Background: This retrospective study aimed to investigate ethnic disparities in demographic, clinicopathologic and biological behaviors of gastric cancer (GC) and established a novel nomogram for predicting overall survival (OS) of individual patients between two main ethnicities in a high GC-incidence area of China. Methods: There were 5,022 GC patients, including 3,987 Han (79.4%) and 987 Hui (14.4%) from Northwest China. All patient data were retrieved from 2009 to 2017. Median survival was estimated using the Kaplan-Meier method and compared using the log-rank test. A Cox proportional hazards model and the rms package of R were used to assess the impact of covariates and to generate the nomogram prediction model, respectively. Findings: Similarly low 5-OS rates were shown in both the Hui and Han groups (23.8% vs 24.2%). Hui at stage T1, N0 or with tumour sizes below 5 cm had 2.144-fold, 1.426-fold or 1.305-fold increased risks of poor prognosis compared with Han ( P <0.05). Further, Hui had 1.265-, 1.364- and 1.401-fold increased risks of poor prognosis compared with Han when all patients had high expression of Ki67, EGFR, and VEGF, respectively ( P <0.05). Based on the data from the Cox model, the nomogram exhibited superior 3- and 5-year survival predictive ability to that of the 7 th AJCC TNM staging system. Interpretation: Ethnic disparities exist in the GC prognosis in Northwest China. Understanding the effects of ethnicity on GC will guide reasonable evaluations of prognosis and future interventions to equalize access to high-quality care for GC patients of different ethnicities in China. Funding Statement: This study was funded by grants from National Natural Science Foundation of China (No. 81760525, 81160249, and 81560485).Declaration of Interests: The authors declared no potential conflicts of interest.Ethics Approval Statement: The study was approved by the Ethical Committee of Ningxia Medical University prior to study conduct (No. 2017-013). Informed consent was obtained from all patients included in the study.
Abstract Background Posttraumatic epilepsy (PTE) is one of the most critical complications of traumatic brain injury (TBI), significantly increasing TBI patients' neuropsychiatric symptoms and mortality. The abnormal accumulation of glutamate caused by TBI and its secondary excitotoxicity are essential reasons for neural network reorganization and functional neural plasticity changes, contributing to the occurrence and development of PTE. Restoring glutamate balance in the early stage of TBI is expected to play a neuroprotective role and reduce the risk of PTE. Aims To provide a neuropharmacological insight for drug development to prevent PTE based on regulating glutamate homeostasis. Methods We discussed how TBI affects glutamate homeostasis and its relationship with PTE. Furthermore, we also summarized the research progress of molecular pathways for regulating glutamate homeostasis after TBI and pharmacological studies aim to prevent PTE by restoring glutamate balance. Results TBI can lead to the accumulation of glutamate in the brain, which increases the risk of PTE. Targeting the molecular pathways affecting glutamate homeostasis helps restore normal glutamate levels and is neuroprotective. Discussion Taking glutamate homeostasis regulation as a means for new drug development can avoid the side effects caused by direct inhibition of glutamate receptors, expecting to alleviate the diseases related to abnormal glutamate levels in the brain, such as PTE, Parkinson's disease, depression, and cognitive impairment. Conclusion It is a promising strategy to regulate glutamate homeostasis through pharmacological methods after TBI, thereby decreasing nerve injury and preventing PTE.
Abstract The relationship between ischemic stroke (IS) and pyroptosis centers on the inflammatory response elicited by cerebral tissue damage during an ischemic stroke event. However, an in-depth mechanistic understanding of their connection remains limited. This study aims to comprehensively analyze the gene expression patterns of pyroptosis-related differentially expressed genes (PRDEGs) by employing integrated IS datasets and machine learning techniques. The primary objective was to develop classification models to identify crucial PRDEGs integral to the IS process. Leveraging three distinct machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), models were developed to differentiate between the Control and the IS patient samples. Through this approach, a core set of 10 PRDEGs consistently emerged as significant across all three machine learning models. Subsequent analysis of these genes yielded significant insights into their functional relevance and potential therapeutic approaches. In conclusion, this investigation underscores the pivotal role of pyroptosis pathways in IS and identifies pertinent targets for therapeutic development and drug repurposing.
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