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 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 assess retinal and choroidal blood flow density in the macular regions of children diagnosed with unilateral low myopia using optical coherence tomography angiography(OCTA).This study aimed to investigate the clinical significance of these mea-surements.Methods A cross-sectional study was conducted on 90 eyes of 45 children with monocular myopia and adolescents aged 8 to 14 years who visited the outpatient department of the Ophthalmology of Dalian Third People's Hospital between June 2022 and February 2023.Optometry was performed after a 1%cyclopentolate cycloplegic muscle paralysis.Eyes with spherical equivalent(SE)-3.00 D to-0.50 D were included in the myopia group,whereas those with SE-0.25 D to＜+2.00 D were placed in the non-myopia group.The Master system was used to measure axial length(AL)and corneal curvature radius(CR),and to calculate AL/CR.Heidelberg spectral-domain optical coherence tomography(SD-OCT)was used to perform horizontal linear scanning of the macular area to obtain subfoveal choroidal thickness(SFCT).The OCTA module was used to obtain 3 mm×3 mm choroidal blood flow images,which were imported into ImageJ graphics processing software to obtain the blood flow densities of the superficial choroidal plexus(SCP),deep choroidal plexus(DCP),choroidal capillary(CC),and foveal avascular zone(FAZ).Pearson's correlation was used to examine the correlations between each blood flow parameter and age,AL,CR,AL/CR,and SFCT.Results The SE and SFCT of the myopia group were smaller(P＜0.05)than those of the non-myopia group,whereas the AL and AL/CR were significantly larger(P＜0.05)than those of the non-myopia group.The DCP blood flow density in the myopia group was significantly lower than that in the non-myopia group(P＜0.01).There was no statistically sig-nificant difference between the residual blood flow parameters of the myopia and non-myopia groups(P＞0.05).The Pearson's correlation analysis indicated that the SCP and DCP blood flow densities in the myopia group were positively correlated with SE(r= 0.611,0.731,P＜0.05),negatively correlated with AL(r=-0.568,-0.712,P＜0.05),and negatively correlated with AL/CR(r=-0.557,-0.564,P＜0.05).The SCP and DCP blood flow densities were negatively correlated with AL/CR in the non-myopia group(r=-0.615,-0.656,P＜0.05).The CC density and FAZ area in the two groups did not correlate with age,SE,AL,CR,AL/CR,or SFCT(P＞0.05).Conclusion Com-pared to non-myopic eyes,the eyes of children with mild monocular myopia had lower DCP blood flow density.Moreover,retinal blood flow density in myopic eyes was correlated with SE,AL,and AL/CR,whereas retinal blood flow density in non-myopic eyes was only correlated with AL/CR.

Drugs and physical stimulation,including light,electricity,and magnetic fields,can be used to influence how neurons operate,among which chemogenetic and optogenetic technologies are most widely used.In recent years,magnetogenetic technology has also been developed that can acti-vate neurons in magnetic fields through magnetic sensitive actuators,leading to non-invasive and instanta-neous activation of specific brain regions.This article reviews the evolution of and problems with chemoge-netic and optogenetic techniques commonly used in brain science research.It also outlines the latest progress in magnetogenetic technologies,which are not full-fledged yet,as well as the role of transcra-nial electrical stimulation,transcranial magnetic stimulation,deep brain stimulation and transcranial ultra-sound stimulation technology in the functional regulation of brain diseases.Constant adjustment and improvement can make it possible for these technologies to be used more widely for the study of brain sciences and the treatment of brain diseases.

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 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.