Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. 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, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. 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, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objectives:To investigate the association between social determinants of health(SDOH)and incident stroke and analyze the main risk factors for stroke among resident with different SDOH levels. Methods:From 2012 to 2015,30 036 residents(≥35 years old)from 30 districts in 14 provincial-level administrative divisions in China were enrolled this study based on stratified multi-stage-random-sampling method.The prevalence of cardiovascular diseases and related risk factors were investigated,and stroke events were followed up in 2018 to 2019.Principal component analysis was performed to establish SDOH scores based on 9 indicators related to socioeconomic and healthcare resources,participants were divided into low SDOH group(n=8 343)when it was≥-2.01 to<-1.14,middle SDOH group(n=7 257)when it was≥-1.14 to<0.10,and high SDOH group(n=8 457)when it was≥0.10 to≤5.79.Multivariate Cox regression was applied to estimate the association of SDOH levels with incident stroke.The random survival forest method was used to analyze the major risk factors in different SDOH levels. Results:A total of 24 057 participants were finally included,669(2.8%)participants developed stroke during a mean of(4.7±0.8)years follow-up.The incidence densities of stroke in the low,medium,and high SDOH groups were 468.39,628.85,and 700.39/100 000 person-years,respectively(Pdifference<0.05,Ptrend=0.01).Compared with individuals with low SDOH level group,fully HR for incident stroke among those with medium and high were 1.91(95%CI:1.54-2.36)and 1.59(95%CI:1.30-1.95),respectively(Ptrend<0.001).Advanced age is the primary risk factor for stroke in the population,especially in districts with high SDOH level.In districts with medium SDOH level,diabetes is an important risk factor for stroke.High blood pressure and alcohol consumption are important modifiable risk factors in low SDOH level districts. Conclusions:Present study shows that higher levels of SDOH are associated with increased risk of stroke.The main risk factors for stroke differ among participants with different SDOH level districts.Targeted interventions should be implemented to improve the prevention and treatment of stroke in populations with different levels of SDOH.

Objective:To explore the difference of embryological and clinical outcomes between the oocytes with indented zona pellucida and normal morphology.Methods:A retrospective cohort study was conducted on 93 cycles treated with intracytoplasmic sperm injection (ICSI) in the Clinical Reproductive Medicine Center of the First Affiliated Hospital of Nanjing Medical University from January 2015 to December 2019. According to the morphology of oocytes and the methods of fertilization, the cycles were divided into three groups, normal oocytes with traditional ICSI group (group A, n=52), indented zona pellucida oocytes with traditional ICSI group (group B, n=30), and indented zona pellucida oocytes with rescue ICSI group (group C, n=11). Laboratory and clinical results were compared among the three groups. Results:There were no differences in oocytes retrieved rate, normal fertility rate, clinical pregnancy rate and implantation rate among the three groups (all P>0.05). The good-quality embryo rate in groups A [62.50% (250/400)] and B [59.07% (114/193)] was significantly higher than that in group C [40.85% (29/71); P=0.001, P=0.006] and blastocyst formation rate in group A [64.78% (160/247)] was significantly higher than that in group C [37.14% (13/35), P=0.002]. Otherwise, the abortion rate in group A [2.44% (1/41)] was significantly lower than that in groups B and C [21.05% (4/19), P=0.031; 44.44% (4/9), P=0.002] and the cumulative pregnancy rate in group A [82.69% (43/52)] was comparatively higher than that in groups B and C [56.67% (17/30), P=0.009; 45.45% (5/11), P=0.014]. Conclusion:Traditional ICSI insemination of the indented pellucida oocytes showed the similar results of good-quality embryo rate and blastocyst formation rate with that of normal oocytes, but the overall clinical outcome was lower than that of normal oocytes. Abnormal oocytes of zona pellucida should be inseminated by ICSI directly, which can obtain better clinical outcome.

Objective:To explore the difference of embryological and clinical outcomes between the oocytes with indented zona pellucida and normal morphology.Methods:A retrospective cohort study was conducted on 93 cycles treated with intracytoplasmic sperm injection (ICSI) in the Clinical Reproductive Medicine Center of the First Affiliated Hospital of Nanjing Medical University from January 2015 to December 2019. According to the morphology of oocytes and the methods of fertilization, the cycles were divided into three groups, normal oocytes with traditional ICSI group (group A, n=52), indented zona pellucida oocytes with traditional ICSI group (group B, n=30), and indented zona pellucida oocytes with rescue ICSI group (group C, n=11). Laboratory and clinical results were compared among the three groups. Results:There were no differences in oocytes retrieved rate, normal fertility rate, clinical pregnancy rate and implantation rate among the three groups (all P>0.05). The good-quality embryo rate in groups A [62.50% (250/400)] and B [59.07% (114/193)] was significantly higher than that in group C [40.85% (29/71); P=0.001, P=0.006] and blastocyst formation rate in group A [64.78% (160/247)] was significantly higher than that in group C [37.14% (13/35), P=0.002]. Otherwise, the abortion rate in group A [2.44% (1/41)] was significantly lower than that in groups B and C [21.05% (4/19), P=0.031; 44.44% (4/9), P=0.002] and the cumulative pregnancy rate in group A [82.69% (43/52)] was comparatively higher than that in groups B and C [56.67% (17/30), P=0.009; 45.45% (5/11), P=0.014]. Conclusion:Traditional ICSI insemination of the indented pellucida oocytes showed the similar results of good-quality embryo rate and blastocyst formation rate with that of normal oocytes, but the overall clinical outcome was lower than that of normal oocytes. Abnormal oocytes of zona pellucida should be inseminated by ICSI directly, which can obtain better clinical outcome.

Objective:To monitor the epidemiological characteristics of viral etiology in children with influenza-like illness and to guide the prevention and management of acute respiratory tract infections in childhood.Methods:Nasopharyngeal swabs were collected from the outpatient children seeking medical care in Children′s Hospital of Fudan University, Shanghai for influenza-like illness between January 2015 and December 2018. Multiplex real-time polymerase chain reaction was performed to detect respiratory syncytial virus (RSV), influenza virus (Flu), adenovirus (ADV), parainfluenza virus (PIV, type Ⅰ to type Ⅳ) and enterovirus (EV), and the epidemiological data were analyzed. Chi-square test was used for statistical analysis.Results:A total of 2 271 patients with influenza-like illness were enrolled, age range from two months to 182 months old, 1 280 cases(56.4%) were positive for the target respiratory viruses tested on respiratory samples. The detection rates of FluA, FluB, PIV, EV, ADV, RSV were 15.1%(343/2 271), 12.5%(284/2 271), 8.4%(191/2 271), 7.8%(177/2 271), 5.1%(116/2 271) and 6.7%(152/2 271), respectively.The detection rates of influenza virus were statistically different among the age groups ( χ2=39.33, P<0.05), which showed an increasing trend with the increasing ages. The detection rate of RSV was 9.7%(35/361) in infant group from zero to 12 months old, which was higher than other age groups. Usually, FluA had two epidemic peaks during the winter and summer seasons, the epidemics of FluB and RSV peaked during the winter season, and EV and PIV were more prevalent in the summer season. Conclusions:Influenza virus remains the most common viral pathogen responsible for childhood influenza-like illness in Shanghai.Influenza virus has high incidence in winter.Widely influenza vaccination is highly recommended for the effective prevention the influenza outbreaks.Continuous monitoring the epidemic trend of viral respiratory infections is imperative for the prevention and control of diseases.

Objective To investigate the effect of early adequate enteral nutrition (EN) on diaphragmatic thickness and prognosis of mechanical ventilation (MV) patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) by ultrasound. Methods Sixty-two MV patients with AECOPD and feasible to receive early EN were admitted to the Department of Intensive Care Unit (ICU) of Wenling Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University from April 2017 to March 2018, and they were divided into an observation group and a control group according to random number table, 31 cases in each group. Besides conventional treatment, both groups started EN (EN) within 2 days, in the observation group, the EN was adequately treated, and the target calories were gradually reached within 3 days; in the control group, nourishing feeding was given, and the target calories were gradually reached after 7 days. Ultrasound was used to measure the diaphragmatic end-expiratory muscle thickness (DTee) and end-inspiratory muscle thickness (DTei) before and after treatment for 3 days, 7 days, and the diaphragmatic thickening fraction (DTF) were calculated; in addition, the MV time, ICU time of stay, 14-day success rate of off-line and incidence of ventilator associated pneumonia (VAP) were compared between the two groups. Results There were no statistical significant differences in the comparisons of age, arterial blood gas analysis and acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score etc general status, and the parameters of DTee, DTei and DTF between the two groups before treatment (all P > 0.05). No statistical significant differences in DTee were found after treatment for 3 days, 7 days and after off-line in the comparisons between the two groups (mm: 3 days was 2.69±0.12 vs. 2.68±0.15, 7 days was 2.70±0.14 vs. 2.70±0.13, off-line was 2.71±0.15 vs. 2.70±0.10, all P > 0.05); while the DTei of the two groups were decreased after treatment for 3 days of treatment, the difference between the two groups being not statistically significant (mm: 3.27±0.13 vs. 3.26±0.12, P > 0.05), but the levels of DTei in the two groups were significantly increased after treatment for 7 days and after off-line, the differences between the two groups being statistically significant (7 days: 27.26±5.25 vs. 28.74±6.39, off-line: 34.19±4.78 vs. 30.10±2.90, both P < 0.01). There was no significant difference in MV time and ICU time of stay between the two groups [MV time (days): 7.8±1.0 vs. 8.5±1.2, ICU time of stay (days): 11.4±2.6 vs. 12.1±2.8, both P > 0.05], the 14-day success rate of off-line and incidence of VAP were similar in the two groups, and the difference were not statistically significant [14-day success rate off-line: 77.42% (24/31) vs. 70.98% (22/31), incidence of VAP: 6.45% (2/31) vs. 9.68% (3/31), both P > 0.05]. Conclusion The early adequate EN therapy can improve diaphragmatic function and prognosis in MV patients with AECOPD.

Objective To explore the relationship between the serum gamma glutamyl transpeptidase (GGT) level and the prognosis of liver cancer patients undergoing living dornor liver transplantation.Methods A retrospective analysis was made on 139 liver cancer cases receiving living donor liver Tx from Apri 2005 to Oct 2015 in Sichuan University Huaxi Hospital.GGT cut-off value of 71 U/L was calculated from the ROC curve method (sensitivity was 72.2％,specificity was 59.0％).According to the values of the cut-off patients were divided into high GGT group (73 cases) and low GGT group (66 cases),chi-square test was used to judge its correlation with capillary tumor emboli and Kaplan Meier method to determine the overall survival rate difference.Results The tumor vascular invasion,AFP level,tumor size were in disfavour for higher GGT group (P ＜0.05).The 1,3,5 year's survival rates were 59.0％,44.5％,39.7％,for high GGT group,and in low GGT group it was 78.6％,63.6％,63.6％,P =0.010).Conclusions The liver cancer patients with high preoperative serum GGT values have poor long-term prognosis after receiving living donor liver transplantation.

Objective we assessed our institutional outcomes of hybrid treatment for aortic arch disease with supra-aortic debranching and endovascular stent graft repair.Methods From March 2016 to November 2016,6 patients underwent Hybrid total aortic arch repair:1 had aortic arch pseudoaneurysm;1 had type Ⅲ aortic dissection;4 had aortic arch aneurysm because of hypotension,of whom 1 with aneurysm prerupture and 1 with Aortic intramural hematoma.Supra-aortic vessels were involved and high-risk for traditional operations in all patients.Bifurcated artificial vessels were used,main vessel was end-to-side anastomosed with ascending aorta.Branching vessel were end-to-end anastomosed with right innominate artery and left subclavian artery,end-to-side anastomosed with left common carotid artery.Then,stent graft was implanted into ascending aorta and aortic arch.All patients were followed postoperatively,with regularly contrast computed tomography angiogram (CTA) and echocardiography(discharge,three months,six months,and yearly).Results Hybrid procedure with supra-aortic debranching and endovascular stent graft repair were completed in all patients,technical success rate was 100％.There were no perioperative obvious morbidity and mortality,follow-up period were 2-9 months.1 patients had stroke during follow-up period,condition improved after treatment.Supra-aortic vessels were patency and there were no endoleak in all patients.There were no recurrent aortic disease during follow-up period.Conclusion Hybrid aortic arch replacement can be performed with good postoperative and early results in high-risk patients for traditional open repair.

Objective To evaluate the efficiency of the application of array comparative genomic hybridization (array-CGH) in preimplantation genetic diagnosis or screening (PGD/PGS), and compare the clinical outcomes of different stage embryo biopsy. Methods The outcomes of 381 PGD/PGS cycles referred in the First Affiliated Hospital of Nanjing Medical University from July 2011 to August 2015 were retrospectively analyzed. There were 320 PGD cycles with 156 cleavage-stage-biopsy cycles and 164 trophectoderm-biopsy cycles, 61 PGS cycles with 23 cleavage-stage-biopsy cycles and 38 trophectoderm-biopsy cycles.Chromosomal analysis was performed by array-CGH technology combined with whole genome amplification.Single embryo transfer was performed in all transfer cycles.Live birth rate was calculated as the main clinical outcomes. Results The embryo diagnosis rate of PGD/PGS by array-CGH were 96.9%-99.1%. In PGD biopsy cycles, the live birth rate per embryo transfer cycle and live birth rate per embryo biopsy cycle were 50.0%(58/116) and 37.2%(58/156) in cleavage-stage-biopsy group, 67.5%(85/126) and 51.8%(85/164) in trophectoderm-biopsy group (both P<0.01). In PGS biopsy cycles, the live birth rate per embryo transfer cycle and live birth rate per embryo biopsy cycle were the same as 34.8%(8/23) in cleavage-stage-biopsy group, the same as 42.1%(16/38) in trophectoderm-biopsy group (both P>0.05). Conclusions High diagnosis rate and idea live birth rate are achieved in PGD/PGS cycles based on array-CGH technology.The live birth rate of trophectoderm-biopsy group is significantly higher than that of cleavage-stage-biopsy group in PGD cycles;the efficiency of trophectoderm-biopsy is better.