Objective To explore the effect of recombinant human granulocyte colony stimulating factor (rhG-CSF) mobilization on TH17/Treg cells and its impact on suppressor of cytokine signaling-3 (SOCS3) gene expression in CD4+ T cells in donors' peripheral blood.Method Sixteen donors were injected subcutaneously with rhG-CSF 5 μg/kg every day for 5 consecutive days for peripheral blood stem cells mobilization.At the first 0,3,5 day,the mononuclear cclls (MNCs) in peripheral blood or graft and serum specimens were taken.The CD4 + T cells in MNCs were sorted using immuno-magnetic beads.The ratio of TH 17 and Treg cells in MNCs,cytokines concentrations of IL-17A,IL-21,ID23 and TGFβ1 in serum,and SODC3 gene expression in CD4+ T cells were detected by using flow cytometry,ELISA,and reverse transcription real-time quantitative PCR (RT-qPCR),respectively.Results (1)The ratio of Th17 cells (CD3+ CD8 CD17+) and Treg cells (CD4+ CD25+ Foxp3+) in MNCs in peripheral blood and graft at the first 0,3 and 5 days after mobilization was (2.69 ± 0.81) ％,(0.91 ± 0.33) ％,(0.35 ± 0.12) ％,(0.21 ± 0.05) ％,and (0.56 ± 0.24) ％,(0.72 ± 0.22％),(1.59 ± 0.54) ％,(3.38 ± 0.52) ％,respectively,showing a significant declining and increasing trend respectively (P＜0.05); (2)The cytokine concentrations in serum at the first 0,3 and 5 days after mobilization were 7.33 ± 0.89,5.78 ± 1.03 and 3.32 ± 0.84 μg/L for IL-17A; 124.56 ± 15.18,117.12 ± 14.45 and 64.94 ± 11.25 μg/L for IL-21 ; 183.52 ± 59.35,280.49 ± 69.75 and 393.62 ± 57.25μg/L for TGF-β1 (P＜0.01) ; and 45.89 ± 6.95,46.25 ± 7.44 and 47.45 ± 10.75 μg/L for IL-23,respectively.The IL-17A and IL-21 concentrations showed significant declining trend,contrarily TGF-β1 with an increasing trend,while IL-23 concentration had no change.After rhG-CSF mobilization,the SOCS3 gene expression in CD4 + T cells of peripheral blood and graft at the first 0,3,5 days was gradually increased.Conclusion rhG-CSF suppresses Th17 cells and promotes regulatory T cells generation,meanwhile decreases IL-17A and IL-21 and elevates serum TGF-β1 concentrations,and contributes to CD4 + T cells differentiation to Tregs,probably by elevating SOSC3 gene expression in CD4+ T cells.

With the wide application of electronic data management (EDC), the data management is shifting to a new mode. In order to recognize the advantages of EDC, we choose 20 representative registered clinical trials, which involve 5 404 subjects and 321 sites. We found that EDC has many beneficial impacts on the course of clinical trial data management, including the process of data collection, data cleaning, data quality control and clinical trial decision-making. The result also provides a reference for the adoption of EDC in clinical trials.

OBJECTIVETo evaluate the marginal adaptation between the new nano-hydroxyapatite composite resin and tooth.

METHODSThirty extracted healthy premolars were randomly assigned to three groups according to the material employed: New nano-hydroxyapatite composite resin (Group A), Karisma composite resin (Group B), and glass ionomer cement (Group C). After the thermal cycling, the teeth were immersed in 2% methylene blue dye, and the depth of microleakage between the composite and tooth structure were observed.

RESULTSThe microleakage depth of group A, B and C were (1.20+/-0.81), (1.94+/-0.70), and (1.73+/-0.54) mm, respectively. No statistically significant differences were found among the three groups (P>0.05). There was no significant difference in the degree of microleakage (P>0.05).

CONCLUSIONAs a new dental restorative material, new nano-hydroxyapatite composite resin has good bonding performance to the tooth structure.

The identification of oleaginous yeast species capable of simultaneously utilizing xylose and glucose as substrates to generate value-added biological products is an area of key economic interest. We have previously demonstrated that the Cutaneotrichosporon dermatisNICC30027 yeast strain is capable of simultaneously assimilating both xylose and glucose, resulting in considerable lipid accumulation. However, as no high-quality genome sequencing data or associated annotations for this strain are available at present, it remains challenging to study the metabolic mechanisms underlying this phenotype. Herein, we report a 39,305,439 bp draft genome assembly for C. dermatis NICC30027 comprised of 37 scaffolds, with 60.15% GC content. Within this genome, we identified 524 tRNAs, 142 sRNAs, 53 miRNAs, 28 snRNAs, and eight rRNA clusters. Moreover, repeat sequences totaling 1,032,129 bp in length were identified (2.63% of the genome), as were 14,238 unigenes that were 1,789.35 bp in length on average (64.82% of the genome). The NCBI non-redundant protein sequences (NR) database was employed to successfully annotate 11,795 of these unigenes, while 3,621 and 11,902 were annotated with the Swiss-Prot and TrEMBL databases, respectively. Unigenes were additionally subjected to pathway enrichment analyses using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cluster of Orthologous Groups of proteins (COG), Clusters of orthologous groups for eukaryotic complete genomes (KOG), and Non-supervised Orthologous Groups (eggNOG) databases.Together, these results provide a foundation for future studies aimed at clarifying the mechanistic basis for the ability of C. dermatis NICC30027 to simultaneously utilize glucose and xylose to synthesize lipids.

Objective:To explore the application of pyrophosphorolysis-activated polymerization(PAP)to monitor plasma cfDNA in ad-vanced non-small cell lung cancer(NSCLC).Methods:A total of 85 patients diagnosed with advanced NSCLC between March 2016 and June 2017 were enrolled in the present study. EGFR mutations in cfDNA extracted from the plasma were detected using PAP and ARMS-PCR technology.The concordance analysis of EGFR mutations involved plasma vs.tumor tissue and PAP vs.ARMS-PCR.Further-more,38 EGFR-positive patients were selected to monitor EGFR mutations with PAP.Results:No statistical differences in EGFR muta-tions were observed between plasma and tumor tissue(P=0.092),as well as PAP and ARMS-PCR(P=0.210).The detection rate of EGFR mutations in cfDNA was higher in the progressor than in the non-progressor(62.5% vs.21.3%,P<0.001).Conclusions:PAP can be used for detecting and monitoring EGFR mutations in cfDNA to predict disease progression.

Objective:To construct and validate a machine learning model for preoperative prediction of perineural invasion (PNI) status in intrahepatic cholangiocarcinoma (ICC).Methods:Clincial data of 329 patients, including 245 admitted to Zhengzhou University People's Hospital from January 2018 to June 2023 and 84 admitted to the Affiliated Cancer Hospital of Zhengzhou University from January 2013 to January 2020 were retrospectively analyzed. Patients were divided into a training set ( n=231) and a validation set ( n=98). Clinicopathological data including age, gender, hepatitis B virus (HBV) infection status were collected. Predictive variables were determined using least absolute shrinkage and selection operator (LASSO) regression analysis. Six machine learning algorithms including random forest (RF), logistic regression, and linear kernel-based support vector machine were selected to construct the preoperative prediction model for PNI in ICC. Performance metrics of the model were calculated using a confusion matrix, and the final model was selected. The model performance was evaluated in the validation set. Calibration curves were plotted to evaluate the final model, and a Pareto chart was used to visualize the importance of predictive variables. Results:LASSO regression identified nine predictive variables included in the prediction model, including carbohydrate antigen 19-9 (CA19-9), HBV infection status, alkaline phosphatase, alanine aminotransferase, prothrombin time, total bilirubin, albumin, neutrophil times gamma-glutamyl transferase to lymphocyte ratio, and tumor burden score. Among the trained six models, the area under the curve (AUC) of the RF model was 0.909, with a sensitivity of 0.842 and an accuracy of 0.870. Compared with the AUC of the RF model, the AUCs of the other 5 models were lower (all P<0.05). The AUC of the RF model for predicting PNI in ICC in validation set was 0.736. Calibration curves showed good fit of the RF model's prediction of PNI in ICC in both training and validation sets. The Pareto chart showed that CA19-9 was the most important predictive variable in the model, followed by HBV infection status. Conclusion:The machine learning model based on the RF algorithm has a high accuracy in preoperative prediction of PNI status in ICC.

Objective:A predictive nomogram model for the prognosis of intrahepatic cholangiocarcinoma (ICC) patients after curative resection was constructed based on the albumin-bilirubin score and tumor burden score (ATS) grade, and the predictive performance of the nomogram model was evaluated.Methods:Retrospective analysis of clinical data was made, from ICC patients who underwent curative resection at Zhengzhou University People's Hospital and Zhengzhou University Cancer Hospital from January 2016 to January 2020. A total of 258 patients were included in the study, with 140 males and 118 females, with an average age of (56.5±9.5) years. The 258 ICC patients were randomly divided into a training set ( n=174) and a testing set ( n=84) in a 7∶3 ratio. Single-factor and multi-factor Cox regression analyses were performed to identify prognostic factors for ICC patients of the training set, and then a nomogram model was constructed. The performance of the nomogram model was evaluated by using the concordance index (C-index), calibration curve, and risky decision curve analysis. Results:In the training set, univariate Cox regression analysis indicated that albumin-bilirubin (ALBI), tumor burden score (TBS), carcinoembryonic antigen (CEA), tumor differentitation, lymphvascular invasion and ATS significantly influenced overall survival after radical resection for ICC (all P<0.05). Multifactorial Cox regression analysis revealed that ATS grade, CEA, tumor differentiation, lymphovascular invasion, and AJCC N stage are independent risk factors for the prognosis of ICC patients after curative resection (all P<0.05). Assessment of the postoperative survival prediction model based on multifactorial Cox regression yielded a C-index of 0.775(95% CI: 0.747-0.841) for the training set and 0.731(95% CI: 0.668-0.828) for the testing set. The calibration curves for both the training and testing sets indicated strong predictive capability of the model. Additionally, the risk decision curve also suggested high net benefit of the model. Conclusions:The preoperative ATS grade is an independent factor affecting the survival after ICC radical resection. The nomogram model constructed based on ATS grade demonstrates excellent predictive value for postoperative prognosis in ICC patients.

Objective:To explore the prognostic value of systemic immune-inflammatory index(SII)combined with tumor burden score (TBS) (hereinafter referred to as STS) in patients with intrahepatic cholangiocarcinoma (ICC) after radical resection, and to construct a nomogram model.Methods:The clinical data (including the degree of tumor differentiation, vascular cancer thrombus, and lymph node metastasis, etc.) of 258 ICC patients who received radical resection at People′s Hospital of Zhengzhou University (170 cases, training set) and Cancer Hospital of Zhengzhou University (88 cases, validation set) from January 1, 2016 to January 31, 2020 were retrospectively analyzed and graded by SII, TBS and STS. Multivariate Cox regression analysis were used to identify independent factors affecting the prognosis of patients with ICC. Kaplan-Meier survival curve and receiver operating characteristic curve (ROC) were drawn to evaluate the predictive efficiency of SII, TBS and STS in the overall survival of patients with ICC after radical resection. The nomogram prediction model was constructed and evaluate the performance of nomogram model using consistency index (C-index) and calibration curve.Results:Among 170 ICC patients in the training set, there were 106 cases of SII grade 1 and 64 cases of SII grade 2; 137 cases of TBS grade 1 and 33 cases of TBS grade 2; and 98 cases of STS grade 1, 47 cases of STS grade 2, and 25 cases of STS grade 3. Among 88 ICC patients in the validation set, there were 33 cases of SII grade 1 and 55 cases of SII grade 2; 66 cases of TBS grade 1 and 22 cases of TBS grade 2; and 30 case of STS grade 1, 39 cases of TBS grade 2, and 19 cases of TBS grade 3.The multivariate Cox regression analysis showed that tumor differentiation degree (highly differentiated vs. moderately differentiated HR=0.157, 95% confidence interval(95% CI) 0.045 to 0.546, highly differentiated vs. poorly differentiated HR=0.452, 95% CI 0.273 to 0.750), STS (grade 3 vs. grade 2 HR=1.966, 95% CI 1.148 to 3.469; grade 3 vs. grade 1 HR=1.405, 95% CI 0.890 to 2.216), vascular cancer thrombus ( HR=2.006, 95% CI 1.313 to 3.066), nerve invasion ( HR=1.865, 95% CI 1.221 to 2.850), and lymph node metastasis ( HR=1.802, 95% CI 1.121 to 2.896) were independent influencing factors of overall survival in ICC patients after radical resection (all P<0.05). The Kaplan-Meier survival curve showed that SII, TBS, and STS were independent influencing factors of overall survival in ICC patients (all P<0.05). The results of ROC analysis showed that the areas under the curve of SII, TBS and STS in predicting overall survival of ICC patients after radical resection were 0.566 (95% CI 0.479 to 0.652), 0.585 (95% CI 0.499 to 0.672), and 0.657 (95% CI 0.522 to 0.692), respectively. Tumor differentiation, vascular tumor thrombus, nerve invassion, lymph node metastasis, and STS were included to constract the nomogram model. The C-indexes of the training set and validation set based on the nomogram model were 0.792 (95% CI 0.699 to 0.825) and 0.776 (95% CI 0.716 to 0.833), respectively. The calibration curves of the survival rate of the training set and the validation set were close to the reference lines, and the nomogram model had better predictive ability in both the training set and the validation set. Conclusions:Preoperative STS grading is an effective and practical predictor of overall survival in ICC patients after radical section. Compared with SII and TBS alone, it has better predictive value for the prognosis of patients with ICC.

Trichoderma reesei Rut-C30 is widely used in industrial cellulase production, and development of cellulase hyper-producer is of great importance for economic lignocellulosic biorefinery. In this study, T. reesei Rut-C30 was engineered with an artificial zinc finger proteins (AZFPs) library. Two mutants T. reesei M1 and M2 with improved cellulase production were obtained. Compared to the parent strain, the filter paper activity (FPase) of T. reesei M1 and M2 increased 100% and 53%, respectively. In addition, the total amount of extracellular protein from the M1 mutant increased 69%, whereas the endo-β-glucanase (CMCase) activity of the M2 mutant is 64% higher compared to the parental strain. Furthermore, RT-qPCR analysis showed that the major cellulase genes exhibited significantly increased expression in both mutants, but different patterns were observed in the two mutants. On the other hand, the cellulase transcriptional repressor ace1 was down-regulated in both mutants, but the transcription level of the activator xyr1 was only up-regulated in the strain M1. These results demonstrated that different AZFPs exert diverse regulatory mechanisms on cellulase production in T. reesei. Analysis of the target genes of AZFPs from T. reesei M1 and M2 will not only benefit further exploration of the regulatory mechanisms of cellulase biosynthesis in T. reesei, but also enable development of cellulase hyper-producing strains by metabolic engineering.
Cellulase ; Gene Library ; Transcription Factors ; Trichoderma ; Zinc Fingers