Objective: To systematically evaluate the influencing factors for repeated implantation failure (RIF) after in vitro fertilization-embryo transfer (IVF-ET) in China, so as to provide the evidence for prevention of RIF. Methods: Literature on influencing factors for RIF in China were retrieved from CNKI, Wanfang Data, VIP, China Medical Literature Service System, PubMed, Web of Science, Cochrane Library and Embase from inception to September, 2024. A meta-analysis was performed using RevMan 5.3 and Stata 14.0 softwares. Literature were excluded one by one for sensitivity analysis. Publication bias was evaluated using Egger's test. Results: Initially 4 836 relevant articles were retrieved, and 12 of them were finally included, with a total sample size of 11 554 individuals. There were 10 case-control studies, 1 cohort study, and 1 cross-sectional study; and 10 high-quality studies and 2 medium-quality studies. The meta-analysis showed that factors including advanced age (OR=1.121, 95%CI: 1.035-1.215), prolonged infertility duration (OR=1.237, 95%CI: 1.091-1.403), abnormal hysteroscopy findings (OR=2.205, 95%CI: 1.119-4.348), positive anti-nuclear antibody (ANA) (OR=2.393, 95%CI: 1.473-3.886), and positive anti-beta2 glycoprotein Ⅰ antibody (β2-GPⅠ-Ab) (OR=2.824, 95%CI: 1.987-4.013) were associated with an increased risk of RIF; while factors including the large number of embryos transferred (OR=0.309, 95%CI: 0.098-0.973), thicker endometrium (OR=0.601, 95%CI: 0.556-0.650), and higher granulocyte colony-stimulating factor (G-CSF) levels (OR=0.657, 95%CI: 0.511-0.845) were associated with a reduced risk of RIF. Conclusion IVF-ET RIF is associated with age, infertility duration, number of embryos transferred, endometrial thickness, hysteroscopy findings, G-CSF levels, ANA and β2-GPⅠ-Ab.

OBJECTIVE: To analyze the time characteristics of the Ethos online adaptive radiotherapy (OART) process in clinical practice and provide guidance for the comprehensive optimization of each stage of adaptive radiotherapy. METHODS: The study involved 61 patients with cervical, rectal, gastric, lung, esophageal, and breast cancers who underwent Ethos OART. The mean ± standard deviation of segmental time, total time, and target volume for these patients were tracked. The time characteristics for different cancer types were evaluated, and the average time for target and organ at risk (OAR) modifications was compared with the average target volume for each cancer type. RESULTS: Cervical cancer born the longest total treatment time, while breast cancer had the shortest. For all cancer types except breast cancer, the modification time for target and OAR was the most time-consuming segment. The average time for target and OAR modifications aligned with the trend of the average target volume. CONCLUSION The total treatment time for various cancers ranges from 15 to 35 minutes, indicating room for improvement.
Humans ; Radiotherapy Planning, Computer-Assisted/methods* ; Neoplasms/radiotherapy* ; Female

Objective To construct a discharge preparation program for patients after total laryn-gectomy led by specialist nurses and explore its application effects.Methods A multidisciplinary team was established to construct a discharge preparation program for patients after total laryngectomy led by specialist nurses based on Meleis's transition theory.A total of 120 patients who underwent to-tal laryngectomy from April 2023 to October 2024 were selected as the study subjects.A total of 60 patients admitted from April to December 2023 were set as control group,and 60 patients admitted from January to October 2024 were set as intervention group.The intervention group implemented the discharge preparation program for patients after total laryngectomy led by specialist nurses,while the control group received conventional perioperative management after total laryngectomy.The discharge readiness,quality of discharge instruction,and incidence of complications during hospitalization on the day of discharge were compared between the two groups.Additionally,the scores of home health care experience at 1 and 3 months after discharge were compared between the two groups.Results On the day of discharge,the scores of each dimension and the total score of the discharge readinessscale in the intervention group were higher than those in the control group.The scores of the dimensions("Actually received content"and"Discharge instruction skills and effects")and the total score of the Quality of Discharge Instruction Scale in the intervention group were higher than those in the control group,with statistically significant differences(P＜0.05).The total incidence of complications during hospitalization in the intervention group was lower than that in the control group,with a sta-tistically significant difference(P＜0.05).At 1 and 3 months after discharge,the scores of the home health care experience scale in the intervention group were higher than those in the control group,with statistically significant differences(P＜0.05).Conclusion The construction of a dis-charge preparation service program for patients after total laryngectomy led by specialist nurses based on Meleis's transition theory can effectively improve the discharge readiness and quality of discharge instruction of patients undergoing total laryngectomy,reduce the incidence of complications,and im-prove the prognosis and early home health care level.

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.

ObjectiveTo establish a method using ultra-high performance liquid chromatography-tandem mass spectrometry （UHPLC-MS/MS） for the detection of six common trichothecene toxins in oats. MethodsOats were selected as the research subject in this study. Response surface design was used to optimize the QuEChERS extraction method. Additionally， a rapid and efficient strategy for sample extraction and purification was developed. Combined with UHPLC-MS/MS， six commonly co-occurring trichothecene toxins in oats were quantitatively analyzed simultaneously. ResultsThis method demonstrated good analytical performance for each analyte across the corresponding concentration ranges （r>0.99）， with accuracy ranging from 87.26% to 99.64%. The inter-day and intra-day relative standard deviations were less than 6.8% and 5.5%， respectively， indicating its potential for practical application. This method was used to detect mycotoxins in 12 oat samples from China， and it was found that one sample exceeded the standard limits for deoxynivalenol （DON）， and the co-contamination of trichothecene toxin was prevalent. ConclusionThe risk posed by these toxins has been underestimated. Ongoing， extensive monitoring is necessary to provide contamination data to assess the consumer risk.

Objective: To examine the developmental trajectory of fine motor ability in schoolage children with attention deficit hyperactivity disorder (ADHD) for two years, so as to provide scientific evidence to promote motor development in ADHD children. Methods: From April to June 2019, 31 children aged 6-8 years old were selected from a public elementary school. They were diagnosed with ADHD by two psychiatric professionals according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) criteria. Additionally, 31 typical developmental children, matched for age, sex and IQ with the ADHD group, were recruited as the control group. Fine motor ability was assessed with tasks of hand manual dexterity in Movement Assessment Battery for Children-2 (MACB-2), and a followup assessment was conducted from April to June 2021. The development changes of fine motor ability between two groups of children were compared by using t test and repeated measures analysis of variance. Results: Between baseline and followup periods after two years, the total score of hand fine motor in the ADHD group did not show significant improvement (7.4±3.0, 8.0±3.4; t=-1.05, P>0.05), while there was a small effect size improvement in typically developing control group (9.5±2.1, 10.5±2.4; t=-2.12, effect size=0.38, P<0.05). Followup after two years, coin/peg throwing scores with dominant hand improved between ADHD group and control group (7.0±3.3, 9.5±3.2; 8.4±2.8, 11.6±1.6) (t=-3.74, -6.33, P<0.01; effect size=0.67, 1.14), with a smaller improvement in the ADHD group. The score for threading beads/threads decreased in between ADHD group and control group (7.9±2.4, 5.8±3.1; 9.2±1.1, 8.2±1.9) (t=3.89, 2.78, P<0.01; effect size=0.70, 0.50), with a greater decrease in the ADHD group. Conclusions The development speed of fine motor ability in children with ADHD aged 6-8 is slow and continues to lag behind normal developmental children. Fine motor development in children with ADHD should be closely monitored, and targeted interventions should be implemented when necessary.

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.