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 compare the effects of scalp acupuncture combined with rehabilitation therapy and rehabilitation therapy alone on the brain functional connectivity(FC)in patients with post-stroke motor dysfunction.Method:Forty patients with post-stroke motor dysfunction were randomly divided into a treatment group and a control group,with 20 patients in each group.Both groups received routine rehabilitation therapy,with the treatment group receiving additionally scalp acupuncture therapy.Functional magnetic resonance imaging(fMRI)scans were performed on all patients before and after treatment,with the affected side primary motor cortex(M1)as the region of interest(ROI)to observe the FC between the Ml and other brain regions.Result:In the treatment group,FC was enhanced between the orbital inferior frontal gyrus,orbital middle frontal gyrus,right middle frontal gyrus,bilateral medial superior frontal gyri,and Ml;conversely,FC be-tween the right inferior temporal gyrus and Ml was reduced(FWE corrected,P＜0.05,cluster size＞59).In the control group,FC between the left triangular part of the inferior frontal gyrus and Ml was enhanced.Af-ter treatment,both groups showed a significant increase in Fugl-Meyer scores compared to pre-treatment,with the treatment group scoring higher than the control group(P＜0.01).In the treatment group,changes in FC be-fore and after treatment were moderately and significantly correlated with improvements in Fugl-Meyer scores(P＜0.05).Additionally,changes in FC of the bilateral medial superior frontal gyri before and after treatment were significantly positively correlated with changes in upper limb Fugl-Meyer scores(P＜0.05)in the treatment group.Conclusion:Scalp acupuncture combined with rehabilitation therapy can enhance the functional connectivity be-tween the frontal lobe and the affected side Ml,and the functional connectivity of the bilateral medial superi-or frontal gyri is associated with the recovery of upper limb function in patients with post-stroke motor dys-function.

Objective:To compare the effects of scalp acupuncture combined with rehabilitation therapy and rehabilitation therapy alone on the brain functional connectivity(FC)in patients with post-stroke motor dysfunction.Method:Forty patients with post-stroke motor dysfunction were randomly divided into a treatment group and a control group,with 20 patients in each group.Both groups received routine rehabilitation therapy,with the treatment group receiving additionally scalp acupuncture therapy.Functional magnetic resonance imaging(fMRI)scans were performed on all patients before and after treatment,with the affected side primary motor cortex(M1)as the region of interest(ROI)to observe the FC between the Ml and other brain regions.Result:In the treatment group,FC was enhanced between the orbital inferior frontal gyrus,orbital middle frontal gyrus,right middle frontal gyrus,bilateral medial superior frontal gyri,and Ml;conversely,FC be-tween the right inferior temporal gyrus and Ml was reduced(FWE corrected,P＜0.05,cluster size＞59).In the control group,FC between the left triangular part of the inferior frontal gyrus and Ml was enhanced.Af-ter treatment,both groups showed a significant increase in Fugl-Meyer scores compared to pre-treatment,with the treatment group scoring higher than the control group(P＜0.01).In the treatment group,changes in FC be-fore and after treatment were moderately and significantly correlated with improvements in Fugl-Meyer scores(P＜0.05).Additionally,changes in FC of the bilateral medial superior frontal gyri before and after treatment were significantly positively correlated with changes in upper limb Fugl-Meyer scores(P＜0.05)in the treatment group.Conclusion:Scalp acupuncture combined with rehabilitation therapy can enhance the functional connectivity be-tween the frontal lobe and the affected side Ml,and the functional connectivity of the bilateral medial superi-or frontal gyri is associated with the recovery of upper limb function in patients with post-stroke motor dys-function.

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 analyze the construction and operation of the 19th Hangzhou Asian Games designated hospitals, and to discuss the medical emergency security work of large-scale sports events, so as to provide references for the planning of designated hospitals in future large-scale sports events.Methods:Retrospective analysis was made on the establishment principles, requirements, selection of medical support personnel, and training exercises of the designated hospitals, focusing on the key links such as organizational system, staffing, designated areas, and drug management.Results:Total of 40 designated hospitals have successfully completed the task of medical security by rebuilding the medical security area of the Asian Games, elevating the process, equipping facilities, and strengthening staff training. During the Asian Games, 349 people were transferred to designated hospitals by ambulance, 54 people were hospitalized, 19 people underwent surgery, and 1022 people went to designated hospitals by themselves.Conclusion:The construction of the designated hospitals during the 19th Hangzhou Asian Games was of high quality, efficient and smooth operation. It is suggested that efforts should be made in the reconstruction of the medical security area for the Asian Games to be "relatively independent". The treatment process of self-visiting patients should be fully considered and the flat urgent emergency response mechanism needs to be established.

Objective: To explore the effect and mechanism of chrysin and chloroquine(CQ) on the proliferation and apoptosis of ovarian cancer cells. Methods: Ovarian cancer specimens and normal ovarian specimens were selected after surgical resection and immunohistochemical experimental methods was used to detect the expression level of autophagy-related protein LC3 in ovarian cancer and normal ovarian tissues. The CCK-8 method was used to detect the proliferation of ovarian cancer cell SKOV3. Western blot and immunofluorescence were used to detect the expression of LC3 in SKOV3 cells. An electron microscope was used to detect the number of autophagosomes in SKOV3 cells. Flow cytometry was used to detect SKOV3 cell apoptosis. Results: The expression level of LC3 in ovarian cancer was higher than that in normal ovarian tissues. SKOV3 cells were treated with chrysin at concentrations of 0, 20, 40, 60, 80, 100 μmol/L for 24 hours. As the concentration increased, cell proliferation decreased significantly. Compared with the control group, 40 μmol/L, chrysin significantly up-regulated the expression of LC3 after 24 hours(P<0.05). Compared with the control group, after 24 hours of SKOV3 cells treated with chrysanthemum, more autophagosomes were observed under transmission electron microscope(P<0.05), but no autophagosome was observed in the control group. When chrysin and chloroquine were used in combination for 24 hours, the expression of LC3 protein in the combined treatment group was higher than that in the chrysin and CQ treatment group(P<0.05). Chrysin could obviously induce apoptosis of SKOV3 cells, and the group of Chrysin+CQ showed the highest proportion of apoptosis(P<0.05). Conclusion Chrysin can induce autophagy to inhibit the proliferation of ovarian cancer cells and promote apoptosis.

The laterodorsal tegmentum (LDT) is a brain structure involved in distinct behaviors including arousal, reward, and innate fear. How environmental stimuli and top-down control from high-order sensory and limbic cortical areas converge and coordinate in this region to modulate diverse behavioral outputs remains unclear. Using a modified rabies virus, we applied monosynaptic retrograde tracing to the whole brain to examine the LDT cell type specific upstream nuclei. The LDT received very strong midbrain and hindbrain afferents and moderate cortical and hypothalamic innervation but weak connections to the thalamus. The main projection neurons from cortical areas were restricted to the limbic lobe, including the ventral orbital cortex (VO), prelimbic, and cingulate cortices. Although different cell populations received qualitatively similar inputs, primarily via afferents from the periaqueductal gray area, superior colliculus, and the LDT itself, parvalbumin-positive (PV) GABAergic cells received preferential projections from local LDT neurons. With regard to the different subtypes of GABAergic cells, a considerable number of nuclei, including those of the ventral tegmental area, central amygdaloid nucleus, and VO, made significantly greater inputs to somatostatin-positive cells than to PV cells. Diverse inputs to the LDT on a system-wide level were revealed.

The competence of scientific research ethnical review in domestic hospital was inadequate,which was associated with the development of medical ethnics,values of Chinese traditional society,unsound domestic laws and regulations,weak administrative management,unqualified committee of medical ethnics,the drive of scientific deriving interests and restriction of project funds.Aiming at the above problems,countermeasures were carried out to strengthen the construction of laws and regulations,strengthen the constraint of administrative management,standardize the self-construction of ethnic committee,implement the standard operative procedure,thus to provide a reference for the standardized construction of scientific research ethnical review.

Purpose To investigate the expression of miR-200a and PTEN in colorectal carcinoma (CRC) and their relationships with clinicopathologic features. Methods In situ hybridization and immunohistochemistry ( EnVision method) for miR-200a and PTEN were performed in 87 CRCs and normal colorectal tissues distant from tumors. Relationship between expression of miR-200a and PTEN and clinicopathologic parameters of CRC was also analyzed. Results The in situ hybridization showed that the positive expression rate of the miR-200a in CRC was higher than those in normal colorectal mucosa (P<0. 01). The expression of miR-200a was correlated with the degree of tumor differentiation (rs =0. 503, P<0. 01). The immunohistochemistry showed that the positive expression rate of the PTEN in CRC was lower than those in normal colorectal mucosa (P<0. 01). The expression of miR-200a was correlated with the degree of tumor differentiation (rs = -0. 493, P<0. 01). Expression of miR-200a and PTEN was not correlated with age, sex, tumor size, depth of tumor invasion, lymph node metastasis and TNM stage (P>0. 05). The expression of miR-200a had a close negative correlation to that of PTEN in CRC (P<0. 01). Conclusions Overexpression of miR-200a might be associated with the occurrence and development by targeting PTEN, and they could be the indicators in the early diagnosis,treatment and prognosis of CRC.

Objective To analyze the curative effects of different managements of different types of fracture of the first metacarpal basal body. Methods From October 1984 to October 2003, 142 patients with fracture of the first metacarpal basal body were treated with 5 different methods: manipulative reduction and fixation with abduction tooth arch, manipulative reduction and suspension traction, manipulative reduction and fixation with abduction frame, manipulative reduction and percutaneous internal fixation with Kirschner wire, as well as open reduction and internal fixation with Kirschner wire or screw. Results 80 patients were followed up. The therapeutic efficacy was excellent in 65 cases , good in 13 cases, poor in 2 cases. Conclusion Different types of fracture of the first metacarpal basal body can be treated satisfactorily if a suitable management is applied accordingly.