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.

Chronic epididymitis (CE) is a long-standing inflammatory condition of the epididymis caused by unresolved acute infections, chronic infections, medication use, or other factors. Clinically, it is characterized by persistent dull pain or a dragging sensation in one or both sides of the scrotum. The disease course typically exceeds three months and is marked by insidious onset and recurrent episodes. Current studies suggest that CE may disrupt the epididymal microenvironment through multiple pathological processes, including local inflammatory responses, oxidative stress, fibrotic remodeling, and autophagy. These alterations impair sperm maturation, transport, and capacitation, thereby contributing to male reproductive dysfunction and infertility. This review summarizes the major etiologies and pathophysiological characteristics of CE and its impact on male reproductive function. It focuses on the roles of inflammatory cytokines and related signaling pathways, oxidative stress mechanisms, and fibrotic progression in the pathogenesis of CE. Moreover, it explores targeted therapeutic strategies based on these mechanisms, aiming to provide a theoretical basis for identifying key molecular targets and signaling pathways involved in CE-induced male reproductive impairment.

Objective To explore the effect of repetitive transcranial magnetic stimulation(rTMS)on the sleep disorder and examination results of recruits.Methods At a training base,the Pittsburgh Sleep Quality Index(PSQI)was used to screen the recruits with sleep disorders(total score of PSQI>7).The recruits were randomly assigned to rTMS group or sham rTMS group.Both groups received cognitive and behavioral intervention therapy,including sleep health education and relaxation training.Moreover,the rTMS group was treated with rTMS at the right posterior parietal lobe by continuous theta burst stimulation(cTBS)twice a day at an interval of at least 50 min for 5 consecutive days as a course of treatment with an interval of 2 days for a total of 2 courses of treatment.The coil position and stimulus intensity of sham rTMS group were consistent with the rTMS group,but the head of subjects was perpendicular to the coil plane and there was no effective stimulation.Before and after treatment,PSQI,self-rating depression scale,generalized anxiety disorder-7 and health questionnaire-15 were used to evaluate the sleep,mood and physical state of the recruits.The training result was assessed one month after treatment.The total effective rate of PSQI improvement and examination results were compared between the two groups.The independent influencing factors of excellent examination result were analyzed.Results Among 351 recruits,83 with sleep disorders completed treatment and follow-up.There were 40 patients in the rTMS group and 43 patients in the sham rTMS group.There was no significant difference between the two groups at baseline.After treatment,the total effective rate of PSQI improvement in the rTMS group was higher than that in the sham rTMS group(77.50%vs 53.49%,P=0.022).The average examination score and excellent rate of the rTMS group were higher than those of the sham rTMS group(91.58±3.19 vs 89.47±4.67,P=0.020;85%vs 65.12%,P=0.037).Logistic regression analysis showed that the treatment mode(rTMS group)was the independent influencing factor of excellent examination results(P=0.032).Conclusion rTMS can effectively and safely improve the sleep disorders and examination results of recruits.rTMS may play a positive role in improving the learning and training effect of recruits,which needs to be further proved.

Chronic epididymitis (CE) is a long-standing inflammatory condition of the epididymis caused by unresolved acute infections, chronic infections, medication use, or other factors. Clinically, it is characterized by persistent dull pain or a dragging sensation in one or both sides of the scrotum. The disease course typically exceeds three months and is marked by insidious onset and recurrent episodes. Current studies suggest that CE may disrupt the epididymal microenvironment through multiple pathological processes, including local inflammatory responses, oxidative stress, fibrotic remodeling, and autophagy. These alterations impair sperm maturation, transport, and capacitation, thereby contributing to male reproductive dysfunction and infertility. This review summarizes the major etiologies and pathophysiological characteristics of CE and its impact on male reproductive function. It focuses on the roles of inflammatory cytokines and related signaling pathways, oxidative stress mechanisms, and fibrotic progression in the pathogenesis of CE. Moreover, it explores targeted therapeutic strategies based on these mechanisms, aiming to provide a theoretical basis for identifying key molecular targets and signaling pathways involved in CE-induced male reproductive impairment.

Fecal microbiota transplantation (FMT) has the potential to rebuild the intestinal microbiome of patients, which can influence the disease course, alleviate symptoms, or even cure the disease. It is seen as a promising breakthrough for treating major chronic diseases that are difficult to manage. Currently, FMT therapy has been clinically studied for over 80 diseases and has led to significant breakthroughs. However, there are still four main challenges: (1) identifying the effective characteristics of donor microbiota and ensuring precise matching between donors and recipients; (2) understanding the pathways and molecular mechanisms by which key FMT bacteria and metabolites improve disease outcomes; (3) studying strain interactions and colonization mechanisms to restore intestinal microbiota balance; and (4) refining the precision of microbiome and functional microbiota transplantation. To address these clinical challenges, this article reviews the latest research both domestically and internationally, outlines the response patterns of FMT therapy, examines the reasons behind FMT failure, and explores future directions for the development of FMT. The aim is to accelerate the scientific and precise advancement of FMT technology in China.

Isoflavones which mainly distributed in leguminous plants have plenty of health benefits.Isoflavone synthase(IFS)is a membrane-associated cytochrome P450 enzyme(CYP450)which carries out the unique aryl-ring migration and hydroxylation.So far,few crystal structures of plant P450s have been obtained.We determined the crystal structure of IFS from Medicago truncatula at 1.9 ? by MAD method using a selenomethionine substituted crystal and conducted molecular docking and mutagenesis study.The structure of IFS complexed with imidazole exhibits the helix Ⅰa-loop-helix Ⅰβ motif which cor-responds to helix Ⅰ of other P450s.Compared with structures of common P450s,IFS/imidazole structure contains an extra domain,i.e.,the γ-domain.The structure reveals a homodimer in which the γ-domain of one molecule interacts with the β-domain of another.The plane of heme group makes an angle of ap-proximately 40° with the helix Ⅰa-loop-helix Ⅰβ motif.Molecular docking combined with mutagenesis study suggested that Trp-128 and Asp-300 might play important roles in substrate binding and recogni-tion.Phe-301,Ser-303 and Gly-305 from the helix Ⅰa-loop-helix Ⅰβ motif may play important roles in the aryl-ring migration.These novel structural features reveal insights into the unique reaction mechanism of IFS and provide a basis for engineering IFS in leguminous crops for health purpose.

Fecal microbiota transplantation (FMT) has the potential to rebuild the intestinal microbiome of patients, which can influence the disease course, alleviate symptoms, or even cure the disease. It is seen as a promising breakthrough for treating major chronic diseases that are difficult to manage. Currently, FMT therapy has been clinically studied for over 80 diseases and has led to significant breakthroughs. However, there are still four main challenges: (1) identifying the effective characteristics of donor microbiota and ensuring precise matching between donors and recipients; (2) understanding the pathways and molecular mechanisms by which key FMT bacteria and metabolites improve disease outcomes; (3) studying strain interactions and colonization mechanisms to restore intestinal microbiota balance; and (4) refining the precision of microbiome and functional microbiota transplantation. To address these clinical challenges, this article reviews the latest research both domestically and internationally, outlines the response patterns of FMT therapy, examines the reasons behind FMT failure, and explores future directions for the development of FMT. The aim is to accelerate the scientific and precise advancement of FMT technology in China.

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.

Adrenocortical carcinoma（ACC）is a rare and highly aggressive malignant tumor. Currently，mitotane is the first-line treatment. However，reports on neoadjuvant therapy for ACC using mitotane combined with immune checkpoint inhibitors remain scarce. This article reports a case of ACC. The patient was asymptomatic，and a right adrenal mass was detected during examination. Diagnostic imaging and endocrine evaluation confirmed the diagnosis of ACC. Due to the large tumor size，radical resection was initially considered unfeasible. After 7 months of mitotane therapy and two courses of tislelizumab，significant tumor shrinkage was achieved，allowing for successful open resection of the large right adrenal tumor combined with right nephrectomy. Postoperative histopathological examination confirmed the diagnosis of ACC. During the 3-month postoperative follow-up，no evidence of recurrence or metastasis was observed.