Objective: To study the effect of comprehensive intervention on life quality and prognosis of inpatients with schizophrenia.Methods: 126 inpatients with first episode schizophrenia were randomly divided into intervention group (n=62) and control group (n=64). Apart from antipsychotic medication, the intervention group received psychosocial help for 8 weeks. Both groups were assessed after one year of discharge. The assessments included PANSS, WHO QOL-100, and records of the rates of relapse and readmission.Results:Compared with the records when discharged, both the intervention group and control group improved in the total score, negative score and positive score of PANSS in the follow-up, with intervention group having greater improvement than control. Same results were got in quality of life. The rates of relapse and readmission were lower in intervention group.Conclusions:Psychosocial help in hospital can benefit schizophrenic inpatients after their discharge.

OBJECTIVE:To prepare Activated carbon N-acetylcysteine microcapsule (ACNAC),and to optimize preparation technology. METHODS:ACNAC was prepared by emulsion cross-linked method using biodegradable material gelatin as capsule wall material. Using comprehensive evaluation index of drug-loading amount,entrapment rate and particle size distribution percent-age(the percentage of 80-140 μm particle)as index,drug-loading ratio,amount of gelatin,mixing speed and the amount of emul-sifier as factors,single factor test and orthogonal test were used to optimize formulation technology. The technology was validated and distribution of particle size of ACNAC was determined. RESULTS:The optimal formulation technology was as follows as drug-loading ratio 1∶1,gelatin 15%,emulsifier 2.0%,mixing speed 1 000 r/min. Average drug-loading amount of 6 batches of ACNAC was 15.9%(RSD=1.21%),average encapsulation efficiency was 78.1%(RSD=1.11%)and average particle size distri-bution percentage was 81.9%. CONCLUSIONS:ACNAC is prepared successfully,and formulation technology is reasonable and feasible.

Objective To compare the dosimetric data between preoperative plans and postoperative verification in computed tomography (CT)-guided and 3D-printing non-coplanar templateassisted 125I seed implantation for pelvic tumor,and to explore the feasibility and accuracy of the personalized template designmethod.Methods A total of 51 patients registered from Dec 2015 to Dec 2016 who were applied with 3D-printing guided template assisted radioactive seed implantations in the hospital were included in this study.A prescribed dose of 110-160 Gy was adopted.3D-printing templates were designed and produced for 51 cases.The dosimetric parameters:Dg0,minimum peripheral dose (mPD),V100,V150,V200,conformal index (CI),external index (EI),and homogeneity index (HI) were compared between pre-and post-plans.Results 51 cases' templates were in place well during the operations.Compared with the preoperative planning,the postoperative D90,V100,V150,V200,CI,EI and HI differences had no statistical difference (P ＞ 0.05);mPD is larger than before (t =-2.96,P ＜ 0.05).Conclusions The main dosimetric parameters of postoperative verification were consistent well with the preoperative planning and have good accuracy,which could meet the clinical requirements.

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.