BACKGROUND:Peripheral nerve axon rupture seriously affects patients' physical function and mental health.Microsurgery,nerve autograft,nerve allograft,fibrin glue and catheter technology are the main treatments for peripheral nerve injury,each of which has its own advantages and disadvantages,but the overall treatment effect is not satisfactory.Despite the clinical success of Schwann cells in promoting axonal regeneration,there are still many challenges in the treatment with Schwann cells,such as slow expansion of Schwann cells,immune rejection,and low survival rate of transplanted cells.OBJECTIVE:To summarize the role and mechanism of Schwann cells in promoting the regeneration of peripheral nerve axons,and the difficulties and challenges of Schwann cells in the process of nerve regeneration treatment.METHODS:PubMed,Medline,WanFang,VIP,and CNKI were searched by computer using the search terms of"Schwann cells,synaptic Schwann cell,macrophage,peripheral nerve axon rupture,Wallerian degeneration,Peripheral nerve axon regeneration,Central nervous system repair"in English and Chinese.Literature related to Schwann cell proliferation and differentiation,promotion of peripheral nerve regeneration,and clinical applications was retrieved from database inception to October 2024,and a total of 95 articles were finally included for review.RESULTS AND CONCLUSION:Schwann cells interact with macrophages,T cells and other cells,to initiate the regeneration process through signaling pathways,including Krox20/C-Jun,NRG-1/ErbB,Notch,MAPK,and PI3K/Akt/mTOR,synthesize and release nerve growth factors,and thus promote regeneration of the peripheral nervous system.Schwann cells have been experimentally demonstrated to have great potential in peripheral nerve repair and are expected to become the key target of therapeutic intervention.However,there are still problems such as difficulties in cell harvest and culture,as well as the occurrence of other diseases during the treatment process.

Esophageal squamous cell carcinoma is the main histological type of esophageal cancer in China, which seriously threatens the health of people. The application of immunotherapy, mainly immune checkpoint inhibitors, has greatly improved the prognosis of patients with esophageal squamous cell carcinoma, but the efficacy of treatment is still limited. Tertiary lymphoid structure (TLS) is an ectopic organized lymphoid structure that accumulates in non-lymphoid organs. Previous studies have found that TLS in esophageal squamous cell carcinoma is associated with better patient outcomes and enhanced immunotherapy efficacy. Based on current researches about TLS in esophageal squamous cell carcinoma, this paper reviews the relationship between TLS and the prognosis and immunotherapy of patients. We hope to provide reference for the precise immunotherapy of esophageal squamous cell carcinoma.

BACKGROUND:Peripheral nerve axon rupture seriously affects patients' physical function and mental health.Microsurgery,nerve autograft,nerve allograft,fibrin glue and catheter technology are the main treatments for peripheral nerve injury,each of which has its own advantages and disadvantages,but the overall treatment effect is not satisfactory.Despite the clinical success of Schwann cells in promoting axonal regeneration,there are still many challenges in the treatment with Schwann cells,such as slow expansion of Schwann cells,immune rejection,and low survival rate of transplanted cells.OBJECTIVE:To summarize the role and mechanism of Schwann cells in promoting the regeneration of peripheral nerve axons,and the difficulties and challenges of Schwann cells in the process of nerve regeneration treatment.METHODS:PubMed,Medline,WanFang,VIP,and CNKI were searched by computer using the search terms of"Schwann cells,synaptic Schwann cell,macrophage,peripheral nerve axon rupture,Wallerian degeneration,Peripheral nerve axon regeneration,Central nervous system repair"in English and Chinese.Literature related to Schwann cell proliferation and differentiation,promotion of peripheral nerve regeneration,and clinical applications was retrieved from database inception to October 2024,and a total of 95 articles were finally included for review.RESULTS AND CONCLUSION:Schwann cells interact with macrophages,T cells and other cells,to initiate the regeneration process through signaling pathways,including Krox20/C-Jun,NRG-1/ErbB,Notch,MAPK,and PI3K/Akt/mTOR,synthesize and release nerve growth factors,and thus promote regeneration of the peripheral nervous system.Schwann cells have been experimentally demonstrated to have great potential in peripheral nerve repair and are expected to become the key target of therapeutic intervention.However,there are still problems such as difficulties in cell harvest and culture,as well as the occurrence of other diseases during the treatment process.

Objective:To explore the relationship between life events, anxiety, depression, and behavior problems in adolescents.Methods:From September to October 2022, the cluster sampling method was used to select 5 341 adolescents from 4 middle schools in Xinxiang urban area.The subjects and their parents were investigated by the adolescent self-rating life events check list (ASLEC), generalized anxiety disorder scale (GAD-7), patient health questionnaire (PHQ-9), and child behavior checklist (CBCL). SPSS 27.0 software was used for Spearman correlation analysis, and AMOS 28.0 software was used to construct the structural equation model.Results:The scores of anxiety, depression, and behavioral problems were 1 (0, 4), 1 (0, 4), and 3 (0, 10). The total score of life events was 5 (1, 13), and the dimensions scored as follows: interpersonal conflict 1 (0, 4), academic pressure 2 (0, 5), punishment 0 (0, 2), loss 0 (0, 0), health and adaptation problem 0 (0, 1), and others 0 (0, 2). There were positive correlations between life events and its dimensions, depression, anxiety and behavioral problems ( r=0.28-0.69, all P<0.01). In the overall population, anxiety and depression played parallel mediating roles in the impact of life events on behavior problems. Life events could positively predict anxiety ( β=0.68, P<0.01), and anxiety could positively predict behavior problems ( β=0.04, P=0.02). Life events could positively predict depression ( β=0.77, P<0.01), and depression could positively predict behavior problems ( β=0.18, P<0.01). The standardized total effect size of the impact of life events on behavioral problems was 0.622 (95% CI=0.564-0.675). The standardized direct effect size and indirect effect size were 0.460 (95% CI=0.374-0.539) and 0.162 (95% CI=0.108-0.218), accounting for 74.0% and 26.0%of the total effect, respectively. After stratification by gender, the results for male adolescents were consistent with the overall population, while the mediating effect of anxiety was not significant in the female adolescents. Conclusion:Life events can lead to anxiety and depression in adolescents, thereby increasing the risk of behavior problems.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective:To explore the relationship between life events, anxiety, depression, and behavior problems in adolescents.Methods:From September to October 2022, the cluster sampling method was used to select 5 341 adolescents from 4 middle schools in Xinxiang urban area.The subjects and their parents were investigated by the adolescent self-rating life events check list (ASLEC), generalized anxiety disorder scale (GAD-7), patient health questionnaire (PHQ-9), and child behavior checklist (CBCL). SPSS 27.0 software was used for Spearman correlation analysis, and AMOS 28.0 software was used to construct the structural equation model.Results:The scores of anxiety, depression, and behavioral problems were 1 (0, 4), 1 (0, 4), and 3 (0, 10). The total score of life events was 5 (1, 13), and the dimensions scored as follows: interpersonal conflict 1 (0, 4), academic pressure 2 (0, 5), punishment 0 (0, 2), loss 0 (0, 0), health and adaptation problem 0 (0, 1), and others 0 (0, 2). There were positive correlations between life events and its dimensions, depression, anxiety and behavioral problems ( r=0.28-0.69, all P<0.01). In the overall population, anxiety and depression played parallel mediating roles in the impact of life events on behavior problems. Life events could positively predict anxiety ( β=0.68, P<0.01), and anxiety could positively predict behavior problems ( β=0.04, P=0.02). Life events could positively predict depression ( β=0.77, P<0.01), and depression could positively predict behavior problems ( β=0.18, P<0.01). The standardized total effect size of the impact of life events on behavioral problems was 0.622 (95% CI=0.564-0.675). The standardized direct effect size and indirect effect size were 0.460 (95% CI=0.374-0.539) and 0.162 (95% CI=0.108-0.218), accounting for 74.0% and 26.0%of the total effect, respectively. After stratification by gender, the results for male adolescents were consistent with the overall population, while the mediating effect of anxiety was not significant in the female adolescents. Conclusion:Life events can lead to anxiety and depression in adolescents, thereby increasing the risk of behavior problems.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective:To evaluate the efficacy of drug-coated balloons (DCB) in hemodialysis patients with peripheral artery disease (PAD) involving infrapopliteal lesions.Methods:A retrospective analysis was conducted on 53 hemodialysis patients (56 limbs, 66 lesions) with infrapopliteal PAD who underwent DCB treatment between Dec 2018 and Dec 2021. The primary outcome was improvement in Rutherford classification, while secondary outcomes included target lesion revascularization (TLR) and wound healing rate. Safety endpoints were all-cause mortality, amputation-free survival, and amputation rate.Results:The mean lesion length was (145.2±78.4) mm, and 87.5% of patients were of Rutherford grade ≥4. The median follow-up period was 14 months. Rutherford classification significantly improved at 3 and 12 months ( P< 0.001). At 12 months, TLR was 16.6%, wound healing rate was 68.6%, amputation-free survival was 73.2%, all-cause mortality was 19.8%, and amputation rate was 8.9%. Multivariate Cox regression indicated that high WIfI risk ( HR=3.936, 95% CI: 1.079-14.355, P=0.038) was an independent predictor of amputation-free survival. Conclusion:DCB is effective and safe for hemodialysis patients with infrapopliteal artery disease, while high WIfI risk predicts poor prognosis.

Objective:To investigate the clinical efficacy and safety of laser ablation and closure for the treatment of sacrococcygeal pilonidal disease (SPD) and to analyze risk factors for postoperative recurrence in male patients.Methods:A retrospective observational study was conducted to collect clinical data of 369 patients with SPD who underwent laser ablation and closure in the Anorectal Department of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Shanghai University of Traditional Chinese Medicine between March 2019 and December 2024. Perioperative outcomes and postoperative recurrence were analyzed. The cohort included 313 males and 56 females, with 43 patients aged ≤18 years. The median body mass index was 26.3 (IQR: 22.9, 29.6) kg/m2, and the median disease duration was 28 months (IQR: 4, 76). Among them, 218 male SPD patients who underwent surgery received preoperative sex hormone testing. A logistic regression model was used to analyze the risk factors for recurrence.Results:All patients completed the surgery. The median intraoperative ablation energy delivered was 426.8 (IQR: 243.9, 683.9) J, with no occurrence of major intraoperative complications. Postoperatively, a total of 31 patients (8.4%) required analgesic medication. Within the first postoperative week, 12 patients experienced wound oozing/bleeding; hemostasis was achieved by compression alone in 5 cases, while the remaining 7 instances required suture hemostasis after failed compression attempts. No other complications were observed. The median postoperative hospital stay was 6 (IQR: 4, 8) days, and the median time to return to regular work and life was 7 (IQR: 5, 12) days. The wound healing rate was 100%, with a median wound healing time of 35 (IQR: 30, 42) days. Postoperative recurrence occurred in 19 patients (5.1%), all of whom were male. Multivariate logistic regression analysis identified age ≤18 years (OR = 4.764, 95%CI: 2.424-34.905, P = 0.008) and a history of previous SPD surgery (OR = 5.078, 95%CI: 1.431-18.019, P = 0.012) as independent risk factors for recurrence after SPD laser ablation and closure surgery. Conclusion:Laser ablation and closure are safe, effective, and minimally invasive treatments for SPD. However, particular attention should be paid to the risk of recurrence in young male patients and those with a history of previous SPD surgery.

OBJECTIVE To evaluate the effects of phthalic acid esters(PAEs)on the expression of heme oxygenase-1(HO-1)in HepG2 cells,and to construct an HO-1-based three-dimensional quantita-tive structure-activity relationship(3D-QSAR)model.METHODS ① HepG2 cells were treated with seven types of PAEs:di-(2-ethylhexyl)phthalate(DEHP),di-n-octyl phthalate(DnOP),dimethyl phthalate(DMP),diethyl phthalate(DEP),dihexyl phthalate(DHXP),dimethylglycol phthalate(DMEP),and dibutyl phthalate(DBP),at final concentrations of 0(DMSO,solvent control),0.062 5,0.125,0.25,0.5 and 1 mmol·L-1(n=6)for 48 h at 37℃.The expression level of HO-1 was measured by Western blotting.② A 3D-QSAR model was constructed using comparative molecular similarity indices analysis(CoMSIA)based on the measured HO-1 levels.The applicability domain(AD)of the model was evaluated using the leverage method.Model fitting quality and predictive ability were evaluated via the KNIME Enalos+node to verify model stability.Additionally,molecular docking was performed to validate the binding interactions between PAEs and HO-1.RESULTS ① Compared with the solvent control group,48 h of exposure to 0.062 5 mmol·L-1 PAEs(DMP,DMEP,DEHP,DnOP and DEP)significantly increased HO-1 protein expressions,while 1 mmol·L-1 PAEs(DMP,DBP,DnOP,DEP and DHXP)significantly suppressed HO-1 expressions.② The 3D-QSAR model showed a non-cross-validated coefficient(R2)of 0.996 and a cross-validated coefficient(Q2)of 0.548.All the seven PAEs in the 3D-QSAR model were within the applicability domain(AD)and passed external validation.Molecular docking results indi-cated that DBP,DnOP,DEHP and DHXP exhibited stronger binding affinities to HO-1.CONCLUSION Forty-eight hours of exposure of HepG2 cells to 1 mmol·L-1 PAEs can significantly suppress HO-1 expres-sions.The 3D-QSAR model established in this study provides a potential tool for predicting the HO-1-related toxic effects of novel PAEs.