Objective To apply the image recognition method based on distance graph convolutional network to the image processing of malaria-infected erythrocytes for realizing the multi-stage recognition of malaria and improving the diagnostic efficiency of malaria.Methods A multi-stage malaria recognition model based on distance graph convolutional network was proposed.A radial basis function was firstly added in KNN graph construction algorithm to construct adjacency matrix and assign weights to the nearest-neighbor nodes according to the similarity between nodes,so as to weaken the effects of the distant nearest-neighbor nodes on the central node.Then,attention mechanism was introduced to update adjacency matrix dynamically in the graph convolutional network for making the model pay attention to near-neighbor nodes with higher similarity,and finally the multi-stage image recognition of malaria-infected erythrocytes was completed.Results Validated on the Malaria-MIT dataset,the experimental results show that compared with original model,the proposed method improved accuracy,precision,recall rate and F1-score to 96.18%,96.23%,96.18%and 96.18%,respectively.Conclusion The proposed approach can effectively accomplish the task of multi-stage image recognition of malaria-infected erythrocytes.

Objective To apply the image recognition method based on distance graph convolutional network to the image processing of malaria-infected erythrocytes for realizing the multi-stage recognition of malaria and improving the diagnostic efficiency of malaria.Methods A multi-stage malaria recognition model based on distance graph convolutional network was proposed.A radial basis function was firstly added in KNN graph construction algorithm to construct adjacency matrix and assign weights to the nearest-neighbor nodes according to the similarity between nodes,so as to weaken the effects of the distant nearest-neighbor nodes on the central node.Then,attention mechanism was introduced to update adjacency matrix dynamically in the graph convolutional network for making the model pay attention to near-neighbor nodes with higher similarity,and finally the multi-stage image recognition of malaria-infected erythrocytes was completed.Results Validated on the Malaria-MIT dataset,the experimental results show that compared with original model,the proposed method improved accuracy,precision,recall rate and F1-score to 96.18%,96.23%,96.18%and 96.18%,respectively.Conclusion The proposed approach can effectively accomplish the task of multi-stage image recognition of malaria-infected erythrocytes.

To address the challenges of blurred boundaries and feature loss in the segmentation of small liver tumors in CT images,a novel model named BounDer-Net is proposed based on an improved ResUNet architecture.By constructing a Transformer-based dynamic multi-scale encoder and introducing a channel-spatial dual-path attention mechanism,the model can focus on tumor features across multiple dimensions.Additionally,the model adopts boundary-sensitive dynamic feature fusion strategy which effectively captures the heterogeneous features of tumors.BounDer-Net model firstly generates initial feature maps through low-level feature extraction,then inputs the features into a Transformer-based dynamic multi-scale encoder for extracting multi-level features,and finally restores spatial details via a decoder and improves the segmentation accuracy of small tumor boundaries using a boundary enhancement module.Experimental results on the LiTS2017 dataset show that BounDer-Net model achieves a Dice similarity coefficient of 94.64%,a mean intersection over union of 92.34%,and a Hausdorff distance of 0.35 mm,significantly outperforming existing methods.This study provides a reliable solution for the automatic diagnosis of small tumors in liver CT images.

To address the challenges of blurred boundaries and feature loss in the segmentation of small liver tumors in CT images,a novel model named BounDer-Net is proposed based on an improved ResUNet architecture.By constructing a Transformer-based dynamic multi-scale encoder and introducing a channel-spatial dual-path attention mechanism,the model can focus on tumor features across multiple dimensions.Additionally,the model adopts boundary-sensitive dynamic feature fusion strategy which effectively captures the heterogeneous features of tumors.BounDer-Net model firstly generates initial feature maps through low-level feature extraction,then inputs the features into a Transformer-based dynamic multi-scale encoder for extracting multi-level features,and finally restores spatial details via a decoder and improves the segmentation accuracy of small tumor boundaries using a boundary enhancement module.Experimental results on the LiTS2017 dataset show that BounDer-Net model achieves a Dice similarity coefficient of 94.64%,a mean intersection over union of 92.34%,and a Hausdorff distance of 0.35 mm,significantly outperforming existing methods.This study provides a reliable solution for the automatic diagnosis of small tumors in liver CT images.

Objective To explore whether major depressive disorder（MDD）and the therapeutic effect of fluoxetine are related to a functional polymorphism-1019C/G in the promoter region of the 5-HT1A receptor（HTR1A）gene.Methods Genotype and allele frequencies of HTR1A receptor gene-1019C/G polymorphism in MDD patients and healthy subjects（control）were examined by PCR-RFLP technique.Before and after the MDD patients accepted fluoxetine treatment for 6 weeks,17-item Hamilton depression rating scales（HAMD）were made to determine the severity of the symptoms,the outcome and remission status.Results There were significant differences in-1019C/G gene genotypes and alleles distribution between the patients and the healthy control,G allele frequency of the MDD patient was higher than that of the healthy control（P 0.05）.There were significant differences in HAMD scores among the patients with different genotypes in MDD group（P 0.05）,the score of C/C genotype patient was especially higher than that of C/G genotype（P 0.05）and G/G genotype patient（P =0.008）.There was no statistical difference in the therapeutic effect of fluoxetine among the patients with different genotypes in MDD group（P =0.761）.Conclusion HTR1A gene-1019C/G genetic polymorphism might related to MDD,especially G allele might be the possible risk factor of MDD.C allele might be correlated with the degree of pathogenetic severity,especially patients with the-1019C/C carriers.-1019C/G genetic polymorphism was not related to the clinical outcome of MDD patients treated with fluoxetine.

Objective To obtain Chinese hamsterovary (CHO) cell line expressing human decay accelerating activity (hDAF) stably and to observe the protective effect of hDAF on heterologous cells under the circumstance of complement activation. Methods The eukaryotic expression vector DAF pcDNA3.1 was constructed and then transfected into CHO cells by lipofection. Monoclones of cells expressing hDAF stably were screened by the method of limiting dilution. hDAF expression was detected by flow cytometry. The decay accelerating activity of hDAF was determined by assay of C3 deposition and 51Cr release. Results The expression vector DAF pcDNA3.1 was successfully constructed, and monoclones of cells expressing hDAF were obtained. CHO cells expressing hDAF could decrease C3 deposition and attenuate the killing effect of activation of the complement system. Conclusion We have obtained CHO cell clones expressing hDAF stably, which is helpful for the further studies of the relationship of the structure with the functions of hDAF.