Research progress on the role and mechanisms of microglia in inflammatory diseases of central nervous system

Microglia are the resident immune cells in the central nervous system (CNS), and play a dual role in maintaining brain homeostasis and mediating neuroprotection. Under normal conditions, microglia maintain brain homeostasis by monitoring environmental changes. When nerve damage or certain pathological stimuli occur, microglia are rapidly activated and initiate a series of complex immune responses to induce neuroinflammation. This proper activation of microglia can protect the brain by inhibiting or clearing various pathogens, but excessive neuroinflammation can lead to neuronal damage and even death. This imbalance of inflammatory response is one of the core features of pathological development of many CNS inflammatory diseases, such as Alzheimer′s disease, Parkinson′s disease, sepsis-associated encephalopathy, and ischemic strokes. In recent years, with the rapid development of frontier biotechnology such as single-cell sequencing, proteinomics and gene editing, important progress has been made in understanding the molecular mechanism by which microglia participate in CNS inflammatory diseases, especially in the activation of inflammatory corpuscles, epigenetic modifications, and metabolic reprogramming. However, due to the heterogeneity and duality of microglia under different pathological conditions, therapeutic methods targeting microglia have not yet been widely used in clinical practice. In summary, this article takes microglia as the starting point and introduces the molecular mechanisms of their involvement in the occurrence and development of CNS inflammatory diseases and its targeted regulatory treatment strategy, aiming to provide theoretical reference for the subsequent precise regulation of microglia function and the development of more targeted therapeutic drugs

Identifying the Conformational Isomers of Single-Molecule Cyclohexane at Room Temperature

构象异构是化学中的基本问题。然而对于环己烷等柔性分子，由于其在室温下极快的互变异构过程，基于系综的表征方法（如核磁等）只能得到所有构象平均贡献的结果。为了应对这一挑战，化学化工学院洪文晶教授与夏海平教授课题组为在室温条件下对柔性分子构象的定量分析与表征这一挑战，课题组成功实现了在室温条件下对环己烷两种椅式构象的电学表征与比例识别。同时，通过纳米电极间隙对分子的限域作用，发现在宏观尺度下极不稳定的扭船式中间体得以在单分子尺度稳定存在，这为不稳定中间体的研究提供了重要表征方法。 这一研究工作是在化学化工学院洪文晶教授、夏海平教授共同指导下完成的，iChEM直博生唐淳与化工系研究生唐永翔为论文共同第一作者。师佳副教授与刘俊扬副研究员为该工作提供了指导，博士后陈志昕、博士研究生陈李珏以及研究生叶艺玲、严哲玮、张珑漪共同参与了该工作。【Abstract】Isomerism reflects the ubiquitous nature that molecules with the same molecular formula show different structures. The interconversion between conformational isomers of flexible molecules is quite fast owing to the low barriers of around 10 kcal mol−1, leading to average signal contributed by all the possible isomers characterized by ensemble methods. On this account, identifying the conformational isomers of flexible molecules at room temperature has a substantial challenge. Here, we develop a single-molecule approach to identify the conformational isomers of cyclohexane at room temperature through the single-molecule electrical characterization. By noise analysis and feature extraction of the conductance of single-molecule junctions, we quantificationally identified two chair isomers of cyclohexane at room temperature, while such identification is only feasible at low temperatures by ensemble characterization. The strategy to apply the single-molecule approach to identify conformational isomers paves the avenue to investigate the isomerization of flexible molecules beyond the ensemble methods.This work was supported by the National Natural Science Foundation of China (nos, 21722305, 21673195, 21703188, and U1705254), the National Key R&D Program of China (2017YFA0204902), China Postdoctoral Science Foundation (no. 2017M622060), and the Fundamental Research Funds for Xiamen University (20720190002).该工作获得了科技部国家重点研发计划、国家自然科学基金等项目的资助，也得到了固体表面物理化学国家重点实验室、能源材料化学协同创新中心的支持

Causal association between type 2 diabetes mellitus and acute suppurative otitis media: insights from a univariate and multivariate Mendelian randomization study

BackgroundType 2 diabetes mellitus (T2DM) and hearing loss (HL) constitute significant public health challenges worldwide. Recently, the association between T2DM and HL has aroused attention. However, possible residual confounding factors and other biases inherent to observational study designs make this association undetermined. In this study, we performed univariate and multivariable Mendelian Randomization (MR) analysis to elucidate the causal association between T2DM and common hearing disorders that lead to HL.MethodsOur study employed univariate and multivariable MR analyses, with the Inverse Variance Weighted method as the primary approach to assessing the potential causal association between T2DM and hearing disorders. We selected 164 and 9 genetic variants representing T2DM from the NHGRI-EBI and DIAGRAM consortium, respectively. Summary-level data for 10 hearing disorders were obtained from over 500,000 participants in the FinnGen consortium and MRC-IEU. Sensitivity analysis revealed no significant heterogeneity of instrumental variables or pleiotropy was detected.ResultsIn univariate MR analysis, genetically predicted T2DM from both sources was associated with an increased risk of acute suppurative otitis media (ASOM) (In NHGRI-EBI: OR = 1.07, 95% CI: 1.02-1.13, P = 0.012; In DIAGRAM: OR = 1.14, 95% CI: 1.02-1.26, P = 0.016). Multivariable MR analysis, adjusting for genetically predicted sleep duration, alcohol consumption, body mass index, and smoking, either individually or collectively, maintained these associations. Sensitivity analyses confirmed the robustness of the results.ConclusionT2DM was associated with an increased risk of ASOM. Strict glycemic control is essential for the minimization of the effects of T2DM on ASOM

Electric-Field-Induced Connectivity Switching in Single-Molecule Junctions

Summary(#br)The manipulation of molecule-electrode interaction is essential for the fabrication of molecular devices and determines the connectivity from electrodes to molecular components. Although the connectivity of molecular devices could be controlled by molecular design to place anchor groups in different positions of molecule backbones, the reversible switching of such connectivities remains challenging. Here, we develop an electric-field-induced strategy to switch the connectivity of single-molecule junctions reversibly, leading to the manipulation of different connectivities in the same molecular backbone. Our results offer a new concept of single-molecule manipulation and provide a feasible strategy to regulate molecule-electrode interaction

A C-X-C Chemokine Receptor Type 2–Dominated Cross-talk between Tumor Cells and Macrophages Drives Gastric Cancer Metastasis

Purpose: C-X-C chemokine receptor type 2 (CXCR2) is a key regulator that drives immune suppression and inflammation in tumor microenvironment. CXCR2-targeted therapy has shown promising results in several solid tumors. However, the underlying mechanism of CXCR2-mediated cross-talk between gastric cancer cells and macrophages still remains unclear. Experimental Design: The expression of CXCR2 and its ligands in 155 human gastric cancer tissues was analyzed via immunohistochemistry, and the correlations with clinical characteristics were evaluated. A coculture system was established, and functional assays, including ELISA, transwell, cell viability assay, and qPCR, were performed to determine the role of the CXCR2 signaling axis in promoting gastric cancer growth and metastasis. A xenograft gastric cancer model and a lymph node metastasis model were established to study the function of CXCR2 in vivo. Results: CXCR2 expression is associated with the prognosis of patients with gastric cancer (P = 0.002). Of all the CXCR2 ligands, CXCL1 and CXCL5 can significantly promote migration of gastric cancer cells. Macrophages are the major sources of CXCL1 and CXCL5 in the gastric cancer microenvironment, and promote migration of gastric cancer cells through activating a CXCR2/STAT3 feed-forward loop. Gastric cancer cells secrete TNF-α to induce release of CXCL1 and CXCL5 from macrophages. Inhibiting CXCR2 pathway of gastric cancer cells can suppress migration and metastasis of gastric cancer in vitro and in vivo. Conclusions: Our study suggested a previously uncharacterized mechanism through which gastric cancer cells interact with macrophages to promote tumor growth and metastasis, suggesting that CXCR2 may serve as a promising therapeutic target to treat gastric cancer

YuLan: An Open-source Large Language Model

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training details hinders further research and development. This paper presents the development of YuLan, a series of open-source LLMs with $12$ billion parameters. The base model of YuLan is pre-trained on approximately $1.7$T tokens derived from a diverse corpus, including massive English, Chinese, and multilingual texts. We design a three-stage pre-training method to enhance YuLan's overall capabilities. Subsequent phases of training incorporate instruction-tuning and human alignment, employing a substantial volume of high-quality synthesized data. To facilitate the learning of complex and long-tail knowledge, we devise a curriculum-learning framework throughout across these stages, which helps LLMs learn knowledge in an easy-to-hard manner. YuLan's training is finished on Jan, 2024 and has achieved performance on par with state-of-the-art LLMs across various English and Chinese benchmarks. This paper outlines a comprehensive technical roadmap for developing LLMs from scratch. Our model and codes are available at https://github.com/RUC-GSAI/YuLan-Chat

Towards Effective and Efficient Continual Pre-training of Large Language Models

Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents a technical report for continually pre-training Llama-3 (8B), which significantly enhances the Chinese language ability and scientific reasoning ability of the backbone model. To enhance the new abilities while retaining the original abilities, we design specific data mixture and curriculum strategies by utilizing existing datasets and synthesizing high-quality datasets. Specifically, we synthesize multidisciplinary scientific question and answer (QA) pairs based on related web pages, and subsequently incorporate these synthetic data to improve the scientific reasoning ability of Llama-3. We refer to the model after CPT as Llama-3-SynE (Synthetic data Enhanced Llama-3). We also present the tuning experiments with a relatively small model -- TinyLlama, and employ the derived findings to train the backbone model. Extensive experiments on a number of evaluation benchmarks show that our approach can largely improve the performance of the backbone models, including both the general abilities (+8.81 on C-Eval and +6.31 on CMMLU) and the scientific reasoning abilities (+12.00 on MATH and +4.13 on SciEval), without hurting the original capacities. Our model, data, and codes are available at https://github.com/RUC-GSAI/Llama-3-SynE.16 pages, 10 figures, 16 table