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Xin Jin

Hebei Medical University

ORCID: 0000-0001-7959-6924

Publishes on Neuroinflammation and Neurodegeneration Mechanisms, Neurological Disease Mechanisms and Treatments, Medicinal Plants and Neuroprotection. 83 papers and 2.7k citations.

83Publications
2.7kTotal Citations

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Top publicationsby citations

Pathophysiology of obesity and its associated diseases
Xin Jin, Tingting Qiu, Li Li et al.|Acta Pharmaceutica Sinica B|2023
Cited by 424Open Access

The occurrence of obesity has increased across the whole world. Many epidemiological studies have indicated that obesity strongly contributes to the development of cancer, cardiovascular diseases, type 2 diabetes, liver diseases and other disorders, accounting for a heavy burden on the public and on health-care systems every year. Excess energy uptake induces adipocyte hypertrophy, hyperplasia and formation of visceral fat in other non-adipose tissues to evoke cardiovascular disease, liver diseases. Adipose tissue can also secrete adipokines and inflammatory cytokines to affect the local microenvironment, induce insulin resistance, hyperglycemia, and activate associated inflammatory signaling pathways. This further exacerbates the development and progression of obesity-associated diseases. Although some progress in the treatment of obesity has been achieved in preclinical and clinical studies, the progression and pathogenesis of obesity-induced diseases are complex and unclear. We still need to understand their links to better guide the treatment of obesity and associated diseases. In this review, we review the links between obesity and other diseases, with a view to improve the future management and treatment of obesity and its co-morbidities.

Baicalin mitigates cognitive impairment and protects neurons from microglia‐mediated neuroinflammation via suppressing <scp>NLRP</scp>3 inflammasomes and <scp>TLR</scp>4/<scp>NF</scp>‐κB signaling pathway
Xin Jin, Ming‐Yan Liu, Dongfang Zhang et al.|CNS Neuroscience & Therapeutics|2019
Cited by 371Open Access

AIMS: Baicalin (BAI), a flavonoid compound isolated from the root of Scutellaria baicalensis Georgi, has been established to have potent anti-inflammation and neuroprotective properties; however, its effects during Alzheimer's disease (AD) treatment have not been well studied. This study aimed to investigate the effects of BAI pretreatment on cognitive impairment and neuronal protection against microglia-induced neuroinflammation and to explore the mechanisms underlying its anti-inflammation effects. METHODS: To determine whether BAI plays a positive role in ameliorating the memory and cognition deficits in APP (amyloid beta precursor protein)/PS1 (presenilin-1) mice, behavioral experiments were conducted. We assessed the effects of BAI on microglial activation, the production of proinflammatory cytokines, and neuroinflammation-mediated neuron apoptosis in vivo and in vitro using Western blot, RT-PCR, ELISA, immunohistochemistry, and immunofluorescence. Finally, to elucidate the anti-inflammation mechanisms underlying the effects of BAI, the protein expression of NLRP3 inflammasomes and the expression of proteins involved in the TLR4/NF-κB signaling pathway were measured using Western blot and immunofluorescence. RESULTS: The results indicated that BAI treatment attenuated spatial memory dysfunction in APP/PS1 mice, as assessed by the passive avoidance test and the Morris water maze test. Additionally, BAI administration effectively decreased the number of activated microglia and proinflammatory cytokines, as well as neuroinflammation-mediated neuron apoptosis, in APP/PS1 mice and LPS (lipopolysaccharides)/Aβ-stimulated BV2 microglial cells. Lastly, the molecular mechanistic study revealed that BAI inhibited microglia-induced neuroinflammation via suppression of the activation of NLRP3 inflammasomes and the TLR4/NF-κB signaling pathway. CONCLUSION: Overall, the results of the present study indicated that BAI is a promising neuroprotective compound for use in the prevention and treatment of microglia-mediated neuroinflammation during AD progression.

Foxn1 Regulates Lineage Progression in Cortical and Medullary Thymic Epithelial Cells But Is Dispensable for Medullary Sublineage Divergence
Cited by 160Open Access

The forkhead transcription factor Foxn1 is indispensable for thymus development, but the mechanisms by which it mediates thymic epithelial cell (TEC) development are poorly understood. To examine the cellular and molecular basis of Foxn1 function, we generated a novel and revertible hypomorphic allele of Foxn1. By varying levels of its expression, we identified a number of features of the Foxn1 system. Here we show that Foxn1 is a powerful regulator of TEC differentiation that is required at multiple intermediate stages of TE lineage development in the fetal and adult thymus. We find no evidence for a role for Foxn1 in TEC fate-choice. Rather, we show it is required for stable entry into both the cortical and medullary TEC differentiation programmes and subsequently is needed at increasing dosage for progression through successive differentiation states in both cortical and medullary TEC. We further demonstrate regulation by Foxn1 of a suite of genes with diverse roles in thymus development and/or function, suggesting it acts as a master regulator of the core thymic epithelial programme rather than regulating a particular aspect of TEC biology. Overall, our data establish a genetics-based model of cellular hierarchies in the TE lineage and provide mechanistic insight relating titration of a single transcription factor to control of lineage progression. Our novel revertible hypomorph system may be similarly applied to analyzing other regulators of development.

Epigallocatechin‐3‐Gallate Attenuates Microglial Inflammation and Neurotoxicity by Suppressing the Activation of Canonical and Noncanonical Inflammasome via TLR4/NF‐κB Pathway
Xin Zhong, Mingyan Liu, Weifan Yao et al.|Molecular Nutrition & Food Research|2019
Cited by 124

SCOPE: In this study, it has been investigated whether the neuroprotective efficacy of epigallocatechin-3-gallate (EGCG) is mediated by inhibition of canonical and noncanonical inflammasome activation via toll-like receptor 4 (TLR4)/NF-κB pathway both in LPS+Aβ-induced microglia in vitro and in APP/PS1 mice in vivo. METHODS AND RESULTS: In BV2 cells, EGCG inhibits the expressions of Iba-1, cleaved IL-1β, and cleaved IL-18 induced by LPS+Aβ. Then, the supernatants are used to treat SH-SY5Y cells, and EGCG treatment significantly recovers the neurotoxicity from LPS+Aβ-induced microglial conditioned media. Subsequently, it has been found that EGCG reduces the microglial expressions of caspase-1 p20, NLRP3, and caspase-11 p26. Furthermore, the expression levels of Toll-like receptor 4 (TLR4), p-IKK/IKK, and p-NF-κB/NF-κB were decreased after EGCG treatment. As expected, when a caspase-1 specific inhibitor Z-YVAD-FMK, and an IKK and caspase-11 inhibitor wedelolactone are used for blocking, Z-YVAD-FMK and wedelolactone exacerbate the inhibitory efficacy than using EGCG alone. Finally, consistent with the results obtained in BV2 cells, EGCG treatment reduces microglial inflammation and neurotoxicity by suppressing the activation of canonical NLRP3 and noncanonical caspase-11-dependent inflammasome via TLR4/NF-κB pathway in LPS+Aβ-induced rat primary microglia and hippocampus of APP/PS1 mice. CONCLUSION: EGCG attenuates microglial inflammation and neurotoxicity by inhibition of canonical NLRP3 and noncanonical caspase-11-dependent inflammasome activation via TLR4/NF-κB pathway.

Placenta inflammation is closely associated with gestational diabetes mellitus.
Xue Pan, Xin Jin, Jun Wang et al.|PubMed|2021
Cited by 122Open Access

OBJECTIVE: To investigate the potential role of placenta inflammation in gestational diabetes mellitus (GDM) and construct a model for the diagnosis of GDM. METHODS: In this study, transcriptome-wide profiling datasets, GSE70493 and GSE128381 were downloaded from Gene Expression Omnibus (GEO) database. Significant immune-related genes were identified separately to be the biomarkers for the diagnosis of GDM by using random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). RESULTS: RF was the best model and was used to select the four key immune-related genes (FABP4, DKK1, CXCL10, and IL1RL1) to diagnose GDM. A nomogram model was constructed to predict GDM based on the four key immune-related genes by using "rms" package. The relative proportion of 22 immune cell types were calculated by using CIBERSORT algorithm. Higher M1 macrophage ratio and lower M2 macrophage ratio in GDM placenta compared to normal patients were observed. CONCLUSIONS: This study provides clues that inflammation was correlated with GDM and suggests inflammation may be the cause and also the potential targets of GDM.