BACKGROUND:The most common complication of traumatic femoral neck fractures after internal fixation is femoral head necrosis.Currently,many studies have reported on the risk factors that affect the occurrence and development of postoperative femoral head necrosis,but there is still a lack of tools to predict the risk of femoral head necrosis after internal fixation of femoral neck fractures.OBJECTIVE:To develop a predictive model that estimates the risk of femoral head necrosis shortly after patients with femoral neck fractures receive cannulated screw internal fixation.METHODS:A retrospective analysis reviewed clinical records of 172 patients who underwent cannulated screw internal fixation for femoral neck fractures at Department of Orthopedics of Mianyang Central Hospital from January 2013 to June 2023.Patients were categorized into two groups based on the presence or absence of femoral head necrosis within one year post-operation:the necrosis group and the non-necrosis group.Univariate analysis,Lasso regression,and multivariate Logistic regression techniques were employed to identify the determinants of femoral head necrosis.A nomogram prediction model was constructed using R language's"rms"package,version 4.0.The receiver operating characteristic curve was used to evaluate the discriminatory ability of the model.The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model,and the decision curve analysis was used to determine its clinical application benefits.Internal validation of the study was conducted using the Bootstrap method,involving 1 000 repeated samplings.To delve deeper into the primary factors influencing femoral head necrosis post-internal fixation of the femoral neck,this paper employed the SHAP method for data set analysis.RESULTS AND CONCLUSION:(1)The risk factors leading to femoral head necrosis in the short term after cannulated screw fixation of femoral neck fractures include:smoking,diabetes,Garden classification,fracture line location,reduction quality,age,and operation time.(2)The prediction model demonstrated robust performance,evidenced by an area under the curve of 0.940(95％Confidence Interval:0.903 to 0.977),indicating a high level of prediction accuracy.The model achieved a sensitivity of 90.2％and a specificity of 87.6％,indicating that its diagnostic performance was stable.The Hosmer-Lemeshow goodness-of-fit test yielded a chi-square value of 6.593 with a P-value of 0.581,confirming that the model's predictions closely align with the observed outcomes.(3)The calibration curve of the model also performed well,and its overall trend was very close to the ideal curve,further proving the high accuracy of the model.(4)The internal validation was carried out by the Bootstrap method with 1 000 repeated samplings,and the area under the curve of the model internal validation was still as high as 0.939,proving that the model had good stability.(5)Through the decision curve,it is found that within the probability threshold range of 1％to 92％,the model can obtain the maximum net benefit value.(6)The SHAP analysis results show that among the risk factors analyzed in this study,the location of the fracture line serves as the most significant predictor of femoral head necrosis following internal fixation with cannulated screws in femoral neck fractures,and subcapital fractures are extremely prone to femoral head necrosis after surgery.(7)It is concluded that the validated prediction model demonstrates strong discriminative power and reliability,offering practical clinical utility.It serves as a useful reference tool for short-term risk assessment of femoral head necrosis following internal fixation of femoral neck fractures.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

BACKGROUND:The quadriceps strength of patients with osteoporosis and sarcopenia is significantly reduced,which can further reduce the function of the knee joint,affect the function of the lower limbs and even lead to a decrease in whole-body coordination.It is speculated that a reasonable quadriceps training program and personalized guidance are beneficial to the recovery of knee joint function in patients with osteoporosis and sarcopenia.OBJECTIVE:To observe the effect of short-term moderate-intensity resistance rehabilitation training on the mass and function of the quadriceps and knee joint function in patients with osteoporosis and sarcopenia.METHODS:Using the integrated physical examination and rehabilitation model,375 patients with osteoporosis and sarcopenia were screened at the Health Management Center of Shanghai Public Health Clinical Center.They underwent 12 weeks of combined/comprehensive exercise rehabilitation based on resistance exercise,including quadriceps resistance isotonic and isometric contraction training twice a week(3-5 sets each time,10-15 minutes per set)and aerobic exercise/balance exercise two or three times a week(30 minutes each time).Assessments and data collection were performed before rehabilitation training,12 weeks after rehabilitation training,and at follow-up 12 weeks after stopping rehabilitation training,mainly including knee joint range of motion and proprioception,quadriceps muscle strength,and cross-sectional area(magnetic resonance imaging results),pain,knee joint function(Hospital for Special Surgery score)and walking function("up-and-go"time and 6 m pace test results)as well as the patient's psychological status assessment.RESULTS AND CONCLUSION:All 375 patients completed 12 weeks of rehabilitation training and 12 weeks of follow-up without any adverse events.(1)Compared with before training,the patients' gait speed and knee range of motion increased significantly after 12 weeks of rehabilitation training(P＜0.01),the time of"stand-to-walk"decreased(P＜0.01),and the proprioception of the knee joint and the strength of the quadriceps femoris were significantly improved(P＜0.01);and at the follow-up visit 12 weeks after stopping training,the above indicators and functions of the patients were well maintained(P＞0.05).(2)Magnetic resonance imaging results showed that the effective cross-sectional area of the quadriceps femoris did not improve significantly after 12 weeks of rehabilitation training(P＞0.05);but the Hospital for Special Surgery score of knee joint function increased significantly(P＜0.01),and the visual analog pain scale score decreased significantly(P＜0.01),suggesting that this may be related to the improvement of quadriceps femoris quality by resistance rehabilitation training.(3)The results of the Hospital Anxiety and Depression Scale score showed that the anxiety and depression scores of the patients continued to decrease,both at 12 weeks of rehabilitation training and at 12 weeks after stopping training(P＜0.01).It is suggested that resistance rehabilitation training of the quadriceps can help patients with osteoporosis and sarcopenia to restore quadriceps muscle strength,increase range of motion,improve proprioception and joint stability,thereby enhancing knee joint function,reducing pain,improving depression and anxiety,and to a certain extent promoting the coordinated recovery of the musculoskeletal system.

BACKGROUND:Stress-induced damage to hippocampal neurons may underlie abnormalities in neuronal structure and function,ultimately leading to mood disorders.G protein-coupled receptors in brain tissue play an important role in mood regulation.OBJECTIVE:To analyze the mechanism of depression-like behavior in chronic social defeat stress mice based on high-throughput sequencing and bioinformatics analysis.METHODS:C57BL/6J mice were randomly divided into control group and model group.There was no special treatment in the control group,while a mouse model of chronic social defeat stress was established in the model group.Depression-like behavior was assessed through the sucrose preference test,tail suspension test,and forced swim test.Anxiety behavior was evaluated using the elevated plus-maze,while social behavior was measured through the social interaction test.Cognitive function was assessed with the Y-maze spontaneous alternation test.Immunofluorescence staining was performed to quantify microglia markers in the mouse hippocampus,and Nissl staining was used to examine neuronal damage in mice.High-throughput sequencing was used to identify differentially expressed genes and gene enrichment in the mouse hippocampus,and qPCR was used to measure the expression of G protein-coupled receptors in the mouse hippocampus.RESULTS AND CONCLUSION:(1)Compared with the control group,chronic social defeat stress mice showed significant behavioral impairments,including increased anxiety,depression,and cognitive deficits.(2)Additionally,the Nissl body light density in hippocampal neurons was significantly reduced in chronic social defeat stress mice.(3)Sequencing results revealed synaptic damage in the neurons after chronic social defeat stress.Microglia activation was also markedly increased in the hippocampus of CSDS mice.Furthermore,the expression of G protein-coupled receptors in the hippocampus was significantly higher in chronic social defeat stress mice compared with the control group.These findings suggest that chronic social defeat stress induces anxiety,depression,and cognitive deficits in mice,accompanied by neuropathological changes in the hippocampus,and that altered G protein-coupled receptors expression may play a key role in these behavioral and neuropathological changes.

BACKGROUND:Promoting skin wound healing is a huge challenge facing global public health.To promote faster and higher-quality wound healing,it is necessary to explore more advantageous dressings to address this problem.OBJECTIVE:To investigate the hemostatic properties of acellular dermal matrix hydrogel and its effect on skin wound healing.METHODS:(1)Acellular dermal matrix hydrogel was prepared,and the differences in microscopic morphology and main components between it and acellular dermal matrix were analyzed.(2)Acellular dermal matrix hydrogel and chitosan hydrogel were used to cover the femoral artery puncture site of rats,and the bleeding quality and coagulation time were recorded.Acellular dermal matrix hydrogel and chitosan hydrogel were mixed with rat anticoagulated blood,and the coagulation index within 30 minutes was detected.(3)A full-thickness skin defect model with a diameter of 12 mm was made on the back of 18 SD rats,and they were randomly divided into 3 groups,with 6 rats in each group:the model group used PBS to clean the wound,and the control group and the experimental group used chitosan hydrogel and acellular dermal matrix hydrogel to cover the wound,respectively.The hydrogel dressing was changed every day,and the treatment was continued for 14 days,and the wound healing was observed.On day 3 after modeling,immunofluorescence staining of inducible nitric oxide synthase(M1 macrophages)and CD206(M2 macrophages)was performed on the wound surface.On day 14 after modeling,hematoxylin-eosin staining,Masson staining,and CD31 immunohistochemical staining were performed on the wound surface.RESULTS AND CONCLUSION:(1)Scanning electron microscopy revealed that the acellular dermal matrix hydrogel had a porous structure,and the Fourier transform infrared spectrum showed that it had the same main components as the acellular dermal matrix.(2)Both acellular dermal matrix hydrogel and chitosan hydrogel had obvious hemostatic ability in vivo.In the in vitro coagulation experiments,the coagulation index of acellular dermal matrix hydrogel was significantly higher than that of chitosan hydrogel.(3)In the rat skin full-thickness defect model,both acellular dermal matrix hydrogel and chitosan hydrogel could improve the wound healing rate.Hematoxylin-eosin and Masson staining results showed that acellular dermal matrix hydrogel could reduce the infiltration of inflammatory cells in the center of the wound.Both acellular dermal matrix hydrogel and chitosan hydrogel could decrease scar width and increase collagen deposition rate.CD31 immunohistochemical staining results showed that both hydrogels could promote angiogenesis in the wound site.Immunofluorescence staining results showed that both hydrogels could reduce the proportion of M1 macrophages and increase the proportion of M2 macrophages,and the effect of acellular dermal matrix hydrogel was stronger than that of chitosan hydrogel.(4)The results show that the acellular dermal matrix hydrogel has good hemostatic properties and the ability to promote wound healing.

BACKGROUND:Spondylolisthesis is a common disease,and there is a lack of effective drugs to treat it.There is still a need to further define the pathogenesis and screen out more suitable therapeutic targets for spondylolisthesis.Mendelian randomization analysis can be used to explore the drugable genes associated with spondylolisthesis and provide valuable guidance for the development of more effective and targeted therapeutic drugs.OBJECTIVE:To explore potential therapeutic targets and effective drugs for spondylolisthesis by means of pharmaceutically available genome-wide Mendelian randomization analysis.METHODS:Using the Finnish database,eQTLGen consortium,drug signature database,drug-gene interaction database,protein-protein interaction database,organic small molecule biological activity database and protein structure database,which contains genome and health information of half a million Finns,data on druggable genes were subjected to two-sample Mendelian randomization analysis and co-localization analysis with data from genome-wide association studies of spondylolisthesis to identify genes highly associated with spondylolisthesis.In addition,GO and KEGG enrichment analysis,protein network construction,drug prediction and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic agents.RESULTS AND CONCLUSION:In this study,we identified 34 potential drug target genes that were significantly associated with spondylolisthesis,particularly the gene APOBEC3G.This gene showed a significant association with spondylolisthesis outcomes through Mendelian analysis and co-localization analysis,suggesting that APOBEC3G may be a priority therapeutic target.As for other potential mechanisms and drugs,we still need to conduct more in-depth research to determine their roles.This study used a database from a European population,which can be used as a reference for the study of population genetics in China.

BACKGROUND:The most common complication of traumatic femoral neck fractures after internal fixation is femoral head necrosis.Currently,many studies have reported on the risk factors that affect the occurrence and development of postoperative femoral head necrosis,but there is still a lack of tools to predict the risk of femoral head necrosis after internal fixation of femoral neck fractures.OBJECTIVE:To develop a predictive model that estimates the risk of femoral head necrosis shortly after patients with femoral neck fractures receive cannulated screw internal fixation.METHODS:A retrospective analysis reviewed clinical records of 172 patients who underwent cannulated screw internal fixation for femoral neck fractures at Department of Orthopedics of Mianyang Central Hospital from January 2013 to June 2023.Patients were categorized into two groups based on the presence or absence of femoral head necrosis within one year post-operation:the necrosis group and the non-necrosis group.Univariate analysis,Lasso regression,and multivariate Logistic regression techniques were employed to identify the determinants of femoral head necrosis.A nomogram prediction model was constructed using R language's"rms"package,version 4.0.The receiver operating characteristic curve was used to evaluate the discriminatory ability of the model.The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model,and the decision curve analysis was used to determine its clinical application benefits.Internal validation of the study was conducted using the Bootstrap method,involving 1 000 repeated samplings.To delve deeper into the primary factors influencing femoral head necrosis post-internal fixation of the femoral neck,this paper employed the SHAP method for data set analysis.RESULTS AND CONCLUSION:(1)The risk factors leading to femoral head necrosis in the short term after cannulated screw fixation of femoral neck fractures include:smoking,diabetes,Garden classification,fracture line location,reduction quality,age,and operation time.(2)The prediction model demonstrated robust performance,evidenced by an area under the curve of 0.940(95％Confidence Interval:0.903 to 0.977),indicating a high level of prediction accuracy.The model achieved a sensitivity of 90.2％and a specificity of 87.6％,indicating that its diagnostic performance was stable.The Hosmer-Lemeshow goodness-of-fit test yielded a chi-square value of 6.593 with a P-value of 0.581,confirming that the model's predictions closely align with the observed outcomes.(3)The calibration curve of the model also performed well,and its overall trend was very close to the ideal curve,further proving the high accuracy of the model.(4)The internal validation was carried out by the Bootstrap method with 1 000 repeated samplings,and the area under the curve of the model internal validation was still as high as 0.939,proving that the model had good stability.(5)Through the decision curve,it is found that within the probability threshold range of 1％to 92％,the model can obtain the maximum net benefit value.(6)The SHAP analysis results show that among the risk factors analyzed in this study,the location of the fracture line serves as the most significant predictor of femoral head necrosis following internal fixation with cannulated screws in femoral neck fractures,and subcapital fractures are extremely prone to femoral head necrosis after surgery.(7)It is concluded that the validated prediction model demonstrates strong discriminative power and reliability,offering practical clinical utility.It serves as a useful reference tool for short-term risk assessment of femoral head necrosis following internal fixation of femoral neck fractures.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

BACKGROUND:The quadriceps strength of patients with osteoporosis and sarcopenia is significantly reduced,which can further reduce the function of the knee joint,affect the function of the lower limbs and even lead to a decrease in whole-body coordination.It is speculated that a reasonable quadriceps training program and personalized guidance are beneficial to the recovery of knee joint function in patients with osteoporosis and sarcopenia.OBJECTIVE:To observe the effect of short-term moderate-intensity resistance rehabilitation training on the mass and function of the quadriceps and knee joint function in patients with osteoporosis and sarcopenia.METHODS:Using the integrated physical examination and rehabilitation model,375 patients with osteoporosis and sarcopenia were screened at the Health Management Center of Shanghai Public Health Clinical Center.They underwent 12 weeks of combined/comprehensive exercise rehabilitation based on resistance exercise,including quadriceps resistance isotonic and isometric contraction training twice a week(3-5 sets each time,10-15 minutes per set)and aerobic exercise/balance exercise two or three times a week(30 minutes each time).Assessments and data collection were performed before rehabilitation training,12 weeks after rehabilitation training,and at follow-up 12 weeks after stopping rehabilitation training,mainly including knee joint range of motion and proprioception,quadriceps muscle strength,and cross-sectional area(magnetic resonance imaging results),pain,knee joint function(Hospital for Special Surgery score)and walking function("up-and-go"time and 6 m pace test results)as well as the patient's psychological status assessment.RESULTS AND CONCLUSION:All 375 patients completed 12 weeks of rehabilitation training and 12 weeks of follow-up without any adverse events.(1)Compared with before training,the patients' gait speed and knee range of motion increased significantly after 12 weeks of rehabilitation training(P＜0.01),the time of"stand-to-walk"decreased(P＜0.01),and the proprioception of the knee joint and the strength of the quadriceps femoris were significantly improved(P＜0.01);and at the follow-up visit 12 weeks after stopping training,the above indicators and functions of the patients were well maintained(P＞0.05).(2)Magnetic resonance imaging results showed that the effective cross-sectional area of the quadriceps femoris did not improve significantly after 12 weeks of rehabilitation training(P＞0.05);but the Hospital for Special Surgery score of knee joint function increased significantly(P＜0.01),and the visual analog pain scale score decreased significantly(P＜0.01),suggesting that this may be related to the improvement of quadriceps femoris quality by resistance rehabilitation training.(3)The results of the Hospital Anxiety and Depression Scale score showed that the anxiety and depression scores of the patients continued to decrease,both at 12 weeks of rehabilitation training and at 12 weeks after stopping training(P＜0.01).It is suggested that resistance rehabilitation training of the quadriceps can help patients with osteoporosis and sarcopenia to restore quadriceps muscle strength,increase range of motion,improve proprioception and joint stability,thereby enhancing knee joint function,reducing pain,improving depression and anxiety,and to a certain extent promoting the coordinated recovery of the musculoskeletal system.

BACKGROUND:Stress-induced damage to hippocampal neurons may underlie abnormalities in neuronal structure and function,ultimately leading to mood disorders.G protein-coupled receptors in brain tissue play an important role in mood regulation.OBJECTIVE:To analyze the mechanism of depression-like behavior in chronic social defeat stress mice based on high-throughput sequencing and bioinformatics analysis.METHODS:C57BL/6J mice were randomly divided into control group and model group.There was no special treatment in the control group,while a mouse model of chronic social defeat stress was established in the model group.Depression-like behavior was assessed through the sucrose preference test,tail suspension test,and forced swim test.Anxiety behavior was evaluated using the elevated plus-maze,while social behavior was measured through the social interaction test.Cognitive function was assessed with the Y-maze spontaneous alternation test.Immunofluorescence staining was performed to quantify microglia markers in the mouse hippocampus,and Nissl staining was used to examine neuronal damage in mice.High-throughput sequencing was used to identify differentially expressed genes and gene enrichment in the mouse hippocampus,and qPCR was used to measure the expression of G protein-coupled receptors in the mouse hippocampus.RESULTS AND CONCLUSION:(1)Compared with the control group,chronic social defeat stress mice showed significant behavioral impairments,including increased anxiety,depression,and cognitive deficits.(2)Additionally,the Nissl body light density in hippocampal neurons was significantly reduced in chronic social defeat stress mice.(3)Sequencing results revealed synaptic damage in the neurons after chronic social defeat stress.Microglia activation was also markedly increased in the hippocampus of CSDS mice.Furthermore,the expression of G protein-coupled receptors in the hippocampus was significantly higher in chronic social defeat stress mice compared with the control group.These findings suggest that chronic social defeat stress induces anxiety,depression,and cognitive deficits in mice,accompanied by neuropathological changes in the hippocampus,and that altered G protein-coupled receptors expression may play a key role in these behavioral and neuropathological changes.