Objective:To investigate the factors influencing the decision to delivery interval (DDI) in emergency cesarean section in Guangzhou and the impact of DDI on maternal-infant outcomes.Methods:A retrospective study was conducted on clinical data of pregnant women who underwent emergency cesarean section at municipal and district maternal and child health hospitals in Guangzhou city in 2021. Per the classification method of emergency cesarean section and recommendations for DDI provided by National Institute for Health and Clinical Excellence guidelines, these subjects were classified into Category Ⅰ and Category Ⅱ cesarean sections. Each category was further divided into two subgroups based on DDI: the Category Ⅰ group into >30 min and ≤30 min subgroups, and the Category Ⅱ group into >75 min and ≤75 min subgroups. Chi-square test or Fisher's exact test, two independent samples t-test, Mann-Whitney U test, and logistic regression were used to analyze the potential factors influencing DDI and the impact of DDI on maternal-infant outcomes. Results:(1) Totally 502 women underwent urgent cesarean section, including 304 (60.6%) Category Ⅰ and 198 (39.4%) Category Ⅱ, were analyzed. Among the Category Ⅰ group, 30.3% (92/304) achieved a DDI of ≤30 min, while 37.4% (74/198) of Category Ⅱ cases had a DDI of ≤75 min. (2) For the Category Ⅰ cases, multivariate logistic regression showed that more patients under intravertebral anesthesia, than those under general anesthesia, had a DDI >30 min ( OR=14.04, 95% CI: 6.14-32.10) as well as more with ward-based emergencies than those with delivery room emergencies ( OR=3.21, 95% CI: 1.72-6.00, both P=0.001). Among the Category Ⅱ cases, logistic regression revealed that cesarean section during routine working hours was more likely to achieve DDI >75 min than that during resting hours ( OR=3.93, 95% CI: 2.03-7.63, P=0.001). The risk of DDI >75 min was higher in tertiary maternal and child health hospitals compared with secondary maternal and child health hospitals ( OR=2.45, 95% CI: 1.06-5.70, P=0.037). (3) Among the Category Ⅰ cases, compared with the DDI ≤30 min group, the DDI >30 min group had a lower risk of neonatal Apgar score ≤7 at 1 min ( OR=0.31, 95% CI: 0.14-0.69, P=0.004), but there was no significant difference in the risk of neonatal Apgar score ≤7 at 5 min ( OR=0.21, 95% CI: 0.04-1.17) or neonatal asphyxia ( OR=0.32, 95% CI: 0.07-1.44) between the two subgroups. In cases of Category Ⅱ cesarean sections, there was no significant difference in any maternal-infant outcomes between DDI ≤75 min and DDI >75 min subgroups. Conclusions:The location of emergency and types of anesthesia are the influencing factors of DDI for Category Ⅰ cesarean sections, while the operation time and level of maternal and child health hospital are the influencing factors of DDI for Category Ⅱ cesarean sections. We did not find any impact of DDI on maternal or infant outcome.

Objective To explore whether stimulator of interferon genes(STING)agonist ADU-S100 could enhance the antitumor immune response of a chitosan(CS)nanoparticle-mediated DNA vaccine containing a tumor-specific antigen Dickkopf-related protein 1(DKK1)in multiple myeloma(MM).Methods CS-DNA nanoparticles were prepared by using the compound coprecipitation method.The particle sizes and Zeta potential of the CS-DNA nanoparticles were measured by using the Zetasizer Nano-ZS laser particle size analyzer.The DNA protection effect and in vivo DNA expression efficiency of the CS-DNA nanoparticles were assessed by using gel retardation assay and Western blot,respectively.The lentiviruses expressing human DKK1(hDKK1)genes were used to establish MPC-11 cells(MPC-11-hDKK1)which stably expressed hDKK1,and the MPC-11-hDKK1 cells were subcutaneously given to mice to construct tumor models.The tumor-bearing mice were randomly divided into a control group(intramuscular injection of CS-pcDNA3.1),an ADU-S100 immunization group(subcutaneous injection of ADU-S100),a CS-pDKK1 immunization group(intramuscular injection of CS-pDKK1)and an ADU-S1OO/CS-pDKK1 co-immunization group(intramuscular injection of CS-pDKK1+subcutaneous injection of ADU-S100),with 5 mice in each group.The tumor-bearing mice in each group were immunized 3 times at 10-day intervals according to the corresponding immunization schedule.The size of tumor was measured every week.On day 42 after MPC-11-hDKK1 cell inoculation,the tumor weight of mice in each immunization group was measured;the percentages of CD11c+dendritic cell(DC),CD8+CD11c+DC and major histocompatibility complex class Ⅱ(MHCII)+CD11c+DC subsets in the spleen of mice in each immunization group were detected by using flow cytometry.The splenocytes of mice in each group were stimulated with recombinant hDKK-1 protein in vitro,the percentage of EdU+cells in CD8+T lymphocytes in each immunization group was detected by using flow cytometry,and the killing effect of cytotoxic T lymphocyte(CTL)in each group was assessed by using the lactate dehydrogenase(LDH)cytotoxicity assay kit.Results The particle size and Zeta potential of the CS-DNA nanoparticles were(204.3±2.31)nm and(15.47±1.01)mV,respectively.Gel retardation assay showed that DNA enveloped in CS nanoparticles could be completely retarded.Western blot analysis indicated that CS-DNA nanoparticles could be effectively expressed in vivo.The relative expression of DKK1 protein was significantly higher in MPC-11-hDKK1 cells than in MPC-11-Ctrl cells(P＜0.05).On days 7 and 14 after MPC-11-hDKK1 cell inoculation,there was no significant difference in tumor volume of mice between the ADU-S100 immunization group,CS-pDKK1 immunization group,ADU-S100/CS-pDKK1 co-immunization group and the control group(P＞0.05);on days 21,28,35 and 42 after MPC-11-hDKK1 cell inoculation,the tumor volumes of mice in the ADU-S100 immunization group,CS-pDKK1 immunization group and ADU-S100/CS-pDKK1 co-immunization group were significantly lower than those in the control group(P＜0.05);the tumor volume of mice in the ADU-S100/CS-pDKK1 co-immunization group was significantly lower than that in the ADU-S100 immunization group and CS-pDKK1 immunization group(P＜0.05).On day 42 after MPC-11-hDKK1 cell inoculation,the tumor weight of mice in the ADU-S100 immunization group,CS-pDKK1 immunization group and ADU-S1 OO/CS-pDKK1 co-immunization group was significantly lower than that in the control group(P＜0.05);the tumor weight of mice in the ADU-S100/CS-pDKK1 co-immunization group was significantly lower than that in the ADU-S100 immunization group and CS-pDKK1 immunization group(P＜0.05).The proportions of CD11c+DC,CD8+CD11c+DC and MHCII+CD11c+DC subsets in the spleen of mice in the ADU-S100 immunization group,CS-pDKK1 immunization group and ADU-S100/CS-pDKK1 co-immunization group were significantly higher than those in the control group(P＜0.05).The proportions of CD11c+DC,CD8+CD11c+DC and MHCII+CD11c+DC subsets in the spleen of mice in the ADU-S100/CS-pDKK1 co-immunization group were significantly higher than those in the ADU-S100 immunization group and CS-pDKK1 immunization group(P＜0.05).The CTL killing effect and the proportion of EdU+cells in CD8+T lymphocytes in the ADU-S100 immunization group,CS-pDKK1 immunization group and ADU-S1OO/CS-pDKK1 co-immunization group were significantly higher than those in the control group(P＜0.05);the CTL killing effect and the proportion of EdU+cells in CD8+T lymphocytes in the ADU-S100/CS-pDKK1 co-immunization group were significantly higher than those in the ADU-S100 immunization group and CS-pDKK1 immunization group(P＜0.05).Conclusion STING agonist ADU-S100 can significantly improve the antitumor immunity of the CS-pDKK1 nanoparticle vaccine in MM,and this vaccine strategy provides a potential treatment approach for MM.

Objective:To explore the antitumor effect of ADU-S100/doxorubicin in situ vaccine on diffuse large B-cell lymphoma (DLBCL) and its mechanism.Methods:The 6-week-old female BALB/c mice were selected, and the bilateral murine subcutaneous B-cell lymphoma model was established with murine B-cell lymphoma A20 cells. The subcutaneous tumor-bearing mice were randomly divided into untreated group (without treatment), ADU-S100 in situ vaccine treatment group (intratumoral injection of interferon gene stimulating factor agonist ADU-S100), doxorubicin in situ vaccine treatment group (intratumoral injection of doxorubicin), and ADU-S100/doxorubicin in situ vaccine treatment group (intratumoral injection of ADU-S100 and doxorubicin) by using random number table method, with 5 mice in each group. The right tumors of the bilateral subcutaneous tumor-bearing mice were defined as proximal tumors, and the left tumors of the bilateral subcutaneous tumor-bearing mice were defined as distal tumors. Only the proximal tumors were treated via the intratumoral route, and the distal tumors were not treated. On day 23 after tumor inoculation, the percentages of CD11c + dendritic cells (DC), CD8 + CD11c + DC and CD80 + CD11c + DC in the spleen of mice in each group were detected by flow cytometry. The splenocytes of mice in each group were stimulated with A20 tumor cell lysate in vitro, the percentages of 5'-ethynyl-2'-deoxyuridine-positive (EdU +) cells and tumor necrosis factor-α-positive (TNF-α +) cells in CD8 + T cells in each in situ vaccine treatment group were detected by flow cytometry, and the killing effect of cytotoxic T lymphocyte (CTL) in each group was measured by using the lactate dehydrogenase (LDH) cytotoxicity assay kit. The mice treated with ADU-S100/doxorubicin in situ vaccine were intraperitoneally injected with anti-mouse CD8α (clone 53-6.7) mAb or isotype control on days 7, 12 and 17 after tumor inoculation to eliminate CD8 + cells. On day 23 after tumor inoculation, the proximal and distal tumor volumes of mice in the ADU-S100/doxorubicin in situ vaccine combined with anti-mouse CD8α (clone 53-6.7) mAb or isotype control treatment group were measured, the percentages of CD8 + T cells and CD8 + CD11c + DC in the spleen of tumor-bearing mice in these two groups were detected by flow cytometry, and the infiltration of CD8 + T cells in the tumor tissues from these two groups was detected by immunohistochemistry (IHC) staining. Results:On days 11, 14, 17, 20 and 23 after tumor inoculation, the proximal and distal tumor volumes of mice in each treated group were lower than those in the untreated group (all P < 0.05). The proportions of CD11c + DC in the spleen of the untreated group, ADU-S100 in situ vaccine treatment group, doxorubicin in situ vaccine treatment group and ADU-S100/doxorubicin in situ vaccine treatment group were (4.92±0.63)%, (7.54±0.84)%, (7.45±0.86)% and (11.63±0.85)%, respectively, and the difference was statistically significant ( F = 72.30, P < 0.001); the proportions of CD8 + CD11c + DC were (1.36±0.34)%, (4.02±0.43)%, (4.22±0.61)% and (6.11±0.73)%, respectively, and the difference was statistically significant ( F = 76.09, P < 0.001); the proportions of CD80 + CD11c + DC were (0.51±0.24)%, (1.69±0.23)%, (1.82±0.25)% and (4.09±0.39)%, respectively, and the difference was statistically significant ( F = 167.40, P < 0.001). The CTL responses and the proportion of EdU + cells and TNF-α + cells in CD8 + T cells in each in situ vaccine treatment group were higher than those in the untreated group (all P < 0.05). Furthermore, the enhanced CTL responses and the increased proportion of EdU + cells and TNF-α + cells in CD8 + T cells were observed in the ADU-S100/doxorubicin in situ vaccine treatment group as compared to the ADU-S100 in situ vaccine treatment group and doxorubicin in situ vaccine treatment group (all P < 0.05). The proportions of CD8 + T cells and CD8 + CD11c + DC in the spleen of mice treated with ADU-S100/doxorubicin in situ vaccine and anti-mouse CD8α mAb were lower than those in ADU-S100/doxorubicin in situ vaccine and isotype control group (both P < 0.05) and both proximal and distal tumor volumes of mice treated with ADU-S100/doxorubicin in situ vaccine and anti-mouse CD8α mAb were larger than those in ADU-S100/doxorubicin in situ vaccine and isotype control group (both P < 0.05). Conclusions:ADU-S100/doxorubicin in situ vaccine can induce profound regression of proximal tumors in bilateral murine subcutaneous B-cell lymphoma model and generate systemic immune responses capable of partially inhibiting distant tumor growth, and the antitumor efficacy of ADU-S100/doxorubicin in situ vaccine may require CD8 + CD11c + DC-mediated CD8 + T cell immune responses.

Objective:To explore the focus of ethical governance in the field of artificial intelligence(Al)in medicine.Methods:By comprehensively reviewing relevant literature to compare the relevant laws and regulations of the field of AI in medicine between China and foreign countries,analyze the governance focus of potential ethical issues,and propose the corresponding governance strategies.Results:At present,the laws,regulations,and regulatory systems related to the field of AI in medicine in China need to be improved.The emphasis of ethical governance should focus on core issues such as protecting privacy rights,ensuring the transparency and fairness of algorithms,clarifying the demarcation and allocation of responsibilities,and clarifying public perceptions and attitudes.Conclusion:The government and all sectors of society should actively learn from international legislative experience,and build an omnidirectional and multi-level ethical governance system from the aspects of policy formulation,legal framework,scientific research,and technological research and development by strengthening top-level design,improving policies and regulations,attaching importance to public feedback,and strengthening interdisciplinary cooperation.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.