Through analyzing the ethical status quo in reproductive medicine treatment unit, this article dis-cussed the social ethical issues brought by ART technology development related to the reproductive medical treat-ment unit. Then it put forward the measures to solve those ethical problems: strengthening the ethical principles and ethical committee construction, sharing data information, fully informed consent, and suggestions to improving ethics education.

Objective To summarize the anesthesia management and surgical characteristic of modified extended Morrow procedure in treating hypertrophic obstructive cardiomyopathy (HOCM). Methods This retrospective study was conducted in 139 patients (male 83 and female 56)aged from 1 1 to 66 years.They underwent general anesthesia by high dose intravenous fentanyl or sufentanyl combined with propofol infusion and low concentration sevoflurane inhalation.The surgeons chose ap-propriate surgical procedures,including modified extended Morrow procedure,or combined with mi-tral valve repair (replacement)and coronary artery bypass grafting,etc.All patients received intraop-erative transesophageal echocardiography (TEE ) or epicardial echocardiography monitoring. Results There was no death case in hospital.All patients were hemodynamically stable and there were no malignant adverse events such as ventricular fibrillation during perioperative period.70 (50.4%)of patients automatically recovered to cardiac rhythm after aortic unclamping,and 1 1 (7.9%)of patients needed temporary pacemaker due to atrioventricular block.Cardiopulmonary by-pass (CPB)were weaned off successfully without positive inotropic drugs in 103(74.1%)of patients, while others 36(25.9%)needed low dose dopamine,epinephrine or norepinephrine to maintain hemo-dynamics stable.The CPB time was (142 ± 5 1 )min and the time of aortic clamping was (96 ± 37 ) min.Blood protection was used in all patients and 129 (92.8%)of the patients didn’t receive any blood product.Conclusion Modified extended Morrow procedure was a safe and effective surgical pro-cedure for treatment of HOCM.Experienced teamwork was essential to achieve satisfactory clinical results.The key points of anesthesia management were administration of appropriate preoperative drugs,maintaining adequate anesthesia depth,appropriate preload and afterload,heart rate and rhythm.Intraoperative TEE monitoring was the golden standard for guiding and evaluating the effec-tiveness of the surgical procedures.Meanwhile protection of myocardium,lung,brain and blood can help to obtain satisfactory clinical outcomes.

Objective:To explore the effect of hydrotherapy combined with breathing training on lung function and mobility of patients with thoracolumbar spinal cord injury.Methods:A total of 80 patients with thoracolumbar spinal cord injury admitted to Beijing Rehabilitation Hospital Affiliated to Capital Medical University from April 2018 to March 2020 were selected as observation objects.A prospective cohort study was conducted and randomly divided into observation group and control group with 40 cases in each group.The control group was given routine rehabilitation therapy combined with respiratory training.On the basis of the control group, the observation group was treated with water therapy.The indexes of lung function, motor function, lower limb muscle tension, function evaluation and activities of daily living were compared between the two groups.Results:After intervention, the observation group′s forced vital capacity (FVC) was (3.86±0.82) L, the forced expiratory volume in the first second (FEV1) was (3.76±0.68) L, the maximum ventilation (MVV) was (102.34±10.38) L/min, the maximum suction pressure (MIP) is (50.36±4.62) cmH 2O; the control group FVC was (3.41±0.76) L, and FEV1 was ( 3.35±0.63) L, MVV was (90.67±11.68) L/min, MIP was (44.38±4.85) cmH 2O, the difference between the two groups is statistically significant ( t=2.546, 2.797, 4.723, 5.646, respectively, all P<0.05). After the intervention, the motor function score of the american spinal injury association (ASIA) of the observation group was (58.62±7.56) points, and the modified ashworth scale (MAS) score was (2.74±0.89) points; The ASIA motor function score of the control group was (42.24±6.40) points, and the MAS score was (3.36±0.94) points.The difference between the two groups was statistically significant ( t=10.459 and -3.029, respectively, all P<0.05). After intervention, the observation group′s spinal cord independence measure (SCIM III) score was (75.33±10.72) points, and the modified barthel index (MBI) was (66.64±6.34) points; the SCIM III score of the control group was (68.34±9.55) points, and the MBI score was (57.52±6.77) points, the difference between the two groups was statistically significant ( t=3.079 and 6.219, respectively, all P<0.05). Conclusion:Hydrotherapy combined with breathing training can significantly improve lung function and respiratory muscle strength in patients with thoracolumbar spinal cord injury, and improve motor function and ability of daily living.

Objective:To investigate the effect of different plasticizers on the compressive strength of injectable calcium sulfate cement (ICSC).Methods:Hydroxymethylcellulose (CMC), methylcellulose (MC), hyaluronic acid (HA), stearic acid (SA) and self-made hemihydrate calcium sulfate were used to mix them, and the compressive strength of the complex was observed after reaction with normal saline; the changes of ICSC's own properties, such as setting time and injectability, were recorded.Results:The compressive strength of calcium sulfate was 21±4.58 mPa, and the final setting time was 3.86 ± 0.09 min. Different concentrations of SA had no significant effect on the compressive strength of calcium sulfate ( F=1.593, P=0.266), but prolonged the setting time ( F=29.868, P=0.000). CMC with different concentrations significantly reduced the compressive strength of calcium sulfate ( F=23.943, P=0.000), and the setting time was prolonged to more than 120 min. Different concentrations of MC can improve the compressive strength of calcium sulfate ( F=4.808, P=0.034), and prolong the setting time ( F=191.192, P=0.000); among them, 1% and 3% MC can significantly improve the compressive strength ( P=0.007, 0.027). Different concentrations of HA can improve the compressive strength ( F=3.818, P=0.058), and prolong the setting time ( F=262.515, P=0.000), of which 3% and 5% were significantly improved ( P=0.026, 0.015), while 1% group was not significantly improved ( P=0.062). In addition, the injectable properties of HA, MC, stearic acid and CMC are better, respectively. Conclusion:SA and CMC can not be used to improve the compressive strength of calcium sulfate, while HA and MC of appropriate concentration can improve the compressive strength of ICSC, and improve the injectable performance, but MC can make the coagulation time more in line with clinical needs.

Objective To study the efficacy of laparoscopic tumor resection combined with iodine-125 and radiofrequency ablation in the treatment of rectal carcinoma with synchronous hepatic metastasis. Methods There were 30 patients diagnosed as rectal carcinoma with synchronous hepatic metastasis detected by CT scan. Hepatic metastases were confirmed by needle biopsy under laparoscopy. Laparoscopic radical resection of rectal carcinoma and metastatic hepatic tumors was performed. Those metastatic tumors that could not be resected were managed by RAF. Iodine-125 was planted in the tumors' site. Results Seven new hepatic metastases were found by the laparoscopic ulstrasound during the operation. 8 hepatic metastatic lesions were removed, 25 tumors located in the right liver were managed by RAF. All patients were followed-up from 12 to 25 months(average 22. 3 months), Local recurrence was found in 6 patients, the 1-year survival rate was 73% (22/30). Conclusions Laparoscopic excision, Iodine-125 and radiofrequency ablation in the treatment of rectal carcinoma with synchronous hepatic metastasis is safe、effective、minimally invasive.

Objective To construct a 3D ResNet deep learning model based on multi-parametric prostate MRI(mpMRI),and to observe its value for automatically identifying the main MR sequences.Methods Totally 1 153 sets pre-biopsy prostate mpMRI data of 1 086 patients who underwent ultrasound-guided prostate biopsy in 3 hospitals were collected and divided into different image datasets,i.e.T2WI,diffusion-weighted imaging(DWI)and apparent diffusion coefficient(ADC)maps with a total of 5 151 images.Then the images were categorized into non-fat-suppressed T2WI(T2WI_nan,n=1 000),fat-suppressed T2WI(T2WI_fs,n=1 188),high b-value DWI(DWI_High,b-value≥500 s/mm2,n=1 045),low b-value DWI(DWI_Low,b-value＜500 s/mm2,n=1 012)or ADC map(n=906),also divided into training set(n=4 122),verification set(n=513)and test set(n=516)at the ratio of 8∶1∶1.After preprocessing and augmentation,a 3D ResNet model for automatically identifying image categories was trained and optimized in the training and verification sets,and its classification efficiency was evaluated in the test set.Results The identifying accuracy,sensitivity,specificity,positive predictive value,negative predictive value,F1 score and Kappa value of the obtained model for automatically identifying categories of images in the test set was 0.995-1.000,0.990-1.000,0.998-1.000,0.990-1.000,0.998-1.000,0.995-1.000 and 0.994-1.000,respectively.Conclusion The obtained 3D ResNet deep learning model could effectively and automatically identify the main sequences of prostate mpMRI.

Purpose To explore the feasibility of the deep learning-based segmentation of extra-pelvic region and metastases in advanced prostate cancer based on metastasis reporting and data system for prostate cancer(MET-RADS-P).Materials and Methods Four datasets(68,91,57 and 263 patients with head,neck,chest and abdomen metastases,respectively)from Jan 2017 to Jan 2022 in Peking University First Hospital were retrospectively collected for the development of the classification model of scanning range and segmentation model of different regions and metastases according to the scanning sites(head,neck,chest and abdomen).In addition,90 patients with prostate cancer confirmed by pathology and underwent whole-body MRI were collected for external validation of the developed model.The manual annotation of the regions and metastases were used as the"reference standard"for the model evaluation.The evaluation indexes included dice similarity coefficient(DSC)and volumetric similarity(VS).Results In the external validation set,the classification accuracy of head,neck,chest and abdomen were 100%(90/90),98.89%(89/90),96.67%(87/90)and 94.44%(85/90),respectively.The range of DSC,VS values of the segmentation model for organs in different regions were(0.86±0.10)-(0.99±0.01),(0.89±0.10)-(0.99±0.01),respectively.The range of DSC,VS values of the segmentation model for metastases in different regions were(0.65±0.07)-(0.72±0.13),(0.74±0.04)-(0.82±0.13),respectively.Conclusion The 3D U-Net model based on deep learning may achieve the segmentation of extra-pelvic region and metastasis in advanced prostate cancer.

Objective To develop a 3D V-Net deep learning segmentation model based on multi-center data,and to evaluate its value for automatic segmentation of prostate on T2WI and apparent diffusion coefficient(ADC)maps.Methods Totally 2 894 sets of multi-parametric MRI data of 2 673 patients with clinically suspected prostate cancer from 3 medical centers within 1 month before biopsy were retrospectively collected.Finally 5 974 sets axial images were enrolled,including 3 654 sets of T2WI and 2 320 sets of ADC maps.Prostate contours were manually annotated layer by layer on axial T2WI and ADC maps,and the left-to-right,anterior-to-posterior,superior-to-inferior diameters and volume of prostate were measured and taken as reference standards.The images were divided into training set(n=4 780,including 2 907 sets of T2WI and 1 873 sets of ADC map),verification set(n=601,including 384 sets of T2WI and 217 sets of ADC map)and test set(n=593,including 363 sets of T2WI and 230 sets of ADC map)at the ratio of 8:1:1.After preprocessing and augmentation,3D V-Net was used to construct and train the segmentation model based on training and verification sets,and the segmentation performance of the model was evaluated in test set using Dice similarity coefficient(DSC),Jaccard coefficient(JACARD)and volume similarity(VS),respectively.The parameters measured with the model were compared with the reference standards,and the correlations were explored.Results Compared with the corresponding ADC maps,DSC,JACARD and VS of the model for automatic segmentation of prostate on T2WI in test set were all higher(all P＜0.001).The left-to-right,anterior-to-posterior and superior-to-inferior diameters of prostate measured with the model on both T2WI and ADC maps were all larger than the reference standards(all P＜0.001),while no significant difference of the volume was found(both P＞0.05).All parameters measured with the model on T2WI and ADC maps were positively correlated with reference standards(rs=0.794-0.985).Conclusion 3D V-Net deep learning model could automatically segment prostate on T2WI and ADC maps with high accuracy,and its efficiency based on T2WI was better than that based on ADC maps.

Bioactive glasses (BGs) are a kind of biomaterials with osteoconductive and osteoinductive properties and are able to create a strong bond with host bone and promote osteogenesis after implantation. According to their compositions, bioactive glasses can be classified as silicate BGs, phosphate BGs, and borate BGs. Nowadays, silicate BGs are still the most common, while phosphate BGs and borate BGs have higher dissolution and degradation rates. Melt?quenching and sol?gel process are two basic methods to produce melt?derived BGs and sol?gel BGs, respectively. The latter requires lower heat treatment temperature with higher specific surface area and biological activity. Bioactive glass?ceramics can be obtained by heat treatment, which improves the mechanical strength but slightly reduces the bioactivity. Nano?bioactive glasses with the higher specific surface area can be ob?tained by changing the structure size of the materials by other treatment methods. On this basis, 3D BGs scaffolds can be made, and hybrid BGs scaffolds as well by combining with other biomaterials to obtain the 3D interconnected pores with the hierarchical or bionic structures, to enhance the mechanical strength, osteogenic activity and provide mechanical support suitable for the host bone. However, the bioactivity of BGs depends on the degradation rate, to some extent, which is contradictory to the mechanical strength. An appropriate porosity or controllable degradation rate can be selected to meet the common needs of early support and osteogenesis. In basic studies, it was found that BGs could act on cells by releasing ions or through the macropinocytosis pathway, up?regulating the expression of related genes or promoting osteogenesis. The degradation rates of BGs are related to their struc?tures and compositions, which enables the quantitative prediction of the change of mechanical strength during degradation. Prog?ress has also been made in structural mechanics and testing methods.

Objective To explore the feasibility of automating the measurement of abduction angle after total hip arthroplasty(THA)on postoperative radiographs by using deep learning algorithms.Methods The data were retrospectively collected.A total of 381 cases were used to develop deep learning model.Two radiologists annotated the key points on the images(lateral-superior point and medial-inferior point of acetabular cups,tear drops).The data was split into training dataset(304 cases),tuning dataset(38 cases),and test dataset(39 cases).A 2D U-net model was trained to segment the key points and the abduction angle were automatically meas-ured.After development of the model,an external validation dataset was collected(143 cases).Dice similarity coefficient(DSC)and mean absolute error(MAE)were used to evaluate the prediction efficiency of the model in the test dataset and the external validation dataset.Bland-Altman test was used to analyze the agreement between the abduction angle measured automatically by the model and the physician measurement.Results The DSC were 0.870-0.905 and 0.690-0.750 in the test dataset and the external validation dataset,and the corresponding MAE were 0.311-0.561 and 0.951-1.310.For the result of Bland-Altman analysis,only 6.52％(3/46)and 2.08％(3/144)of the abduction angle measurements in the test dataset and external validation dataset were outside the 95％limit of agreement(LoA).In the qualitative evaluation of the abduc-tion angle,the agreement of the model with the physician were 97.8％and 90.3％in the test dataset and the external validation dataset.Conclusion It is feasible to use deep learning algorithms to automatically measure the abduction angle after THA on X-ray images,achieving similar accuracy to that of physician.