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BackgroundOur study aimed to construct and validate prognostic nomograms for predicting survival for patients with Nonfunctional Pancreatic neuroendocrine tumor (NF-pNET).MethodsThis retrospective study included 1824 patients diagnosed with NF-pNET in the Surveillance, Epidemiology and End Results database between 2004 and 2016. Randomization divided the patients into training (n = 1278) and validation (n = 546) cohorts. Prognostic factors were determined using Cox regression analyses, nomograms based on AJCC 7th and 8th staging system were constructed separately. The prediction models were validated using internal validation and external validation.ResultsAge, year of diagnosis, primary tumor site, grade, 7th or 8th TNM stage, surgery, tumor size were determined as prognostic indicator to construct two nomograms. Harrell's concordance index (C-index) of two nomograms exhibited a clinical predictive ability of 0.828 (95%CI, 0.808~0.849) vs 0.828 (95% CI, 0.808~0.849) in the internal verification. The c-index in the external validation was 0.812 (95%CI, 0.778~0.864) vs 0.814 (95% CI, 0.779~0.848). The predictive power of the two nomograms is comparable.ConclusionsOur nomogram may be a effective tool for predicting overall survival in patients with NF-pNET. The AJCC 8th-edition system provides discrimination similar to that of the 7th-edition system.

The bin packing problem has a wide range of applications in industry. With the upgrade of the task difficulty, the traditional 2d rectangular layout algorithm can no longer meet the needs of modern industry, such as express packing task and exoplanet ore collection task. The express or ore samples come in heterogeneous shapes so they cannot all be treated as rectangular pieces. In this paper, we propose a three-stage method called covering, corner-searching and occupying (C,S&O) to solve the two-dimensional multishape part packing problem. The objective of the packing problem variant is to ensure maximum use of the raw material and minimize the trim loss. The algorithm cannot make use of information about the sequence of future objects that are going to arrive, only knowing the shape and size of the coming one, and the coming part must be packed into the bin immediately after its arrival without buffering or readjusting. The method of C,S&O hybridizes the idea of “gold corners, silver edges and grass belly” in the Chinese game Go and the method of finding picture corners in machine vision. In the first stage, the rectangular bin and the coming part are transformed into matrix representation, and generating the position matrix that indicates possible ways of packing the part into the bin. In the second stage, the suitable layout position of the coming part is obtained using machine vision image processing technology for reference. The third stage is calculating the environment matching degree to determine the current optimal placement orientation. In order to facilitate the display of the simulation results, only three shapes of parts are considered in the simulation, rectangle, circle and triangle. The experimental results show the effectiveness of this method. Consulting the literature, it is found that this paper is the first to propose a layout method for multishape manufacturing parts.

BackgroundAs a malignant tumor with poor prognosis, accurate and effective prediction of the prognosis of pancreatic cancer (PC) is crucial.MethodsA total of 12,909 patients diagnosed with pancreatic cancer were selected from the Surveillance, Epidemiology, and End Results program between 2004 and 2016. The sex, age, ethnicity, marital status, metastasis status, radiotherapy, chemotherapy, tumor size, regional nodes examined, regional nodes positive of each patient were recorded. Univariate and multivariate Cox regression analyses were used to identify prognostic factors with a threshold of PResultsAge, year of diagnosis, sex, grade, histologic, marital, TNM stage, surgery of the primary site, tumor size, regional nodes positive and regional nodes examined ratio (LNR), lymph node dissection, radiotherapy, and chemotherapy were identified as prognostic factors for the construction of the nomogram. The nomogram exhibited a clinical predictive ability of 0.675(95% CI, 0.669~0.681) in the internal verification. The predicted calibration curve was similar to the standard curve. Decision curve analysis showed that the nomogram had value in terms of clinical application. Besides, the nomogram was able to divide the patients into different groups according to total points.ConclusionsHence, our nomogram was highly effective in predicting overall survival in patients with PC, which may provide a reference tool for clinicians to guide individualized treatment and follow-ups for patients with PC, accurately determine the 1-,3- and 5-year overall survival of patients.

[This corrects the article DOI: 10.1371/journal.pone.0212951.].

To observe the adaptive response (AR) induced by ionizing radiation in human fibroblasts under monolayer and 3-dimensional (3-D) condition. Three kinds of fibroblasts were cultured under both monolayer and 3-D condition. Immunofluorescent staining was used to detect the γ-H2AX foci and the morphological texture. Trypan blue staining was used to detect the cell death. Western blot was used to detect the expressions of γ-H2AX, p53 and CDKN1A/p21 (p21). We found that DNA damage increased in a dose-dependent and time-dependent manner after high doses of radiation. When cells were pretreated with a priming low dose of radiation followed by high dose radiation, DNA damage was attenuated under both monolayer and 3-D condition, and the adaptive response (AR) was induced. Additionally, the morphology of cells under monolayer and 3-D conditions were different, and radiation also induced AR according to morphological texture analysis. Priming low dose radiation induced AR both under monolayer and 3-D condition. Interestingly, 3-D microenvironment made cells more sensitive to radiation. The expression of p53 and p21 was changed and indicated that they might participate in the regulation of AR.

Hypoxia has been reported to cause the significant enhancement of hemoglobin (Hb) and hematocrit (Hct), which stabilizes at relatively high levels after an individual ascends to a high altitude. However, the quality of the suspended red blood cells (SRBCs) obtained from individuals at high altitudes such as Tibetan plateau migrants after storage has not been studied. In this study, we compared the storage quality of SRBCs prepared from Tibetan plateau and Deyang lowland populations by adding a normal volume of mannitol-adenine-phosphate (MAP), which is a common additive solution used in blood storage in Asian countries. The storage cell characteristics were examined on days 1, 7, 14 and 35.We found higher Hct and Hb levels and viscosity in the high altitude samples. The metabolic rates, including those for electrolytes and lactate, were higher in plateau SRBCs than in lowland SRBCs; these findings were consistent with the higher osmotic fragility and hemolysis of plateau SRBCs throughout the entire storage period. In addition, the reduction rates of 2,3-diphosphoglycerate (2,3-DPG) and oxygen tension to attain 50% oxygen saturation of Hb (P50) in plateau SRBCs were higher than those in lowland SRBCs, and the oxygen delivering capacity in plateau SRBCs was weaker than that in lowland SRBCs. We concluded that the storage quality of plateau SRBCs was inferior to that of lowland SRBCs when using the same concentration of MAP. We suggested that the optimal formula, including the MAP concentration or even a new additive solution, to store the plateau SRBCs must be assessed and regulated.

Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies - producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled "ENA", accessible on CRAN (http://cran.r-project.org/web/packages/ENA/).