Objective To probe into the variation law of seasons factor on medical consumables and dispose resources sci- entifically. Methods The data of supplying sorts and times during the past 5 years was analyzed. Results The analysis shows evidently that there exists the highest point and the lowest point in demands of medical consumables varying with the seasons. Conclusion The law of medical consumables supplying can be given reference reasonably and scientifically for procurement and supply job in supply department.

On the basis of analyzing characteristics of ordinary agents warehouse to strengthen the management of the warehouse and give the ways of improving supply level,such as classified location management,ABC key management methods and sound inventory plan.It is an effective way to ensure the warehouse management.

OBJECTIVETo assess the association of single nucleotide polymorphisms (SNP) rs547984, rs540782, rs693421 and rs2499601 of Zona Pellucida Glycoprotein 4 (ZP4) gene with primary open-angle glaucoma (POAG) among ethnic Han Chinese from Sichuan Province.

METHODSA dye terminator-based SNaPshot method was used to genotype 336 patients with POAG and 768 healthy controls.

RESULTSNo significant difference was detected in allelic frequencies of rs547984, rs540782, rs693421 and rs2499601 between the two groups (P>0.05). Haplotypic analysis showed a significant difference in G-G-A-G haplotype formed by the 4 SNPs between the POAG and the control groups (P<0.05).

CONCLUSIONZP4 gene SNPs rs547984, rs540782, rs693421, rs2499601 are not associated with POAG among ethnic Hans from Sichuan.

Objective: To investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer(GBC)who underwent curative intent surgery. Methods: The clinical data of patients with advanced GBC who underwent curative intent surgery in 9 institutions from January 2010 to December 2015 were analyzed retrospectively.A median survival time model based on a tree augmented naïve Bayes algorithm was established by Bayesia Lab software.The survival time, number of metastatic lymph nodes(NMLN), T stage, pathological grade, margin, jaundice, liver invasion, age, sex and tumor morphology were included in this model.Confusion matrix, the receiver operating characteristic curve and area under the curve were used to evaluate the accuracy of the model.A priori statistical analysis of these 10 variables and a posterior analysis(survival time as the target variable, the remaining factors as the attribute variables)was performed.The importance rankings of each variable was calculated with the polymorphic Birnbaum importance calculation based on the posterior analysis results.The survival probability forecast table was constructed based on the top 4 prognosis factors. The survival curve was drawn by the Kaplan-Meier method, and differences in survival curves were compared using the Log-rank test. Results: A total of 316 patients were enrolled, including 109 males and 207 females.The ratio of male to female was 1.0∶1.9, the age was (62.0±10.8)years.There was 298 cases(94.3%) R0 resection and 18 cases(5.7%) R1 resection.T staging: 287 cases(90.8%) T3 and 29 cases(9.2%) T4.The median survival time(MST) was 23.77 months, and the 1, 3, 5-year survival rates were 67.4%, 40.8%, 32.0%, respectively.For the Bayesian model, the number of correctly predicted cases was 121(≤23.77 months) and 115(>23.77 months) respectively, leading to a 74.86% accuracy of this model.The prior probability of survival time was 0.503 2(≤23.77 months) and 0.496 8(>23.77 months), the importance ranking showed that NMLN(0.366 6), margin(0.350 1), T stage(0.319 2) and pathological grade(0.258 9) were the top 4 prognosis factors influencing the postoperative MST.These four factors were taken as observation variables to get the probability of patients in different survival periods.Basing on these results, a survival prediction score system including NMLN, margin, T stage and pathological grade was designed, the median survival time(month) of 4-9 points were 66.8, 42.4, 26.0, 9.0, 7.5 and 2.3, respectively, there was a statistically significant difference in the different points(P<0.01). Conclusions The survival prediction model of GBC based on Bayesian network has high accuracy.NMLN, margin, T staging and pathological grade are the top 4 risk factors affecting the survival of patients with advanced GBC who underwent curative resection.The survival prediction score system based on these four factors could be used to predict the survival and to guide the decision making of patients with advanced GBC.

OX40 is a costimulatory receptor that is expressed primarily on activated CD4⁺, CD8⁺, and regulatory T cells. The ligation of OX40 to its sole ligand OX40L potentiates T cell expansion, differentiation, and activation and also promotes dendritic cells to mature to enhance their cytokine production. Therefore, the use of agonistic anti-OX40 antibodies for cancer immunotherapy has gained great interest. However, most of the agonistic anti-OX40 antibodies in the clinic are OX40L-competitive and show limited efficacy. Here, we discovered that BGB-A445, a non-ligand-competitive agonistic anti-OX40 antibody currently under clinical investigation, induced optimal T cell activation without impairing dendritic cell function. In addition, BGB-A445 dose-dependently and significantly depleted regulatory T cells in vitro and in vivo via antibody-dependent cellular cytotoxicity. In the MC38 syngeneic model established in humanized OX40 knock-in mice, BGB-A445 demonstrated robust and dose-dependent antitumor efficacy, whereas the ligand-competitive anti-OX40 antibody showed antitumor efficacy characterized by a hook effect. Furthermore, BGB-A445 demonstrated a strong combination antitumor effect with an anti-PD-1 antibody. Taken together, our findings show that BGB-A445, which does not block OX40-OX40L interaction in contrast to clinical-stage anti-OX40 antibodies, shows superior immune-stimulating effects and antitumor efficacy and thus warrants further clinical investigation.
Mice ; Animals ; Receptors, Tumor Necrosis Factor/physiology* ; Receptors, OX40 ; Membrane Glycoproteins ; Ligands ; Antibodies, Monoclonal/pharmacology* ; Antineoplastic Agents/pharmacology*