Objective To explore the clinical features and genetic characteristics of primary immunodeficiency disease (PIDs) with skin symptoms in children. Methods The clinical data of PIDs with skin symptoms in 15 children from January 2014 to March 2017 were analyzed retrospectively. Results The median age at onset in 15 children was 4 months (neonatal period to 11 years 8 months). All of them showed obvious skin symptoms, including eczema or chilblain rash, pustular psoriasis, skin infections, subcutaneous hemorrhage or skin ecchymosis, ichthyosiform erythroderma, progeroid appearance, or other cutaneous vasculitis. The accompanying manifeslations included recurrent infections, auto inflammation, autoimmunity, growth retardation, or lymphoid proliferation, and impairment of brain, lung, kidney and other important organs. Eosinophil counts were increased in 5 children, IgE levels were elevated in 5 children, and 4 children were abnormal in both indicators. Gene detection showed WAS, RNASEH2C, NLRP12, IL36RN, NRAS, PIK3CD, STAT1, FOXP3, STAT3, DOCK8, TYK2, SPINK5, NBAS or ITGB2 gene mutations, respectively. Two children died from multiple organ dysfunction syndrome, 1 child was lost for follow up, the remaining 12 children survived and were under the individualized treatment. Conclusions A variety of PIDs can have skin symptoms. When accompanied by recurrent infections, auto inflammation, autoimmune, growth retardation, or lymph proliferation, PIDs should be considered, and gene detection is helpful for the diagnosis.

Objective To screen the critical genes related to the development of esophageal squamous cell carcinoma ( ESCC ) by weighted gene co-expression network analysis ( WGCNA ) and to verify by experiments.Methods Gene expression data of ESCC were downloaded from gene expression omnibus (GEO) database based on gene chip platform ( GPL) 570, GPL571, GPL96/97 or GPL14613 platform, respectively. Meanwhile, the obtained differentially expressed genes together with gene expression data of 81 ESCC patients from the cancer genome atlas ( TCGA ) and clinical data were analyzed by WGCNA to set up co-expression networks including mRNA and microRNA ( miRNA ) . The expression of miRNA in ESCC tissues and paracancerous tissues was examined by quantitative real-time polymerase chain reaction ( RT-PCR ) .And the expression of target protein Kruppel like factor 4 ( KLF4 ) and desmocollin 2 ( DSC2 ) were detected by immunohistochemistry .After ESCC cell line ECA-109 cells were transfected with miRNA-92b-3p mimic, cell cycle was tested by flow cytometry ,the cell invasion and migration ability was measured by Transwell chamber assay and scratch-wound assay.The expression of KLF4 and DSC2 was observed by confocal laser scanning microscopy and Western blotting .The target genes were verified by luciferase assay .T-test, rank sum test, chi-square test and Pearson correlation analysis were performed for statistical analysis .Results A total of 4023 differential expression gene ( DEG) and 328 differential expression miRNA ( DEM) were screened and 11 gene modules were set up by WGCNA .Among them, the brown modules were negatively associated with tumor grade and T stage (r=-0.340 and -0.268, P=0.002 and 0.016).Meanwhile, has-miR-92b and the potential target genes KLF4 and DSC2 were all in the brown module .Furthermore, the results of RT-PCR showed the expression of miRNA-92b-3p in ESCC tissues was higher than that in paracancerous tissues (3.052(1.652, 5.371) vs.0.985(0.558, 2.032)), and the difference was statistically significant (Z=-4.021,P<0.01). The results of immunohistochemistry demonstrated that the positive rates of KLF 4 and DSC2 in ESCC tissues were 43.3％(13/30) and 20.0％(6/30), respectively, which were lower than those of paracancerous tissues (70.0％(21/30) and 63.3％(19/30)), and the differences were statistically significant (χ2 =4.344 and 1.589, both P<0.05).After ECA-109 cells were transfected with miRNA-92b-3p mimics, the percentage of cells at G0/G1 phase decreased ((63.71 ±2.83)％vs.(54.62 ±4.00)％) and the percentage of cells at the S phase and G2/M phase increased ((31.81 ±2.88)％vs.(41.20％±2.87)％, and (3.87 ±1.75)％vs. (8.10 ±1.71)％, respectively), and the differences were statistically significant (t =3.215, 4.000 and 2.998;P=0.032, 0.016 and 0.040).The invasion and migration ability of the cells were significantly improved (79.67 ±27.54 vs.280.33 ±46.18, (69.72 ±3.91)％ vs.(84.90 ±5.25)％), and the differences were statistically significant (t=6.465 and 4.019, P=0.003 and 0.016).The results of Western blotting indicated that, compared with control mimic group , the expression of KLF4 and DSC2 was both dramatically downregulated after transfected with miRNA-92b-3p mimics transfected (1.00 ±0.23 vs.0.42 ±0.03, 1.00 ±0.20 vs.0.55 ± 0.21), and differences were statistically significant (t=4.470 and 5.493, P=0.042 and 0.032).The results of luciferase assay demonstrated that miRNA-92b-3p could directly bind KLF4 and DSC2. Conclusion WGCNA is an efficient systemic biological approach by which miRNA-92b-3p is identified as a new cancer-promoting gene .

Objective: To investigate the clinical features and genetic characteristics of cases with X-linked immunodeficiency with magnesium defect, Epstein-Barr virus (EBV) infection, and neoplasia (XMEN). Methods: Characteristics of clinical material, immunological data and gene mutation of two cases with XMEN in the same family in China were retrospectively analyzed. The related reports literature were searched by using search terms'MAGT1 gene’or'XMEN’. Results: The proband, a 2-year-eight-month old boy, was admitted due to 'Urine with deepened color for two days and yellow stained skin for one day’. He had suffered from recurrent upper respiratory tract infection and sinusitis previously. Hemoglobin level was 38 g/L. The absolute count of reticulocytes was 223.2×10⁹/L. Urobilinogen level was 38 μmol/L (3-16 μmol/L). Coomb's test was positive. Both total (77.2 μmol/L) and indirect bilirubin (66 μmol/L) levels were elevated. There was an inverted CD4⁺/CD8⁺T cell ratio (0.89). The gene sequencing results showed MAGT1 gene c.472delG, p.D158Mfs*6 mutation. His 1-year-6-month old brother, was also identified to have MAGT1 gene c.472delG, p.D158Mfs*6 mutation.The younger brother mainly suffered from recurrent upper respiratory tract infection, accompanied by an inverted CD4⁺/CD8⁺T cell ratio (0.45), an elevated ratio and number of total B cells (45.7%). A total of 7 reports were retrieved including 11 male cases caused by MAGT1 gene mutation. These 11 cases were characterized by EBV viremia (11 cases), recurrent upper respiratory tract infection, otitis media or sinusitis (10 cases), secondary neoplasia diseases (8 cases), reduction of CD4⁺/CD8⁺ T cell ratio (7 cases),and autoimmune thrombocytopenia or hemolytic anemia (2 cases). Conclusion XMEN often manifests as male onset, recurrent upper respiratory tract infection, otitis media or sinusitis, EBV viremia, lymphoproliferative disease or lymphoma, autoimmune diseases and reduction of CD4⁺/CD8 ⁺T cell ratio. NKG2D expression in NK cells is significantly reduced, and gene sequencing analysis shows a pathogenic mutation in MAGT1 gene.

Objective To observe the value of 3D Res2Net deep learning model for predicting volume doubling time(VDT)of solid pulmonary nodule.Methods Chest CT data of 734 patients with solid pulmonary nodules were retrospectively analyzed.The patients were divided into progressive group(n=218)and non-progressive group(n=516)according to whether lung nodule volume increased by ≥25％during follow-up or not,also assigned into training set(n=515)and validation set(n=219)at a ratio of 7∶3.Then a clinical model was constructed based on clinical factors being significantly different between groups,CT features model was constructed based on features of nodules on 2D CT images using convolutional neural network,and 3D Res2Net model was constructed based on Res2Net network using 3D CT images as input.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated.Taken actual VDT as gold standard,the efficacy of the above models for predicting solid pulmonary nodule'VDT≤400 days were evaluated.Results No significant difference of predicting efficacy for solid pulmonary nodule'VDT≤400 days was found among clinical model,CT feature model and 3D Res2Net model,the AUC of which was 0.689,0.698 and 0.734 in training set,0.692,0.714 and 0.721 in validation set,respectively.3D Res2Net model needed 5-7 s to predict VDT of solid pulmonary nodules,with an average time of(5.92±1.08)s.Conclusion 3D Res2Net model could be used to predict VDT of solid pulmonary nodules,which might obviously reduce manual interpreting time.

Objective To observe the value of feature pyramid network(FPN)for automatic segmentation and semantic feature classification of spontaneous intracerebral hemorrhage(sICH)hematoma showed on non-contrast CT.Methods Non-contrast CT images of 408 sICH patients in hospital A(training set)and 103 sICH patients in hospital B(validation set)were retrospectively analyzed.Deep learning(DL)segmentation model was constructed based on FPN to segment the hematoma region,and its efficacy was assessed using intersection over union(IoU),Dice similarity coefficient(DSC)and accuracy.Then DL classification model was established to identify the semantic features of sICH hematoma.Receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the efficacy of DL classification model for recognizing semantic features of sICH hematoma.Results The IoU,DSC and accuracy of DL segmentation model for 95％sICH hematoma in training set was 0.84±0.07,0.91±0.04 and(88.78±8.04)％,respectively,which was 0.83±0.07,0.91±0.05 and(88.59±7.76)％in validation set,respectively.The AUC of DL classification model for recognizing irregular shape,uneven density,satellite sign,mixed sign and vortex sign of sICH hematoma were 0.946-0.993 and 0.714-0.833 in training set and validation set,respectively.Conclusions FPN could accurately,effectively and automatically segment hematoma of sICH,hence having high efficacy for identifying semantic features of sICH hematoma.