Neuropathic pain is frequently comorbidity with cognitive deficits. Neuralized1 (Neurl1)-mediated ubiquitination of CPEB3 in the hippocampus is critical in learning and memory. However, the role of Neurl1 in the cognitive impairment in neuropathic pain remains elusive. Herein, we found that lumbar 5 spinal nerve ligation (SNL) in male rat-induced neuropathic pain was followed by learning and memory deficits and LTP impairment in the hippocampus. The Neurl1 expression in the hippocampal CA1 was decreased after SNL. And this decrease paralleled the reduction of ubiquitinated-CPEB3 level and reduced production of GluA1 and GluA2. Overexpression of Neurl1 in the CA1 rescued cognitive deficits and LTP impairment, and reversed the reduction of ubiquitinated-CPEB3 level and the decrease of GluA1 and GluA2 production following SNL. Specific knockdown of Neurl1 or CPEB3 in bilateral hippocampal CA1 in naïve rats resulted in cognitive deficits and impairment of synaptic plasticity. The rescued cognitive function and synaptic plasticity by the treatment of overexpression of Neurl1 before SNL were counteracted by the knockdown of CPEB3 in the CA1. Collectively, the above results suggest that the downregulation of Neurl1 through reducing CPEB3 ubiquitination and, in turn, repressing GluA1 and GluA2 production and mediating synaptic plasticity impairment in hippocampal CA1 leads to the genesis of cognitive deficits in neuropathic pain.
Animals ; Male ; Neuralgia/metabolism* ; Rats ; Down-Regulation/physiology* ; Ubiquitination/physiology* ; Neuronal Plasticity/physiology* ; Rats, Sprague-Dawley ; CA1 Region, Hippocampal/metabolism* ; Cognitive Dysfunction/metabolism* ; RNA-Binding Proteins/metabolism* ; Receptors, AMPA/metabolism*

Background::The effect of bariatric surgery on type 2 diabetes mellitus (T2DM) control can be assessed based on predictive models of T2DM remission. Various models have been externally verified internationally. However, long-term validated results after laparoscopic sleeve gastrectomy (LSG) surgery are lacking. The best model for the Chinese population is also unknown.Methods::We retrospectively analyzed Chinese population data 5 years after LSG at Beijing Shijitan Hospital in China between March 2009 and December 2016. The independent t-test, Mann–Whitney U test, and chi-squared test were used to compare characteristics between T2DM remission and non-remission groups. We evaluated the predictive efficacy of each model for longterm T2DM remission after LSG by calculating the area under the curve (AUC), sensitivity, specificity, Youden index, positive predictive value (PPV), negative predictive value (NPV), and predicted-to-observed ratio, and performed calibration using Hosmer–Lemeshow test for 11 prediction models. Results::We enrolled 108 patients, including 44 (40.7%) men, with a mean age of 35.5 years. The mean body mass index was 40.3 ± 9.1 kg/m 2, the percentage of excess weight loss (%EWL) was (75.9 ± 30.4)%, and the percentage of total weight loss (% TWL) was (29.1 ± 10.6)%. The mean glycated hemoglobin A1c (HbA1c) level was (7.3 ± 1.8)% preoperatively and decreased to (5.9 ± 1.0)% 5 years after LSG. The 5-year postoperative complete and partial remission rates of T2DM were 50.9% [55/108] and 27.8% [30/108], respectively. Six models, i.e., "ABCD", individualized metabolic surgery (IMS), advanced-DiaRem, DiaBetter, Dixon et al’s regression model, and Panunzi et al’s regression model, showed a good discrimination ability (all AUC >0.8). The "ABCD" (sensitivity, 74%; specificity, 80%; AUC, 0.82 [95% confidence interval [CI]: 0.74–0.89]), IMS (sensitivity, 78%; specificity, 84%; AUC, 0.82 [95% CI: 0.73–0.89]), and Panunzi et al’s regression models (sensitivity, 78%; specificity, 91%; AUC, 0.86 [95% CI: 0.78–0.92]) showed good discernibility. In the Hosmer–Lemeshow goodness-of-fit test, except for DiaRem ( P <0.01), DiaBetter ( P <0.01), Hayes et al ( P = 0.03), Park et al ( P = 0.02), and Ramos-Levi et al’s ( P <0.01) models, all models had a satifactory fit results ( P >0.05). The P values of calibration results of the "ABCD" and IMS were 0.07 and 0.14, respectively. The predicted-to-observed ratios of the "ABCD" and IMS were 0.87 and 0.89, respectively. Conclusion::The prediction model IMS was recommended for clinical use because of excellent predictive performance, good statistical test results, and simple and practical design features.

Objective To explore the role and value of thromboelastography(TEG)combined with white matter hyperintensity(WMH)in predicting the functional prognosis of patients with non-cardiogenic acute ischemic stroke(AIS)through machine learning.Methods This study included 130 patients with non-cardiogenic AIS from August 2022 to February 2024.General clinical data,TEG and WMH information of all patients were collected.Three months later,functional outcomes were followed up using the modified Rankin scale(mRS),with an mRS score of≥2 indicating a poor prognosis.The prediction models were divided into four feature sets according to different ranges of predictors:set A(general clinical data+TEG indicators+WMH score),set B(general clinical data+TEG indicators),set C(general clinical data+WMH score),and set D(general clinical data).For each feature set,three machine learning algorithms,traditional logistic regression(LR)model,random forests(RF),neural network(NNET),and K-nearest neighbors(KNN),were used to construct models for predicting the 3-month neurological function outcome of patients with non-cardiogenic AIS.Bootstrap resampling internal validation was used to compare the performance of prediction models.Results The training and testing of the model were performed on 130 patient samples,and the AUC value and its confidence interval of the model were corrected by the 0.632+method(optimism correction).For the LR,NNET,and KNN models,the corrected AUC values of feature set A were significantly better than those of feature set D(DeLong test,P<0.05).For all models,the corrected AUC value of feature set A was higher than that of other feature sets.For feature set A,the corrected AUC value(0.830)of the NNET model was higher than that of other models.Among the 19 features of feature set A,six features with important associations with functional prognosis were selected including National Institute of Health stroke scale(NIHSS)score,stroke history,small artery occlusion subtype,periventricular white matter hyperintensities(PWMH)score,and TEG indicators maximum amplitude(MA)and LY30.Conclusion Combining TEG indicators and WMH information on the basis of general clinical data can significantly improve the accuracy of predicting poor functional prognosis in patients with non-cardiogenic AIS.The prediction models established by machine learning-based NNET and KNN algorithms have high predictive value.

Objective To explore the role and value of thromboelastography(TEG)combined with white matter hyperintensity(WMH)in predicting the functional prognosis of patients with non-cardiogenic acute ischemic stroke(AIS)through machine learning.Methods This study included 130 patients with non-cardiogenic AIS from August 2022 to February 2024.General clinical data,TEG and WMH information of all patients were collected.Three months later,functional outcomes were followed up using the modified Rankin scale(mRS),with an mRS score of≥2 indicating a poor prognosis.The prediction models were divided into four feature sets according to different ranges of predictors:set A(general clinical data+TEG indicators+WMH score),set B(general clinical data+TEG indicators),set C(general clinical data+WMH score),and set D(general clinical data).For each feature set,three machine learning algorithms,traditional logistic regression(LR)model,random forests(RF),neural network(NNET),and K-nearest neighbors(KNN),were used to construct models for predicting the 3-month neurological function outcome of patients with non-cardiogenic AIS.Bootstrap resampling internal validation was used to compare the performance of prediction models.Results The training and testing of the model were performed on 130 patient samples,and the AUC value and its confidence interval of the model were corrected by the 0.632+method(optimism correction).For the LR,NNET,and KNN models,the corrected AUC values of feature set A were significantly better than those of feature set D(DeLong test,P<0.05).For all models,the corrected AUC value of feature set A was higher than that of other feature sets.For feature set A,the corrected AUC value(0.830)of the NNET model was higher than that of other models.Among the 19 features of feature set A,six features with important associations with functional prognosis were selected including National Institute of Health stroke scale(NIHSS)score,stroke history,small artery occlusion subtype,periventricular white matter hyperintensities(PWMH)score,and TEG indicators maximum amplitude(MA)and LY30.Conclusion Combining TEG indicators and WMH information on the basis of general clinical data can significantly improve the accuracy of predicting poor functional prognosis in patients with non-cardiogenic AIS.The prediction models established by machine learning-based NNET and KNN algorithms have high predictive value.

Stigmasterol is a plant sterol with anti-apoptotic, anti-oxidative and anti-inflammatory effect through multiple mechanisms. In this study, we further assessed whether it exerts protective effect on human brain microvessel endothelial cells (HBMECs) against ischemia-reperfusion injury and explored the underlying mechanisms. HBMECs were used to establish an in vitro oxygen and glucose deprivation/reperfusion (OGD/R) model, while a middle cerebral artery occlusion (MCAO) model of rats were constructed. The interaction between stigmasterol and EPHA2 was detected by surface plasmon resonance (SPR) and cellular thermal shift assay (CETSA). The results showed that 10 μmol·L^-1 stigmasterol significantly protected cell viability, alleviated the loss of tight junction proteins and attenuated the blood-brain barrier (BBB) damage induced by OGD/R in thein vitro model. Subsequent molecular docking showed that stigmasterol might interact with EPHA2 at multiple sites, including T692, a critical gatekeep residue of this receptor. Exogenous ephrin-A1 (an EPHA2 ligand) exacerbated OGD/R-induced EPHA2 phosphorylation at S897, facilitated ZO-1/claudin-5 loss, and promoted BBB leakage in vitro, which were significantly attenuated after stigmasterol treatment. The rat MCAO model confirmed these protective effects in vivo. In summary, these findings suggest that stigmasterol protects HBMECs against ischemia-reperfusion injury by maintaining cell viability, reducing the loss of tight junction proteins, and attenuating the BBB damage. These protective effects are at least meditated by its interaction with EPHA2 and inhibitory effect on EPHA2 phosphorylation.
Humans ; Animals ; Rats ; Stigmasterol ; Phosphorylation ; Endothelial Cells ; Molecular Docking Simulation ; Reperfusion Injury ; Blood-Brain Barrier ; Glucose ; Microvessels ; Oxygen

Objective:To investigate the predictive value of plasma exosomal microRNA (miR)-124-3p in the risk of chronic cerebral hypoperfusion (CCH).Methods:A case-control study. Thirty patients who were diagnosed with CCH (CCH group) based on cranial artery spin labeling (ASL) in the neurology outpatient clinic of Sichuan Provincial People′s Hospital from March 2022 to June 2022 and 30 healthy volunteers (control group) were included. Age, gender, smoking history, alcohol consumption history, diabetes history, hypertension, hyperlipidemia history, uric acid, fasting blood glucose, homocysteine and plasma exosomal miR-124-3p expression level were compared between the two groups. Comparisons of categorical variables were analyzed by either χ2 test or Fisher′s exact test. If the data of continuous variables followed a normal distribution, they were expressed as mean±standard deviation (SD) and compared by t-test for two independent samples; otherwise, the data were expressed as M( Q1, Q3), and analyzed by Mann-Whitney U test for comparison between two groups. The correlation between cerebral blood flow and exosomal miR-124-3p levels was analyzed by Pearson′s correlation. Binary multifactorial logistic regression analysis was used to determine the risk factors associated with CCH, and corresponding odds ratios ( OR) and 95% confidence intervals ( CI) were calculated. P<0.05 was considered significant. Results:There was no significant difference in age (64±8 vs. 60±8 years old), gender (33.3% vs. 30.0%), history of smoking (20.0% vs. 3.3%), alcohol consumption (20.0% vs. 6.7%), diabetes mellitus (13.3% vs. 13.3%), hypertension (53.3% vs. 30.0%), history of hyperlipidemia (46.7% vs. 36.7%), uric acid (288±60 vs.319±67 μmol/L), and fasting glucose [4.99(4.63, 5.91) vs. 5.28(5.09, 6.05) mmol/L] and homocysteine [11.35(10.18, 13.08) vs.11.00(9.78, 13.03) μmol/L] between the CCH and control groups ( P>0.05). Plasma exosomal miR-124-3p expression was significantly higher in the CCH group than in the control group [13.08 (8.59, 21.55) vs. 2.85 (1.44, 5.10), respectively; U=169.50, P<0.001]. Pearson′s correlation test showed that the level of exosomal miR-124-3p was negatively correlated with cerebral blood flow in the hypoperfused region in patients with CCH ( r=-0.932, P<0.001). Multi-factor logistic regression analysis showed that plasma exosomal miR-124-3p was independently associated with the risk of CCH ( OR=1.169,95% CI 1.063-1.286, P=0.001). Conclusions:The expression of plasma exosomal miR-124-3p is negatively correlated with cerebral blood flow in areas of low perfusion and is an independent risk factor for CCH. Plasma exosomal miR-124-3p may thus serve as a valid biomarker for CCH risk prediction.

Abdomen/diagnostic imaging* ; Artificial Intelligence ; Body Composition ; Magnetic Resonance Imaging ; Practice Guidelines as Topic

Lipoxin A4 (LXA4) is one of the metabolites of arachidonic acid, and it is an endogenous anti-inflammatory factor that can alleviate the inflammatory reaction through various pathways. Inflammatory response plays an important role in the process of cerebral ischemia. LXA4 can play a protective role on nerve cells by regulating proinflammatory cytokines, protecting blood-brain barrier, inhibiting activation and infiltration of leukocyte, alleviating local microcirculation inflammatory response, regulating inflammatory mediators such as leukotrienes and inflammasome, regulating the metabolism of inflammatory related enzymes, and alleviating oxidative stress injury. This article reviews the anti-inflammatory effects of LXA4 in cerebral ischemia.