectric field-effect transistors (FeFETs). Compared with commonly used designs with the similar function, 1T1I uniquely achieves pure on-chip-based weight transition at an optimized working current without relying on assistance from off-chip calculation units for signed-weight comparison, facilitating high-speed training at low power consumption. Further improvements in linearity and training speed can be obtained via a two-transistor-one-inverter (2T1I) design. Overall, focusing on energy and time efficiencies, this work provides a valuable design strategy for future FeFET-based computing-in-memory (CiM)