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 analyze the composition and function of residual cells in pre-transplantation human thymic slices by single-cell transcriptomics sequencing(scRNA-seq),and established a quality assessment method for thymic slices based on the expression levels of molecular markers in the culture supernatant.Methods The discarded thymus from 18 patients with congenital heart disease undergoing surgical treatment in Department of Cardiothoracic Surgery of Children's Hospital Affiliated to Chongqing Medical University from May 2023 to January 2024 were collected and prepared into thymic slices.After the slices were cultured in vitro for 14 d,scRNA-seq was employed to identify the residual cell types,and gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis was performed to analyze the biological function of the residual cells.Then based on the literature concerning thymic slice culture,the molecular markers indicating thymocyte function were screened out.ELISA was applied to detect the changes in protein levels of molecular markers in the supernatant.Receiver operating characteristic(ROC)curve was plotted and assess the value of the molecular markers in the supernatant in evaluating the quality of thymic slices with area under the curve(AUC).Then,the qualified and unqualified thymic slices determined by our obtained molecular markers were transplanted subcutaneously into male nude mice(6～8 weeks old,weighing 14～17 g),respectively,and the male nude mice without transplantation of the thymic slices served as control group.Flow cytometry and histologic analysis were utilized to observe the immune reconstitution after transplantation.Results ① scRNA-seq identified 11 cell types in thymic slices,dominated with epithelial cells,fibroblasts,and T cells.GO and KEGG enrichment analysis showed that epithelial cells were involved in enrichment entries related to chemotaxis,epithelial cell development,cell matrix adhesion and tight junction;fibroblasts were involved in enrichment entries related to extracellular matrix,epithelial cell proliferation,negative regulation of cell migration,and regulation of actin cytoskeleton;T cells were mainly related to T cell differentiation,regulation of T cell activation,T cell apoptosis,and T cell receptor signaling.② Molecular markers,CCL19,CCL21,CXCL12,CXCL16,IL16 and SELL were identified to indicate thymocyte function.Compared with the levels of the first day,the protein secretions of CCL19,CCL21,CXCL12 and CXCL16 were significantly increased during in vitro culture(P<0.05),while the protein secretions of IL16 and L-selectin(protein form of SELL)were significantly decreased(P<0.05).The combined predictor Pre1 from subset of cytokines(IL16 and L-selectin)had the highest value in the quality assessment of thymic slices after 1 d of culture(AUC=0.883),and the combined predictor Pre2 from subset of cytokines(CCL19,CCL21,CXCL12 and CXCL16)had the highest value in the quality assessment after 14 d of culture(AUC=0.948).③ Transplantation in nude mice indicated that the qualified thymic slices could develop to thymus structure in vivo,and effectively increase the proportion of T cells in peripheral blood(P<0.01),while the unqualified thymic slices could not obtain the reconstitution of T cell development.Conclusion The main residual component cells in thymic slices are epithelial cells,fibroblasts and T cells.IL16 and L-selectin can be used as potential indicators to determine the quality of donor thymic samples.CCL19,CCL21,CXCL12 and CXCL16 can effectively evaluate the quality of thymic slices before transplantation.

Objective To explore the potential role and underlying mechanism of the functionally uncharacterized gene Gm49394 on regulating β-cell apoptosis under diabetic conditions.Methods The expression and translational activity of Gm49394 in pancreatic β-cell lines and non-β-cell lines were validated using RNA fluorescence in situ hybridization(RNA-FISH),quantitative real-time PCR(qPCR),Western blotting,and immunofluorescence(IF)assay.The β-cell lines(NIT-1/Min6)with Gm49394 overexpression or knockdown were constructed.The proliferation,apoptosis,mitochondrial function,as well as oxidative stress and endoplasmic reticulum stress markers in these β-cell lines under physiological homeostasis or pathological stress conditions,such as high glucose(30 mmol/L),inflammation(10 ng/mL IFN-γ alone or combined with 10 ng/mL IL-6),and hydrogen peroxide(100 μmol/L H2O2)were detected by flow cytometry and Western blotting.Results RNA-FISH and qPCR indicated that Gm49394 was specifically expressed in pancreatic β-cell lines and up-regulated under high glucose or inflammatory stimulation.IF assay and Western blotting showed that Gm49394 had protein-coding activity.Flow cytometry and Western blotting identified that Gm49394 overexpression did not affect β-cell proliferation,but promoted β-cell apoptosis and increased reactive oxygen species(ROS)and mitochondrial superoxide(MitoSOX)levels in β cells under physiological homeostasis or pathological stress conditions(P<0.05).Under physiological conditions,Gm49394 knockdown failed to induce significant alterations on β-cell apoptosis,ROS,or MitoSOX levels.Under pathological stress conditions,Gm49394 knockdown significantly suppressed β-cell proliferation,apoptosis,as well as oxidative and endoplasmic reticulum stress(P<0.05).Conclusion Gm49394 may promote β-cell apoptosis via oxidative stress and endoplasmic reticulum stress.

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 investigate the value of serum IgG4 detection in diagnosis,treatment,evaluation and outcome of IgG4-associated diseases(IgG4-RD).Methods:By integrating the clinical data of patients,retrospectively and systematically analyze the results of common laboratory examinations,imaging examinations,pathological examinations,etc.26 patients who were diagnosed with IgG4-RD in Huaian First Hospital Affiliated to Nanjing Medical University from March 2013 to October 2023.Results:The median age of onset was 65(31～90)years old,and the male to female ratio was 3.33∶1.The first three symptoms were abdominal discomfort,skin scleral yellow stain and fever.Patients mainly involved multiple organs,the most commonly involved organs were lung,pancreas,liver,kidney,bile duct.Compared with the control group,NLR,PLR,ESR,CRP,TBIL,DBIL,ALT,AST,GGT,IgG,IgA and IgM were all abnormally elevated in the first hematological,biochemical and immune tests of the patients,the difference was statisti-cally significant,while C3 and C4 were all decreased,but there was no statistical difference.The first IgG4 level of IgG4-RD patients was correlated with the length of hospitalization,the length of return visit,and the dynamic changes of IgG4 level after return visit.Conclusion:Serum IgG4 detection plays an important role in the diagnosis and treatment of IgG4-RD.In patients with abnormally high level of IgG4,those who are sensitive to high-dose glucocorticoid therapy have a faster decline in IgG4 levels and a longer duration of symptom remission.

Objective To development and validate a predictive model for distinguishing between malignant pleural effusion(MPE)and benign pleural effusion(BPE).Methods A total of 428 patients diagnosed with pleural effusion(PE)and hospitalized at the First Hospital of Huai'an Affiliated to Nanjing Medical University from July 2020 to May 2022 were selected.The patients were divided into BPE group(211 cases)and MPE group(217 cases)according to diagnostic criteria.The basic information and clinical data of these patients were collected.Boruta method was used for univariate screening,followed by multivariate Logistic regression to construct a basic nomogram model.Bootstrap method was used for internal validation to evaluate the performance of the nomogram,including dis-crimination,accuracy,and clinical applicability.Results The model included 8 key variables:dyspnea,chest pain,general symp-toms,X-ray/CT with malignant tumor features,serum carcinoembryonic antigen,serum neuron-specific enolase,pleural lactate dehy-drogenase,and pleural carcinoembryonic antigen.Internal validation showed that the area under the receiver operating characteristic curve(AUCROC)of the model was 0.933(95%confidence interval:0.912-0.954),with good accuracy(P＞0.05).Decision curve a-nalysis(DCA)indicated that this predictive model for predicting MPE risk had a significant net benefit when the probability threshold exceeded 1%.Conclusion The constructed prediction model could effectively distinguish between MPE and BPE.

Core competitiveness of medical colleges and universities refers to their unique advantages and abilities in teaching,research and serving society compared to other colleges.As an important part of core competitiveness of medical colleges,teachers'teaching ability directly affects students'learning effect and quality training,and also relates to overall promotion process of core com-petitiveness of medical colleges.From perspective of core competitiveness of medical colleges,this paper analyzes and combs develop-ment of teachers'teaching ability from theoretical definition of core competitiveness of medical colleges,finds bottleneck of improving teachers'teaching ability,and puts forward suggestions for improving teachers'teaching ability in medical colleges,so as to improve teachers'teaching ability in medical colleges.

Core competitiveness of medical colleges and universities refers to their unique advantages and abilities in teaching,research and serving society compared to other colleges.As an important part of core competitiveness of medical colleges,teachers'teaching ability directly affects students'learning effect and quality training,and also relates to overall promotion process of core com-petitiveness of medical colleges.From perspective of core competitiveness of medical colleges,this paper analyzes and combs develop-ment of teachers'teaching ability from theoretical definition of core competitiveness of medical colleges,finds bottleneck of improving teachers'teaching ability,and puts forward suggestions for improving teachers'teaching ability in medical colleges,so as to improve teachers'teaching ability in medical colleges.