Objective To study the related factors associated with the reversal of posttransplant diabetes mellitus (PTDM) following liver transplantation. Methods The clinical data of 62patients with PTDM in 232 patients receiving liver transplantation (26. 7 %) were retrospectively analyzed and the patients were divided into two groups: patients with transient PTDM (34 cases) and those with persistent PTDM (28 cases). Pre-operative and post-operative variables, including sex,age, body mass index, family history of diabetes, hepatitis B virus infection, pretransplantation fasting plasma glucose, the immunosuppressant regime, FK506 concentration and duration of steroid usage, were analyzed retrospectively. Results The variables, including sex, age, body mass index,family history of diabetes, hepatitis B virus infection, pretransplantation fasting plasma glucose,FK506 concentration at month 1, 3 and 6 after operation, rate of cyclosporine usage and duration of steroid usage had no significant difference between the two groups (P＞0. 05). Compared with the persistent PTDM patients, the transient PTDM patients were characterized by younger age at the time of transplantation (54 ± 8 vs. 42 ± 6 years, P＜0. 05), longer time before the development of PTDM (18 ± 23 vs. 35 ± 42 days, P＜0. 05), and higher rate of mycophenolate mofetil or sirolimus usage (0vs. 8. 9 %, P＜0. 05). Based on a multivariate analysis, age at the time of transplantation was determined as the single independent predictive factor associated with reversal of PTDM following liver transplantation (odds ratio: 1. 312, 95 % confidence interval: 1. 005 - 1. 743). Conclusion Age at the time of transplantation, duration before the development of PTDM and rate of mycophenolate mofetil or sirolimus usage are associated with reversal of PTDM following liver transplantation. Among these factors, age at the time of transplantation is only the single independent predictive factor.

Objective To analyze the relationships of high mobility group box 1 protein (HMGB1) with regulatory T cells (Treg), T helper 17 cells (Th17) and cytokine secrtion in peripheral blood of gravidas with preeclampsia(PE),and to investigate the mechanism of HMGB1 in regulating Th17/Treg ratio via receptor for advanced glycation end products (RAGE)-IL-6 pathway. Methods Forty gravi-das with mild(20 cases) and severe(20 cases) PE were recruited as experimental groups,20 heathy gravi-das in the third trimester of pregnancy were enrolled as control group. Concentrations of HMGB1,IL-6,IL-17 and TGF-β in peripheral blood of all subjects were determined by enzyme-linked immunosorbent assay (ELISA). Real-time quantitative PCR(RT-PCR) was used to detect the expression of RAGE at mRNA lev-el in peripheral blood mononuclear cells(PBMCs). The percentages of Treg and Th17 cells were determined by flow cytometry. RT-PCR was performed to analyze changes in the expression of RAGE,IL-6,Foxp3 and RORγt at mRNA level after the PBMCs isolated from 20 garvidas with PE were cultured in vitro and stimula-ted with recombinant human HMGB1 (rhHMGB1). Results The levels of HMGB1,IL-6,Th17 and IL-17 in peripheral blood of gravidas with PE were significantly higher than those in the normal pregnancy group. Moreover,HMGB1 level was positively correlated with IL-6 level and ratios of Th17/Treg and IL-17/TGF-β in preeclamptic pregancies. In vitro stimulation of PBMCs with rhHMGB1 significantly enhanced the expres-sion of RAGE,IL-6 and RORγt at mRNA level, but suppressed the expression of Foxp3 at mRNA level. Conclusion Enriched HMGB1 in plasma shifts the Th17/Treg balance towards Th17 dominance via the RAGE-IL-6 pathway, which exacerbates inflammation and participates in the onset of preeclampsia during pregnancy.

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.

Blotting, Western ; Cell Cycle Checkpoints ; Cell Line, Tumor ; Chaperonin 60 ; metabolism ; HEK293 Cells ; Humans ; Mitochondria ; metabolism ; Reactive Oxygen Species ; metabolism ; Real-Time Polymerase Chain Reaction ; Sirtuin 3 ; genetics ; metabolism