Growth arrest DNA damage-inducible protein 45 β (GADD45B) has been reported to be a regulatory factor for active DNA demethylation and is implicated in the modulation of synaptic plasticity and chronic stress-related psychopathological processes. However, its precise role and mechanism of action in stress susceptibility remain elusive. In this study, we found a significant reduction in GADD45B expression specifically in the ventral, but not the dorsal hippocampal CA1 (dCA1) of stress-susceptible mice. Furthermore, we demonstrated that GADD45B negatively regulates susceptibility to social stress and NMDA receptor-dependent long-term potentiation (LTP) in the ventral hippocampal CA1 (vCA1). Importantly, through pharmacological inhibition using the NMDA receptor antagonist MK801, we provided further evidence supporting the hypothesis that GADD45B potentially modulates susceptibility to social stress by influencing NMDA receptor-mediated LTP. Collectively, these results suggested that modulation of NMDA receptor-mediated synaptic plasticity is a pivotal mechanism underlying the regulation of susceptibility to social stress by GADD45B.
Animals ; Receptors, N-Methyl-D-Aspartate/antagonists & inhibitors* ; CA1 Region, Hippocampal/drug effects* ; Male ; Stress, Psychological/physiopathology* ; Mice ; Neuronal Plasticity/drug effects* ; Long-Term Potentiation/drug effects* ; Mice, Inbred C57BL ; Antigens, Differentiation/metabolism* ; Dizocilpine Maleate/pharmacology* ; Excitatory Amino Acid Antagonists/pharmacology* ; GADD45 Proteins

BACKGROUND:Secondary screw perforation of the articular surface is one of the major complications after locking plate internal fixation of proximal humerus fracture,and cut-out screws can damage shoulder function by abrading the glenoid and causing impingement of the acromion.Therefore,accurate risk prediction has positive clinical significance.OBJECTIVE:To screen risk factors for secondary screw perforation after proximal humerus fracture plating by machine learning methods,and to develop and validate a risk prediction model that facilitates clinicians to identify and intervene in high-risk patients at an early stage.METHODS:Clinical data of 214 patients with proximal humerus fractures who underwent locking plate internal fixation from June 2013 to June 2022 were collected as a training group to establish the model,and 61 similar patients from another hospital in the same period were included in the external validation group.The patients were divided into secondary screw perforation and screw maintenance groups according to whether they developed secondary screw perforation after surgery.The training group used three machine learning algorithms,namely,random forest,support vector machine,and logistic regression,to construct the prediction model.The recursive feature elimination method was used,and 10-fold cross-validation resampling was used as the screening method for the variables,and the intersection of the variables that were included when the accuracy of the three models was the highest was taken as the highly correlated with the secondary screw perforation reliable risk variables.The dynamic predictive model was constructed by R language software and presented as a web calculator,and the model was internally and externally validated.The internal test of the model was conducted by the Bootstrap method with 1 000 resamples,and the area under the receiver operating characteristic curve,the calibration curve,and the clinical decision curve were used to evaluate the differentiation,calibration ability,and clinical application value of the model.The Youden index was used to determine the optimal risk threshold of the prediction model,according to which the patients in the external validation group were divided into high-and low-risk groups,and the stability and extensibility of the model were evaluated according to the accuracy of its risk prediction ability.RESULTS AND CONCLUSION:(1)The machine learning algorithm identified four risk variables that were highly correlated with secondary screw perforation,namely cortical support of the proximal medial humeral column,deltoid tuberosity index,fracture type,and postoperative reduction.(2)The constructed risk prediction model showed good discrimination and accuracy[area under the curve=0.874,95％confidence interval(0.827,0.922)],and the calibration curve showed good agreement between the model predicted risk and the actual occurrence risk.(3)The clinical decision curve suggested that the model had good clinical applicability when the probability of the risk threshold was in the 0.1-0.75 range.(4)A risk probability of 26％was the optimal threshold for model risk stratification,and the external validation group used model risk stratification to predict secondary screw perforation with an overall accuracy rate of 84％.(5)The risk prediction model has good accuracy and extrapolation,and may provide a basis for guiding clinical treatment.

Aim To explore the key components and mechanisms of Lizhong decoction in treating rats with cold-damp diarrhea based on network pharmacology,molecular docking technology and animal experiments.Methods By literature review and database collec-tion,the components of Lizhong decoction,therapeutic targets,and the mapping with diarrhea disease targets were conducted to construct an intersection target pro-tein-protein interaction network for screening core tar-gets,and GO and KEGG pathway enrichment analysis was performed to build an"active component-target-pathway"network,followed by molecular docking vali-dation.Forty-eight rats were randomly divided into the normal control group(K),model group(DG),Lizhong decoction group(LZDG),and Pulsatilla decoction group(BTDG).Subsequently,a rat cold-damp diar-rhea model was established using Senna combined with low-temperature high-humidity environment,and the rats were intervened with Lizhong decoction and Pul-satilla decoction.HE staining was used to detect path-ological changes in intestinal tissue,ELISA was em-ployed to measure the levels of peripheral blood IL-6,IL-10,IL-1 β,and TNF-α,and western blot was used to determine the expression of colon tight junction pro-teins.Results Network pharmacology initially identi-fied 125 compounds in Lizhong decoction,5 186 drug target components,438 disease targets,and 60"drug-disease"shared targets.GO and KEGG enrichment a-nalysis showed that signaling pathways such as IL-17 and TNF were highly enriched.Molecular docking in-dicated that the core components of the drug had good binding activity with corresponding key targets.Liz-hong decoction could effectively improve the clinical symptoms of rats with cold-damp diarrhea,and com-pared with the DG group,the diarrhea rate,diarrhea in-dex,and other related indicators also gradually de-creased to normal levels.Compared with the DG group,the LZDG group showed reduced inflammation levels and a recovery in energy metabolism levels.Conclusion It can regulate targets such as MMP9 and IL-17 signaling pathways through multi-components like Calycosin and formononetin to exert its therapeutic effect on cold-damp diarrhea.

Aim To explore the key components and mechanisms of Lizhong decoction in treating rats with cold-damp diarrhea based on network pharmacology,molecular docking technology and animal experiments.Methods By literature review and database collec-tion,the components of Lizhong decoction,therapeutic targets,and the mapping with diarrhea disease targets were conducted to construct an intersection target pro-tein-protein interaction network for screening core tar-gets,and GO and KEGG pathway enrichment analysis was performed to build an"active component-target-pathway"network,followed by molecular docking vali-dation.Forty-eight rats were randomly divided into the normal control group(K),model group(DG),Lizhong decoction group(LZDG),and Pulsatilla decoction group(BTDG).Subsequently,a rat cold-damp diar-rhea model was established using Senna combined with low-temperature high-humidity environment,and the rats were intervened with Lizhong decoction and Pul-satilla decoction.HE staining was used to detect path-ological changes in intestinal tissue,ELISA was em-ployed to measure the levels of peripheral blood IL-6,IL-10,IL-1 β,and TNF-α,and western blot was used to determine the expression of colon tight junction pro-teins.Results Network pharmacology initially identi-fied 125 compounds in Lizhong decoction,5 186 drug target components,438 disease targets,and 60"drug-disease"shared targets.GO and KEGG enrichment a-nalysis showed that signaling pathways such as IL-17 and TNF were highly enriched.Molecular docking in-dicated that the core components of the drug had good binding activity with corresponding key targets.Liz-hong decoction could effectively improve the clinical symptoms of rats with cold-damp diarrhea,and com-pared with the DG group,the diarrhea rate,diarrhea in-dex,and other related indicators also gradually de-creased to normal levels.Compared with the DG group,the LZDG group showed reduced inflammation levels and a recovery in energy metabolism levels.Conclusion It can regulate targets such as MMP9 and IL-17 signaling pathways through multi-components like Calycosin and formononetin to exert its therapeutic effect on cold-damp diarrhea.

BACKGROUND:Secondary screw perforation of the articular surface is one of the major complications after locking plate internal fixation of proximal humerus fracture,and cut-out screws can damage shoulder function by abrading the glenoid and causing impingement of the acromion.Therefore,accurate risk prediction has positive clinical significance.OBJECTIVE:To screen risk factors for secondary screw perforation after proximal humerus fracture plating by machine learning methods,and to develop and validate a risk prediction model that facilitates clinicians to identify and intervene in high-risk patients at an early stage.METHODS:Clinical data of 214 patients with proximal humerus fractures who underwent locking plate internal fixation from June 2013 to June 2022 were collected as a training group to establish the model,and 61 similar patients from another hospital in the same period were included in the external validation group.The patients were divided into secondary screw perforation and screw maintenance groups according to whether they developed secondary screw perforation after surgery.The training group used three machine learning algorithms,namely,random forest,support vector machine,and logistic regression,to construct the prediction model.The recursive feature elimination method was used,and 10-fold cross-validation resampling was used as the screening method for the variables,and the intersection of the variables that were included when the accuracy of the three models was the highest was taken as the highly correlated with the secondary screw perforation reliable risk variables.The dynamic predictive model was constructed by R language software and presented as a web calculator,and the model was internally and externally validated.The internal test of the model was conducted by the Bootstrap method with 1 000 resamples,and the area under the receiver operating characteristic curve,the calibration curve,and the clinical decision curve were used to evaluate the differentiation,calibration ability,and clinical application value of the model.The Youden index was used to determine the optimal risk threshold of the prediction model,according to which the patients in the external validation group were divided into high-and low-risk groups,and the stability and extensibility of the model were evaluated according to the accuracy of its risk prediction ability.RESULTS AND CONCLUSION:(1)The machine learning algorithm identified four risk variables that were highly correlated with secondary screw perforation,namely cortical support of the proximal medial humeral column,deltoid tuberosity index,fracture type,and postoperative reduction.(2)The constructed risk prediction model showed good discrimination and accuracy[area under the curve=0.874,95％confidence interval(0.827,0.922)],and the calibration curve showed good agreement between the model predicted risk and the actual occurrence risk.(3)The clinical decision curve suggested that the model had good clinical applicability when the probability of the risk threshold was in the 0.1-0.75 range.(4)A risk probability of 26％was the optimal threshold for model risk stratification,and the external validation group used model risk stratification to predict secondary screw perforation with an overall accuracy rate of 84％.(5)The risk prediction model has good accuracy and extrapolation,and may provide a basis for guiding clinical treatment.

Objective:To investigate the relationship between mutated genes and clinical features in patients with essential thrombocythemia(ET).Methods:The clinical data of 69 patients with ET from October 2018 to March 2022 were retrospectively analyzed.According to driver mutation type,patients were divided into JAK2 group,CALR group and triple-negative group.The sex,age,cardiovascular risk factors,thrombosis,splenomegaly,routine blood test and coagulation status of patients in three groups were analyzed.Results:Among 69 ET patients,46 cases were associated with JAK2 mutation,14 cases with CALR mutation,8 cases with triple-negative mutation,and one with MPL gene mutation.There were no significant differences in age and sex among the three groups(P＞0.05).The highest thrombotic rate was 26.09％(12/46)in JAK2 group,then 12.5％(1/8)in triple-negative group,while no thrombotic events occurred in CALR group.The incidence of splenomegaly was the highest in JAK2 group(34.78％),while no splenomegaly occurred in triple-negative group.The white blood cell(WBC)count in JAK2 group was(9.00±4.86)× 109/L,which was significantly higher than(6.03±2.32)× 109/L in CALR group(P＜0.05).The hemoglobin(Hb)and hematocrit(HCT)in JAK2 group were(148.42±18.79)g/L and(0.44±0.06)％,respectively,which were both significantly higher than(131.00±15.17)g/L and(0.39±0.05)％in triple-negative group(P＜0.05).The platelet(PLT)in JAK2 group was(584.17±175.77)× 109/L,which was significantly lower than(703.07±225.60)× 109/L in CALR group(P＜0.05).The fibrinogen(Fg)in JAK2 and triple-negative group were(2.64±0.69)g/L and(3.05±0.77)g/L,respectively,which were both significantly higher than(2.24±0.47)g/L in CALR group(P＜0.05,P＜0.01).The activated partial thromboplastin time(APTT)in triple-negative group was(28.61±1.99)s,which was significantly decreased compared with(31.45±3.35)s in CALR group(P＜0.05).Conclusions:There are differences in blood cell count and coagulation status among ET patients with different driver gene mutations.Among ET patients,JAK2 mutation is most common.Compared with CALR group,the thrombotic rate,WBC and Fg significantly increase in JAK2 group,while PLT decrease.Compared with triple-negative group,the incidence of splenomegaly and HCT significantly increase.Compared with CALR group,Fg significantly increases but APTT decreases in triple-negative group.

Objective:To analyze the genes related to platelet activation in essential thrombocythemia (ET)based on transcriptome sequencing technology (RNA-seq ),and to explore the potential targets related to ET thrombosis. Methods:Blood samples from ET patients and healthy individuals were collected for RNA-seq,and differentially expressed lncRNAs,miRNAs,and mRNAs were selected to construct a lncRNA-miRNA-mRNA regulatory network. Differential mRNAs in the regulatory network were enriched and analyzed using Gene Ontology (GO ) and Kyoto Encyclopedia of Genes and Genomes (KEGG).The real-time PCR method was applied to validate differential mRNAs on crucial signaling pathways.Results:A total of 32 lncRNAs (3 up-regulated,29 down-regulated),16 miRNAs (8 up-regulated,8 down-regulated),and 35 mRNAs (27 up-regulated,8 down-regulated)were identified as differentially expressed.Among them,5 lncRNAs,12 miRNAs,and 19 mRNAs constituted the regulatory network.KEGG enrichment analysis showed that the differential mRNAs were related to the platelet activation signaling pathway,and there were 6 differential mRNAs related to platelet activation,namely F2R,ITGA2B,ITGB1,ITGB3,PTGS1,and GP1 BB,which were all up-regulated in their expression.RT-PCR results showed that the expression of five mRNAs including F2R,ITGA2B,ITGB1,ITGB3,and GP1BB were upregulated in ET patients compared with healthy subjects,and consistent with RNA-seq results,while PTGS1 expression was not significantly different.Conclusion:Differential mRNAs in ET patients are related to the platelet activation pathway,and F2R,ITGA2B,ITGB1,ITGB3,and GP1BB mRNAs may serve as novel targets associated with platelet activation in ET.

BACKGROUND:Proximal humeral fracture in older adults is one of the three major osteoporotic fractures.Anatomic locking plate fixation is the first choice for most scholars to treat difficult-to-reduce and complex fracture types.However,the probability of reduction failure after the operation is high,which seriously affects patients'quality of life. OBJECTIVE:To investigate the correlation between deltoid tuberosity index and postoperative reduction failure of proximal humeral fractures in the elderly,analyze and filter preoperative independent risk factors for reduction failure of proximal humeral fractures in the elderly,and construct and verify the effectiveness of a clinical prediction model. METHODS:The clinical data of 153 elderly patients with proximal humeral fractures who met the diagnosis and inclusion criteria and received open reduction and locking plate surgery in Foshan Hospital of TCM from June 2012 to June 2021 were collected.The patients were divided into the reduction failure subgroup and the reduction maintenance subgroup.The independent risk factors were selected by multivariate Logistic regression analysis,and the nomogram was constructed by R language.After 1000 times of resampling by Bootstrap method,the Hosmer-Lemeshow goodness of fit correlation test,receiver operating characteristic curve,calibration curve,clinical decision,and influence curve were plotted to evaluate its goodness of fit,discrimination,calibration ability,and clinical application value.Fifty-five elderly patients with proximal humeral fractures from June 2013 to August 2021 were selected as the model's external validation group to evaluate the prediction model's stability and accuracy. RESULTS AND CONCLUSION:(1)Of the 153 patients in the training group,44 patients met reduction failure after internal plate fixation.The prevalence of postoperative reduction failure was 28.8%.Multivariate Logistic regression analysis identified that deltoid tuberosity index[OR=9.782,95%CI(3.798,25.194)],varus displacement[OR=4.209,95%CI(1.472,12.031)],and medial metaphyseal comminution[OR=4.278,95%CI(1.670,10.959)]were independent risk factors for postoperative reduction failure of proximal humeral fractures in older adults(P<0.05).(2)A nomogram based on independent risk factors was then constructed.The Hosmer-Lemeshow test results for the model of the training group showed that χ2=0.812(P=0.976)and area under curve=0.830[95%CI(0.762,0.898)].The calibration plot results showed that the model's predicted risk was in good agreement with the actual risk.The decision and clinical influence curves showed good clinical applicability.(3)In the validation group,the accuracy rate in practical applications was 86%,area under curve=0.902[95%CI(0.819,0.985)].(4)It is concluded that deltoid tuberosity index<1.44,medial metaphyseal comminution,and varus displacement were independent risk factors for reduction failure.(5)The internal and external validation of the risk prediction model demonstrated high discrimination,accuracy,and clinical applicability could be used to individually predict and screen the high-risk population of postoperative reduction failure of proximal humeral fractures in the elderly.The predicted number of patients at high risk is highly matched to the actual number of patients who occur when the model's threshold risk probability is above 65%,and clinicians should use targeted treatment.

Objective:To investigate the prevalence of cataracts, the surgical coverage, and postoperative visual acuity of adults in Ningxia.Methods:A cross-sectional study using multistage cluster random sampling was conducted.Ten survey sites in Ningxia were selected and the population aged 18 years and over was surveyed with questionnaire, height and weight measurements, visual acuity, intraocular pressure, fundus photography and slit-lamp examinations.Cataract prevalence and its influencing factors were analyzed.Cataract prevalence, surgical coverage and presenting visual acuity (PVA) and best corrected visual acuity (BCVA) after surgery were investigated in different age groups of the examined population.The study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of the People's Hospital of Ningxia Hui Autonomous Region (No.[2023]-LL-010).Participants signed informed consent prior to the examination.Results:A total of 6 145 people should be examined, and 5 721 people were actually examined, with an examination rate of 93.10%.The study population consisted of 2 558 males, accounting for 44.71%, and 3 163 females, accounting for 55.28%, with ages ranging from 18 to 93 years old and an average age of (64.27±13.48) years.Among them, 1 180 patients diagnosed with cataract, with a cataract prevalence of 20.62%.The prevalence of cataract increased with age and decreased with education level, showing statistically significant differences ( χ2=1 091.32, 581.92; both at P<0.01).The prevalence of cataract was significantly higher among people with hypertension, diabetes mellitus, hyperlipidemia, and coronary heart disease than those without these diseases ( χ2=274.65, 118.15, 78.05, 182.71; all at P<0.01).Cataract surgery was performed in 245 cases in the cataract patient population, with a surgical coverage rate of 20.76%.Of the 245 cases, 229 cases were implanted with IOLs, with an implantation rate of 93.40%.The social burden rate of cataract blindness was 2.29%, and increased with age.Of the 339 eyes that underwent cataract surgery, 241 had a PVA≥0.3, accounting for 71.09%, and 272 had a BCVA≥0.3, accounting for 80.24%. Conclusions:In Ningxia, cataracts are still the main cause of vision impairment and blindness in the elderly, and the social burden rate of cataract blindness is high.Moreover, the coverage rate of cataract surgery is low, so both the coverage and quality of surgery need improvement.

BACKGROUND:Internal fixation and open reduction with locking plate is the main treatment for proximal humeral fractures with medial column instability.However,reduction failure is one of the main postoperative complications,and accurate risk factor assessment is beneficial for screening high-risk patients and clinical decision selection. OBJECTIVE:To construct four types of prediction models by different machine learning algorithms,compare the optimal model to analyze and sort the risk variables according to their weight scores on the impact of outcome,and explore their significance in guiding clinical diagnosis and treatment. METHODS:262 patients with proximal humeral fractures with medial column instability,aged(60.6±10.2)years,admitted to Foshan Hospital of Traditional Chinese Medicine between June 2012 and June 2022 were included.All patients underwent open reduction with locking plate surgery.According to the occurrence of reduction failure at 5-month follow-up,the patients were divided into a reduction failure group(n=64)and a reduction maintenance group(n=198).Clinical data of patients were collected,and model variables and their classification were determined.The data set was randomly divided into a training set and a test set according to a 7:3 ratio,and the optimal hyperparameters were obtained in the training set according to a 5-fold cross-over test.Four machine learning prediction models of logistic regression,random forest,support vector machine,and XGBoost were constructed,and the performance of different algorithms was observed in the test set using AUC,correctness,sensitivity,specificity,and F1 scores,so as to comprehensively evaluate the prediction performance of the models.The best-performing model was evaluated using SHAP to assess important risk variables and to evaluate its clinical guidance implications. RESULTS AND CONCLUSION:(1)There were significant differences between the two groups in deltoid tuberosity index,fracture type,fracture end with varus deformity before operation,fragment length of inferior metaphyseal of humerus,postoperative reduction,cortical support of medial column of proximal humerus,and insertion of calcar screw(P<0.05).(2)The best-combined performance of the four machine models was XGBoost.The AUC,accuracy,and F1 scores were 0.885,0.885,and 0.743,respectively;followed by random forest and support vector machine,with both models performing at approximately equal levels.Logistic regression had the worst combined performance.The SHAP interpretation tool was used in the optimal model and results showed that deltoid tuberosity index,medial humeral column cortical support,fracture type,fracture reduction quality,and the status of the calcar screw were important influencing fators for postoperative fracture reduction failure.(3)The accuracy of using machine learning to analyze clinical problems is superior to that of traditional logistic regression analysis methods.When dealing with high-dimensional data,the machine learning approach can solve multivariate interaction and covariance problems well.The SHAP interpretation tool can not only clarify the importance of individual variables but also obtain detailed information on the impact of dummy variables in each variable on the outcome.