The Emergence of Complexity: Lessons from DNA

The same molecular qualities that endowed DNA with its capacity to carry hereditary information make it a powerful tool to explore the self-assembly of complex nanostructure

Construction and validation of a glioblastoma prognostic model based on immune-related genes

BackgroundGlioblastoma multiforme (GBM) is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have plateaued.PurposeHere, we identified an immune-related gene (IRG)-based prognostic signature to comprehensively define the prognosis of GBM.MethodsGlioblastoma samples were selected from the Chinese Glioma Genome Atlas (CGGA). We retrieved IRGs from the ImmPort data resource. Univariate Cox regression and LASSO Cox regression analyses were used to develop our predictive model. In addition, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and 3-year overall survival (OS) probabilities of individuals with GBM. Additionally, the molecular and immune characteristics and benefits of ICI therapy were analyzed in subgroups defined based on our prognostic model. Finally, the proteins encoded by the selected genes were identified with liquid chromatography-tandem mass spectrometry and western blotting (WB).ResultsSix IRGs were used to construct the predictive model. The GBM patients were categorized into a high-risk group and a low-risk group. High-risk group patients had worse survival than low-risk group patients, and stronger positive associations with multiple tumor-related pathways, such as angiogenesis and hypoxia pathways, were found in the high-risk group. The high-risk group also had a low IDH1 mutation rate, high PTEN mutation rate, low 1p19q co-deletion rate and low MGMT promoter methylation rate. In addition, patients in the high-risk group showed increased immune cell infiltration, more aggressive immune activity, higher expression of immune checkpoint genes, and less benefit from immunotherapy than those in the low-risk group. Finally, the expression levels of TNC and SSTR2 were confirmed to be significantly associated with patient prognosis by protein mass spectrometry and WB.ConclusionHerein, a robust predictive model based on IRGs was developed to predict the OS of GBM patients and to aid future clinical research

Dual carbon isotopes (C-14 and C-13) and optical properties of WSOC and HULIS-C during winter in Guangzhou, China

Water-soluble brown carbon (ws-BrC) exerts an important influence on climate change, but its emission sources and optical properties remain poorly understood. In this study, we isolated two ws-BrC proxies, water-soluble organic carbon (WSOC) and humic-like substance carbon (HULIS-C), from particulate matter collected in Guangzhou, China, during December 2012 for the measurement of dual carbon isotopes (C-14 and C-13) and light absorption. The mass absorption efficiencies of WSOC and HULIS-C at 365 nm were 0.81 +/- 0.16 and 1.33 +/- 0.21 m(2) g(-1) C, respectively. The C-14 results showed that two-thirds of WSOC and HULIS-C were derived from non-fossil sources (e.g., biomass burning and biogenic emission), and the remaining third was derived from fossil sources. The delta C-13 values of WSOC and HULIS-C were -23.7 +/- 1.2 parts per thousand and -24.2 +/- 0.9 parts per thousand, respectively, underlining the limited influences of C4 plants and natural gas on ws-BrC. Fitting the data to a multiple linear regression, we further concluded that approximately 80% and 10% of the light absorption at 365 nm was due to non-fossil and fossil carbon, respectively. Non-fossil sources of ws-BrC, such as the burning of agricultural residue, were responsible for the light absorption recorded in Guangzhou. (c) 2018 Elsevier B.V. All rights reserved