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 investigate the effects of Amanita caojizong on cardiomyocyte apoptosis and the expression of apoptosis-related factors Bcl-2 and Bax,thereby providing experimental evidence for the prevention and treatment of Amanita caojizong poisoning.Methods Mouse cardiomyocytes(HL-1 cells)cultured in vitro were divided into an experimental group(treated with Amanita caojizong extract)and a control group(treated with PBS).After treatment with Amanita caojizong extract,apoptosis of HL-1 cells was observed using TUNEL staining,and the protein expression levels of Bax,Bcl-2,Caspase-3,and Cleaved Caspase-3 in HL-1 cardiomyocytes were detected by Western blot.Results Compared with the control group,the TUNEL staining showed significantly increased apoptotic fluorescence intensity in the Amanita caojizong extract-treated group.The protein expressions of Bax,Caspase-3,and Cleaved Caspase-3 in HL-1 cells in the Amanita caojizong-treated group were upregulated,while the expression of Bcl-2 was downregulated.Conclusion Amanita caojizong can promote apoptosis of mouse cardiomyocytes,and its mechanism may be associated with the Bcl-2/Bax pathway.

Objective To investigate the effects of Amanita caojizong on cardiomyocyte apoptosis and the expression of apoptosis-related factors Bcl-2 and Bax,thereby providing experimental evidence for the prevention and treatment of Amanita caojizong poisoning.Methods Mouse cardiomyocytes(HL-1 cells)cultured in vitro were divided into an experimental group(treated with Amanita caojizong extract)and a control group(treated with PBS).After treatment with Amanita caojizong extract,apoptosis of HL-1 cells was observed using TUNEL staining,and the protein expression levels of Bax,Bcl-2,Caspase-3,and Cleaved Caspase-3 in HL-1 cardiomyocytes were detected by Western blot.Results Compared with the control group,the TUNEL staining showed significantly increased apoptotic fluorescence intensity in the Amanita caojizong extract-treated group.The protein expressions of Bax,Caspase-3,and Cleaved Caspase-3 in HL-1 cells in the Amanita caojizong-treated group were upregulated,while the expression of Bcl-2 was downregulated.Conclusion Amanita caojizong can promote apoptosis of mouse cardiomyocytes,and its mechanism may be associated with the Bcl-2/Bax pathway.

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.

In the earliest days,the idea that surviving a single infection often resulted in lifelong immunity to the infecting pathogen was recorded and then led to the discovery of vaccination.We have now confirmed that such protection is primarily based on the generation of immunological memory in antibody response.With the wide implementation of more and more vaccines around the world,it is well documented that different vaccines have different potential regarding to the duration of antibody response.In clinical observations,live-attenuated vaccines often elicit long-term immunity but are also accompanied with risks in safety that are hard to avoid.In order to develop novel vaccines with both excellent potential in eliciting antibody memory and low safety risk,it is critical to further investigate the mechanism of antibody memory in the perspective of immunology.Antibody memory is mediated by certain long-lived B cells:long-lived plasma cell can secret antibody to maintain serum antibody titer while memory B cell contributes to the rapid immune response during the secondary encounter of pathogens.Cellular and molecular processes that drive the production of long-lived plasma cells and memory B cells are subjects of intensive research and have important implications for global health.Several factors in the vaccine would indeed affect and regulate these processes,including the antigen valency,vaccine kinetics and the signal integration of both antigen and danger molecules.Many studies have focused on strategies to manipulate these factors to improve or develop new vaccines.Here,we will summarize our current knowledge on how the component in vaccines will affect their potential in generating and sustaining antibody memory,and also point out the challenges we face in the route of developing a"perfect"vaccine.

Extranodal NK/T cell lymphoma, nasal type（ENKTL） is a highly aggressive malignant tumor derived from NK cells. This article reports a case of ENKTL invading the larynx and digestive tract. The clinical clinical manifestations include hoarseness and intranasal masses.
Humans ; Lymphoma, Extranodal NK-T-Cell/pathology* ; Nose/pathology* ; Nose Neoplasms/pathology* ; Larynx/pathology* ; Gastrointestinal Tract/pathology*

Laparoscopic surgery based on magnetic anchor technique has great potential for further minimally invasive surgery and good surgical field exposure, in which the internal grasper is the key factor. In this paper, an internal grasper based on magnetic anchor laparoscopic surgery is designed, which consists of three parts:target magnet, connection module and tissue forceps. The magnetic shield shell is used to wrap the magnetic core in the target magnet, which not only can increase the magnetic force in the working area, but also reduce the magnetic interference between the instruments, and the connecting module can flexibly adjust the length of the internal grasper. The special structure of tissue gripper can effectively reduce deputy injury and facilitate the replacement of clamp position. It has many advantages, such as ingenious design, easy processing, simple operation and wide range of application, which greatly increased its clinical application value.
Equipment Design ; Laparoscopy ; Magnetics ; Magnets ; Surgical Instruments

Objective: To analyze the correlation between circumcision and incidence and clearance of male genital HPV infection. Methods: From May to July 2014, 18-55 year old men who had sexual behavior history were recruited from the general population in Liuzhou, Guangxi to set up a cohort. Totally, 113 circumcised and 560 uncircumcised men were enrolled and interviewed using a questionnaire (including information on demographic characteristics and sexual behaviors), then they were followed-up with 6-month interval for 2 times. On each visit, specimens of male external genitalia were collected and genotyped for HPV DNA. The differences of incidence and clearance of genital HPV infections between circumcised and uncircumcised men were analyzed by Log-rank test. Cox regression was used to analyze the relationship between circumcision and incidence and clearance of HPV infection. Results: The median age (P₂₅, P₇₅) of circumcised and uncircumcised men were 28 (24, 35) and 32 (24, 31), respectively. The incidences of any HPV infections were 9.1 (95%CI: 2.4-15.7) and 8.4 (95% CI: 5.6-11.2) per 1 000 person-months (χ²=0.10, P=0.758), respectively. The clearance of circumcised men [136.3 (95%CI: 70.0-202.7) per 1 000 person-months] was higher than that in uncircumcised men [89.6 (95%CI: 65.9-113.3) per 1 000 person-months] (χ²=8.19, P=0.004). In multivariate COX regression analysis, compared with uncircumcised men, circumcised men had higher possibility to clear any HPV infections (HR: 2.41, 95%CI: 1.30-4.46). Compared with men having one sexual partner, people having more than 4 sexual partners had lower possibility to clear any HPV infections (HR: 0.49, 95%CI: 0.25-0.96). Compared with 18-25 years old men, men aged 26-35 years old had higher possibility to clear high-risk HPV infections (HR: 2.14, 95%CI: 1.08-4.23). Conclusion Circumcised and uncircumcised men had similar incidence of genital HPV infection, whereas, men conducted circumcision and having fewer sexual partners could increase the clearance of genital HPV infections.

Sleep-disorderedbreathingarecloselyassociatedwithischemicstroke.Sleep-disordered breathing includes obstructive sleep apnea and central sleep apnea. Studies have show n that obstructive sleep apnea is an independent risk factor for stroke, w hile stroke can also increase the incidence of sleep-disordered breathing. This article review s the latest research progress of sleep-disordered breathing and stroke.

To detect and phylogeneticaly analyze arenavirus carried by wild rodents in Ningbo ,China ,two pairs of degener‐ate‐primers were designed to amplify the S and L gene of arenavirus ,and then RT‐PCR was applied to detect arenavirus carried by rodents which captured from Ningbo port area .All 73 rodents samples were detected ,of which 12 Rattus norvegicus were positive ,an arenavirus virus strain named DX1401 were separated .The S gene amplified products of DX1401 was about 413 bp ,and the L gene was 1 204 bp .The phylogenetic analysis of S segments showed that DX1401 strain was in one branch of phylogenetic tree with Mobala virus strain ACAR3080 .The genetic distance to Mobala virus strain ACAR3080 was the closest , with the value of 0 .467 ;the phylogenetic analysis of L segments showed that DX1401 strain were in one group of phylogenetic tree with Lassa virus strain Josiah ,NL ,Z148 ,Bamba‐R114 ,Soromba‐R ,Nig08‐A37 ,Nig08‐A47 ,Mobala virus strain ACAR3080 ,Morogoro virus strain 13017/2004 ,Mopeia virus strain Mozambique ,and AN 21366‐BNI .The genetic distance to Mobala virus strain ACAR3080 was the closest ,with the value of 6 .953 .In conclusion ,the study confirmed the existence of arenavirus popular in wild rodents in Ningbo ,China .