Objective To compare the results of conservative treatment operation for two groups of tubal pregnancy with laparoscopy and laparotomy. Methods We disparted tubal pregnancy patients into two groups: laparoscopy group(418 cases from August 2001 to December 2004) and laparotomy group(182 cases from January 1998 to July 2001) ,and evaluated the clinical characteristics,the postoperative incidences of persistent ectopic pregnancy, the rate of intra-uterine pregnancy and the rate of ectopic pregnancy. Results In the laparoscopy group,only 1 case arised the postoperative incidences of persistent ectopic pregnancy. In the laparotomy group,6(3. 3% ) out of 182 arised(P 0. 05). Conclusion Laparoscopy has advantage over laparotomy therapy in the conservative operation of tubal pregnancy.

BACKGROUND:Peridental membrane distraction osteogenesis exerts the functions at peridental membrane and leads to tooth movement. Alveolar bone distraction osteogenesis produces tooth movement through the displacement of the whole bone plate.
OBJECTIVE:To establish three-dimensional finite element model of upper and lower jaw of healthy adults under three different conditions, and to compare the stress distribution and distal movement of the models using three-dimensional finite element analysis method.
METHODS:Model 1:three-dimensional finite element model of the canine under normal conditions using a
variety of software;Model 2:three-dimensional finite element model of the canine after distracting osteggenesis of the periodontal ligament;Model 3, three-dimensional finite element model of the canine after reducing resistance and distracting osteggenesis of the alveolar bone. The force loadings were stimulated among these models.
RESULTS AND CONCLUSION:The biggest displacements on those three models occurred in canine crown on 1/3, and the displacement quantity on canine was model 2>model 3>model 1. The biggest equivalent stress concentrated in distal alveolar crest, and the equivalent stress was model 2

Objective: To classify the characteristics of circadian type among clinical nurses and examine their relationships with presenteeism and work-related flow. Methods: Using a cross-sectional design, 568 nurses were recruited through convenience sampling in January 2021 from three hospitals in Shandong Province, China. The data were collected using self-report measures, including the 11-item Circadian Type Inventory, Stanford Presenteeism Scale-6, and Work-Related Flow Inventory. Latent class analysis was performed to identify any clustering of circadian types. One-way analysis was performed to compare the differences between presenteeism and work-related flow in different circadian types. Results: Four latent classes were identified, including high response class (14.4%), high flexible class (20.1%), high languid class (51.1%), and low response class (14.4%). Regarding presenteeism, the high languid class had higher scores than others. Regarding work-related flow, the scores of high flexible class were higher than those of high languid class, while the differences in all three dimensions were statistically significant. Conclusion Although the shift work mode is not expected to change, nursing managers could use circadian type as a predictive index to select and employ individuals for shift work to enhance work performance and provide sufficient support to staff who are intolerant to shift work.

Objective:To systematically integrate the real life experience of patients after liver transplantation, so as to discover the real thoughts and challenges of patients and provide a reference for the formulation of continuous nursing plan.Methods:Qualitative researches on real life experience of patients after liver transplantation were searched through PubMed, Cochrane Library, Web of Science, Embase, China National Knowledge Infrastructure, WanFang Data, VIP and China Biology Medicine disc. The retrieval period was from the establishment of database to December 31, 2021. The Joanna Briggs Institute of Australia Qualitative Research Quality Evaluation criteria for Evidence-Based health care centers was used to evaluate the quality of studies. The results were integrated by integrating methods.Results:A total of 15 studies were included, 34 results were extracted, 7 categories were classified, and 4 integrated results were synthesized: subjective experience, physical-level experience, social-family-level experience and spiritual-life-level experience.Conclusions:Patients after liver transplantation face many challenges in psychological, physiological, social, and life. Hospitals, communities and families should pay attention to their problems and needs, provide support and help, so as to help patients cope with challenges smoothly, improve postoperative satisfaction and quality of life.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

The rapid development of artificial intelligence put forward higher requirements for the computational speed, resource consumption and the biological interpretation of computational neuroscience. Spiking neuron networks can carry a large amount of information, and realize the imitation of brain information processing. However, its hardware is an important way to realize its powerful computing ability, and it is also a challenging technical problem. The memristor currently is the electronic devices that functions closest to the neuron synapse, and able to respond to spike voltage in a highly similar spike timing dependent plasticity (STDP) mechanism with a biological brain, and has become a research hotspot to construct spiking neuron networks hardware circuit in recent years. Through consulting the relevant literature at home and abroad, this paper has made a thorough understanding and introduction to the research work of the spiking neuron networks based on the memristor in recent years.