This study was aimed to investigate the quantity and subtypes of dendritic cells (DC) in patients with immune related pancytopenia (IRP) and to explore the role of DC in pathogenesis of IRP. The quantity of plasmacytoid dendritic cells (pDC, Lin(-)HLA-DR(+) CD123(+) cells) and myeloid dendritic cells (mDC, Lin(-)HLA-DR(+) CD11c(+)cells) in peripheral blood of 65 patients with IRP (37 new diagnosed and 28 remitted) and 17 healthy controls were analyzed by flow cytometry. The results indicated that the ratio of pDC in peripheral blood mononuclear cells (PBMNC) was (0.91 ± 064)% in new diagnosed group, which was significantly higher than that in remission group (0.39 ± 0.11)% and control group (0.29 ± 0.13)% (P < 0.01), while this ratio of pDC in remission group was higher than that in control group (P < 0.05). The ratio of mDC in PBMNC was (0.21 ± 0.20)% in new diagnosed group and (0.34 ± 0.21)% in remission group respectively, there was no statistical difference as compared with control group (0.29 ± 0.09)% (P > 0.05). The ratio of pDC to mDC in new diagnosed group was 6.75 ± 7.11, which was significantly higher than that in remission group (1.55 ± 0.93) and control group (1.07 ± 0.43, P < 0.01), there was no statistical difference between the ratio of remission group and control group (P > 0.05). The ratio of pDC in PBMNC of IRP group negatively correlated to ratio of Th1/Th2 (r = -0.347, P < 0.05), and positively correlated to the ratio of auto-antibody on membrane of BMMNC (r = 0.606, P < 0.05) and to the quantity of CD5(+)B cells (r = 0.709, P < 0.05), while it negatively correlated to the levels of hemoglobin (r = -0.381, P < 0.01) and platelets (r = -0.343, P < 0.01). The ratio of mDC in PBMNC positively correlated to the ratio of Th1/Th2 (r = 0.595, P < 0.05) and the level of hemoglobin (r = 0.292, P < 0.05). The ratio of pDC/mDC negatively correlated to ratio of Th1/Th2 (r = -0.395, P < 0.05), it positively correlated to the level of antibody on membrane of BMMNC (r = 0.421, P < 0.05) and the quantity of CD5(+)B cells (r = 0.423, P < 0.05), while it negatively correlated to the levels of hemoglobin (r = -0.304, P < 0.05) and platelets (r = -0.287, P < 0.05). It is concluded that the quantity of pDC in peripheral blood of IRP patients increases, which may be related to the immunopathogenesis of IRP.
Adolescent ; Adult ; Blood Cell Count ; Case-Control Studies ; Child ; Child, Preschool ; Dendritic Cells ; cytology ; immunology ; Female ; Flow Cytometry ; Humans ; Male ; Middle Aged ; Pancytopenia ; blood ; immunology ; Young Adult

OBJECTIVETo investigate the function of dendritic cells (DC) of patients with immune related pancytopenia (IRP) and explore the role of DC in IRP.

METHODSThe expression of CD80 and CD86 on myeloid DC (mDC, Lin-HLA-DR(+) CD11c(+) cells) and plasmacytoid DC (pDC, Lin-HLA-DR(+) CD123(+) cells) of 65 IRP (37 untreated and 28 remitted) patients and 17 healthy controls were analyzed by flow cytometry.

RESULTSThe expression of CD86 on pDC was (82.47 ± 13.17)% in untreated group and (60.08 ± 14.29)% in remission group, which were significantly higher than that of controls (47.95 ± 18.59)% (P < 0.05), while the expression in untreated group was higher than that of remission group (P < 0.05). The expression of CD80 on pDC was (6.31 ± 4.49)% in untreated group, which was significantly higher than that of remitted patients (3.09 ± 2.93)% and controls (2.33 ± 2.25)% (P < 0.05). The expression of CD86 on mDC was (97.06 ± 4.82)% in untreated group and (91.35 ± 12.20)% in control group, while the expression in untreated group was higher than that of control group (P < 0.05). The expression of CD80 on mDC was (6.20 ± 5.44)% in untreated group and (3.97 ± 3.24)% in remission group, which were significantly higher than that of controls (1.86 ± 1.73)% (P < 0.05). The expression of CD86 on pDC was negatively correlated to Th1/Th2 (r = -0.733, P < 0.05), it was positively correlated to the antibody on membrane of BMMNC (r = 0.283, P < 0.05) and the quantity of CD5(+)B cells (r = 0.436, P < 0.05), while it was negatively correlated to the level of hemoglobin, platelets and white blood cells (r = -0.539, P < 0.05; r = -0.519, P < 0.05; r = -0.567, P < 0.05, respectively). The expression of CD80 on pDC was negatively correlated to the level of hemoglobin and platelets (r = -0.431, P < 0.05; r = -0.464, P < 0.05).

CONCLUSIONThe function of pDC in PB of IRP were strengthened, which was relevant to the immunopathogenesis of IRP.

Adolescent ; Adult ; Autoimmune Diseases ; complications ; B7-1 Antigen ; metabolism ; B7-2 Antigen ; metabolism ; Case-Control Studies ; Child ; Child, Preschool ; Dendritic Cells ; metabolism ; Female ; Flow Cytometry ; Humans ; Male ; Middle Aged ; Pancytopenia ; blood ; etiology ; pathology ; Young Adult

OBJECTIVE: To screen differentially expressed gene (DEG) related to myelodysplastic syndrome (MDS) based on Gene Expression Omnibus (GEO) database, and explore the core genes and pathogenesis of MDS by analyzing the biological functions and related signaling pathways of DEG. METHODS: The expression profiles of GSE4619, GSE19429, GSE58831 including MDS patients and normal controls were downloaded from GEO database. The gene expression analysis tool (GEO2R) of GEO database was used to screen DEG according to | log FC (fold change) |≥1 and P<0.01. David online database was used to annotate gene ontology function (GO). Metascape online database was used to enrich and analyze differential genes in Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction network (PPI) was constructed by using STRING database. CytoHubba and Mcode plug-ins of Cytoscape were used to analyze the key gene clusters and hub genes. R language was used to diagnose hub genes and draw the ROC curve. GSEA enrichment analysis was performed on GSE19429 according to the expression of LEF1. RESULTS: A total of 74 co-DEG were identified, including 14 up-regulated genes and 60 down regulated genes. GO enrichment analysis indicated that BP of down regulated genes was mainly enriched in the transcription and regulation of RNA polymerase II promoter, negative regulation of cell proliferation, and immune response. CC of down regulated genes was mainly enriched in the nucleus, transcription factor complexes, and adhesion spots. MF was mainly enriched in protein binding, DNA binding, and β-catenin binding. KEGG pathway was enriched in primary immunodeficiency, Hippo signaling pathway, cAMP signaling pathway, transcriptional mis-regulation in cancer and hematopoietic cell lineage. BP of up-regulated genes was mainly enriched in type I interferon signaling pathway and viral response. CC was mainly enriched in cytoplasm. MF was mainly enriched in RNA binding. Ten hub genes and three important gene clusters were screened by STRING database and Cytoscape software. The functions of the three key gene clusters were closely related to immune regulation. ROC analysis showed that the hub genes had a good diagnostic significance for MDS. GSEA analysis indicated that LEF1 may affect the normal function of hematopoietic stem cells by regulating inflammatory reaction, which further revealed the pathogenesis of MDS. CONCLUSION Bioinformatics can effectively screen the core genes and key signaling pathways of MDS, which provides a new strategy for the diagnosis and treatment of MDS.
Computational Biology ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Ontology ; Humans ; Myelodysplastic Syndromes/genetics*

Humans ; Lymphohistiocytosis, Hemophagocytic/complications* ; Neoplasms

BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. METHODS: Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. RESULTS: The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). CONCLUSIONS: The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. TRIAL REGISTRATION Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.
Area Under Curve ; Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; China ; Deep Learning ; Humans ; ROC Curve ; Sensitivity and Specificity