Aim To investigate the impact of G pro-tein-coupled estrogen receptor 1(GPER1),also known as GPR30 playing a significant role in the nerv-ous system,on neuronal damage and cognitive dysfunc-tion following epileptic seizures.Methods The pro-tein expression levels of GPER1 and the DNA damage marker γ-H2AX in epileptic rats were assessed using Western blot.The hippocampal neuronal damage and apoptosis in pilocarpine-induced epilepsy models were evaluated using Nissl and TUNEL staining techniques,compared with GPER1 knockdown(GPER1-KD)rats with wild-type(WT)controls.The behavioral activi-ties,including memory and spatial learning,were mo-nitored during the chronic phase of epilepsy using the IntelliCage system.Results Compared to the control group,GPER1 protein expression in the cerebral cortex and hippocampus significantly increased 24 hours post-epilepsy onset.In the GPER1-KD+EP group,hipp-ocampal neuronal damage was more severe,with a sig-nificant increase in apoptotic neurons compared to the WT+EP group.The IntelliCage data revealed that during free exploration,nose contact,position learn-ing,and reverse position learning stages in the GPER1-KD+EP group exhibited fewer visits and a higher error rate than in the WT+EP group.Conclu-sions Deficiency in GPER1 impairs memory and spa-tial learning abilities following epilepsy,potentially due to exacerbated neuronal injury,apoptosis,and inflam-mation.GPER1 represents a promising therapeutic tar-get for mitigating post-epileptic nerve damage and cog-nitive impairment.

As an indispensable trace element in the human body,copper plays an important role in various physiological and biochemical reactions.The dyshomeostasis of copper leads to the disorder of copper metabolism and the occurrence of related diseases.Cuproptosis,a newly proposed regulatory cell death mode,is different from the known apoptosis,pyroptosis,necroptosis,and ferroptosis.Recent studies have found that the dyshomeostasis of copper has been observed in a variety of cancers.Therefore,targeting copper for disease treatment may become a new strategy and a new idea.This article systematically summarizes the fundamental properties of copper,copper dyshomeostasis-related diseases (Menkes syndrome,Wilson's disease,and cancer) and their treatment,and reviews the research progress in cuproptosis.
Humans ; Copper/metabolism* ; Homeostasis ; Neoplasms/metabolism* ; Hepatolenticular Degeneration/metabolism* ; Menkes Kinky Hair Syndrome/metabolism*

Objective:To assess the application value of one-hour post-load glucose (1hPG) for detecting prediabetes among individuals with high risk of type 2 diabetes mellitus (T2DM).Methods:The study was conducted between August 2023 and January 2024, and individuals with a high risk of T2DM were invited to receive an oral glucose tolerance test (OGTT), structural questionnaires, physical measurements, and other biochemical examinations. The fasting, one-, and two-hour glucose and insulin were tested. According to the 1hPG cut point on hyperglycemia suggested by International Diabetes Federation (IDF), normal glucose tolerance (NGT) and prediabetes were further divided into two subgroups, respectively, i.e., NGT with 1hPG<8.6 mmol/L (NGT-1hPG-normal), NGT with 1hPG≥8.6 mmol/L (NGT-1hPG-high), prediabetes with 1hPG<8.6 mmol/L (PDM-1hPG-normal), and prediabetes with 1hPG≥8.6 mmol/L (PDM-1hPG-high). The insulin release curve was drawn by the groups as above. Insulin resistance was evaluated by homeostasis model assessment for insulin resistance (HOMA-IR), and β-cell secretory function was evaluated by homeostasis model assessment for β cell function (HOMA-β)/HOMA-IR. Spearman rank correlation analysis was used to calculate the correlation coefficients among 1hPG, 2hPG and HOMA indices, and Steiger′s Z test was used to compare the difference between two correlation coefficients. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to assess the accuracy of 1hPG for detecting prediabetes. Results:A total of 2 469 subjects consisting of 1 485 men (60.1%) and 984 (39.9%) women, with a mean age of (45.76±6.20) years, of which 1 844 (74.7%) had 1hPG≥8.6 mmol/L. The prevalence of 1hPG≥8.6 mmol/L was 46.8%, 93.0% and 99.8% in individuals with NGT, prediabetes and newly diagnosed T2DM, respectively ( χ 2=763.78, P<0.001). The insulin release curve showed that insulin secretion increased rapidly in subjects with NGT-1hPG-high, and peaked at one hour, then decreased rapidly, with a significantly higher level of one- and two-hour insulin than those with NGT-1hPG-normal ( P<0.001). Compared to individuals with NGT-1hPG-normal, the counterparts with NGT-1hPG-high exhibited higher HOMA-IR and lower adjusted HOMA-β ( P<0.001). Spearman rank correlation analysis showed that the correlation coefficient of 1hPG with HOMA-IR was similar to the correlation coefficient of 2hPG with HOMA-IR (0.493 vs. 0.480, P=0.550), while the correlation of 1hPG with adjusted HOMA-β was significantly stronger than that of 2hPG (-0.692 vs. -0.587, P<0.001). Excluding patients with T2DM, according to the cut point recommended by IDF, the AUC of 1hPG≥8.6 mmol/L for detecting prediabetes was 0.731 (95% CI: 0.714-0.748), and the sensitivity and specificity were 0.930 and 0.532, respectively, with the kappa value of 0.45. Conclusion:1hPG is closely related to insulin resistance and islet function, and there′s substantial value for individuals with a high risk of T2DM to detect prediabetes by using the 1hPG cut points recommended by IDF.

Objective:To assess the application value of one-hour post-load glucose (1hPG) for detecting prediabetes among individuals with high risk of type 2 diabetes mellitus (T2DM).Methods:The study was conducted between August 2023 and January 2024, and individuals with a high risk of T2DM were invited to receive an oral glucose tolerance test (OGTT), structural questionnaires, physical measurements, and other biochemical examinations. The fasting, one-, and two-hour glucose and insulin were tested. According to the 1hPG cut point on hyperglycemia suggested by International Diabetes Federation (IDF), normal glucose tolerance (NGT) and prediabetes were further divided into two subgroups, respectively, i.e., NGT with 1hPG<8.6 mmol/L (NGT-1hPG-normal), NGT with 1hPG≥8.6 mmol/L (NGT-1hPG-high), prediabetes with 1hPG<8.6 mmol/L (PDM-1hPG-normal), and prediabetes with 1hPG≥8.6 mmol/L (PDM-1hPG-high). The insulin release curve was drawn by the groups as above. Insulin resistance was evaluated by homeostasis model assessment for insulin resistance (HOMA-IR), and β-cell secretory function was evaluated by homeostasis model assessment for β cell function (HOMA-β)/HOMA-IR. Spearman rank correlation analysis was used to calculate the correlation coefficients among 1hPG, 2hPG and HOMA indices, and Steiger′s Z test was used to compare the difference between two correlation coefficients. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to assess the accuracy of 1hPG for detecting prediabetes. Results:A total of 2 469 subjects consisting of 1 485 men (60.1%) and 984 (39.9%) women, with a mean age of (45.76±6.20) years, of which 1 844 (74.7%) had 1hPG≥8.6 mmol/L. The prevalence of 1hPG≥8.6 mmol/L was 46.8%, 93.0% and 99.8% in individuals with NGT, prediabetes and newly diagnosed T2DM, respectively ( χ 2=763.78, P<0.001). The insulin release curve showed that insulin secretion increased rapidly in subjects with NGT-1hPG-high, and peaked at one hour, then decreased rapidly, with a significantly higher level of one- and two-hour insulin than those with NGT-1hPG-normal ( P<0.001). Compared to individuals with NGT-1hPG-normal, the counterparts with NGT-1hPG-high exhibited higher HOMA-IR and lower adjusted HOMA-β ( P<0.001). Spearman rank correlation analysis showed that the correlation coefficient of 1hPG with HOMA-IR was similar to the correlation coefficient of 2hPG with HOMA-IR (0.493 vs. 0.480, P=0.550), while the correlation of 1hPG with adjusted HOMA-β was significantly stronger than that of 2hPG (-0.692 vs. -0.587, P<0.001). Excluding patients with T2DM, according to the cut point recommended by IDF, the AUC of 1hPG≥8.6 mmol/L for detecting prediabetes was 0.731 (95% CI: 0.714-0.748), and the sensitivity and specificity were 0.930 and 0.532, respectively, with the kappa value of 0.45. Conclusion:1hPG is closely related to insulin resistance and islet function, and there′s substantial value for individuals with a high risk of T2DM to detect prediabetes by using the 1hPG cut points recommended by IDF.

Aim To investigate the impact of G pro-tein-coupled estrogen receptor 1(GPER1),also known as GPR30 playing a significant role in the nerv-ous system,on neuronal damage and cognitive dysfunc-tion following epileptic seizures.Methods The pro-tein expression levels of GPER1 and the DNA damage marker γ-H2AX in epileptic rats were assessed using Western blot.The hippocampal neuronal damage and apoptosis in pilocarpine-induced epilepsy models were evaluated using Nissl and TUNEL staining techniques,compared with GPER1 knockdown(GPER1-KD)rats with wild-type(WT)controls.The behavioral activi-ties,including memory and spatial learning,were mo-nitored during the chronic phase of epilepsy using the IntelliCage system.Results Compared to the control group,GPER1 protein expression in the cerebral cortex and hippocampus significantly increased 24 hours post-epilepsy onset.In the GPER1-KD+EP group,hipp-ocampal neuronal damage was more severe,with a sig-nificant increase in apoptotic neurons compared to the WT+EP group.The IntelliCage data revealed that during free exploration,nose contact,position learn-ing,and reverse position learning stages in the GPER1-KD+EP group exhibited fewer visits and a higher error rate than in the WT+EP group.Conclu-sions Deficiency in GPER1 impairs memory and spa-tial learning abilities following epilepsy,potentially due to exacerbated neuronal injury,apoptosis,and inflam-mation.GPER1 represents a promising therapeutic tar-get for mitigating post-epileptic nerve damage and cog-nitive impairment.

Objective To construct radiomics-semantic models to predict the treatment resistance of chemoradiotherapy in brain gliomas based on MRI and clinical data of multicenter patients.Methods Among 2 108 brain gliomas patients from five medical institutions,132 patients had residual gliomas after surgery.The clinical risk factors and multimodal MRI were collected.All patients were divided into training set(n=95)and validation set(n=37).The treatment response of gliomas after standardized chemoradiotherapy were divided into resistant and non-resistant types.The semantic features of MRI were evaluated by two radiologists.Three different segmentation regions of interest(ROI)were delineated to extract radiomics features.And that three groups of radiomics models were con-structed based on different sequence MRIs.The radiomics model with the best predictive efficacy in each group was selected and combined with MRI semantic features,three radiomics-semantic models(combined models)were established.Finally,a MRI semantic model,three groups of radiomics models and three combined models were developed.Results Comparisons between the different models showed that the radiomics-semantic model based on pre-operative T2-fluid attenuated inversion recovery(FLAIR)sequence,had the best predictive efficacy,the area under the curve(AUC)in the training and validation sets were 0.866[95％confidence interval(CI)0.790-0.942]and 0.810(95％CI 0.667-0.952),respectively.The radiomics-semantic model based on postoperative T1 WI sequence performed the second best,with the AUC of the training and validation sets being 0.812(95％CI 0.726-0.898)and 0.711(95％CI 0.541-0.881),respectively.Conclusion The combined models based on MRI radiomics and semantic features are able to predict the treatment resistance of chemoradiotherapy in brain gliomas patients,and may be used as an important basis for optimizing treatment.

Objective To construct radiomics-semantic models to predict the treatment resistance of chemoradiotherapy in brain gliomas based on MRI and clinical data of multicenter patients.Methods Among 2 108 brain gliomas patients from five medical institutions,132 patients had residual gliomas after surgery.The clinical risk factors and multimodal MRI were collected.All patients were divided into training set(n=95)and validation set(n=37).The treatment response of gliomas after standardized chemoradiotherapy were divided into resistant and non-resistant types.The semantic features of MRI were evaluated by two radiologists.Three different segmentation regions of interest(ROI)were delineated to extract radiomics features.And that three groups of radiomics models were con-structed based on different sequence MRIs.The radiomics model with the best predictive efficacy in each group was selected and combined with MRI semantic features,three radiomics-semantic models(combined models)were established.Finally,a MRI semantic model,three groups of radiomics models and three combined models were developed.Results Comparisons between the different models showed that the radiomics-semantic model based on pre-operative T2-fluid attenuated inversion recovery(FLAIR)sequence,had the best predictive efficacy,the area under the curve(AUC)in the training and validation sets were 0.866[95％confidence interval(CI)0.790-0.942]and 0.810(95％CI 0.667-0.952),respectively.The radiomics-semantic model based on postoperative T1 WI sequence performed the second best,with the AUC of the training and validation sets being 0.812(95％CI 0.726-0.898)and 0.711(95％CI 0.541-0.881),respectively.Conclusion The combined models based on MRI radiomics and semantic features are able to predict the treatment resistance of chemoradiotherapy in brain gliomas patients,and may be used as an important basis for optimizing treatment.

Ferroptosis is a form of regulated cell death that depends on iron and reactive oxygen spe-cies.Different from apoptosis,necrosis,and autophagy,ferroptosis is characterized by the accumulation of lipid peroxides in cells.Studies have discovered that ferroptosis is closely associated with the occurrence and develop-ment of tumors and inducing ferroptosis in tumor cells can enhance the therapeutic effects of drugs on tumors.This article summarizes the latest research progress in ferroptosis regarding its mechanisms and associations with tumors,aiming to provide a reference for further understanding the interaction mechanisms between ferroptosis and tumors and offering new insights and targets for the treatment of tumors.

Purpose::Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries.Methods::This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q 1, Q 3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. Results::According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval ( CI): 2.08 -25.42, p = 0.002), 2.85 (95% CI: 1.11 -7.31, p = 0.030), 2.62 (95% CI: 1.12 -6.13, p = 0.027), 2.44 (95% CI: 1.25 -4.76, p = 0.009), and 1.5 (95% CI: 1.10 -2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ 2= 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ 2= 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. Conclusion::Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

The treatment strategy for blast injuries is closely linked to the clinical outcome of blast injury casualties. However, the application of military surgery experience to blast injuries caused by production safety accidents is relatively uncommon. In this study, the authors present 2 cases of blast injuries caused by one gas explosion, both cases involved individuals of the same age and gender and experienced similar degree of injury. The authors highlight the importance of using a military surgery treatment strategy, specifically emphasizing the need to understand the concept of damage control and disposal. It is recommended that relevant training in this area should be strengthened to improve the clinical treatment of such injuries. This study provides a valuable reference for healthcare professionals dealing with blast injuries.