High Accordance in Prognosis Prediction of Colorectal Cancer across Independent Datasets by Multi-Gene Module Expression Profiles

A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors

Risk Factors Associated with Late Failure of Noninvasive Ventilation in Patients with Chronic Obstructive Pulmonary Disease

Background. Risk factors for noninvasive ventilation (NIV) failure after initial success are not fully clear in patients with acute exacerbation of chronic obstructive pulmonary disease (COPD). Methods. Patients who received NIV beyond 48 h due to acute exacerbation of COPD were enrolled. However, we excluded those whose pH was higher than 7.35 or PaCO2 was less than 45 mmHg which was measured before NIV. Late failure of NIV was defined as patients required intubation or died during NIV after initial success. Results. We enrolled 291 patients in this study. Of them, 48 (16%) patients experienced late NIV failure (45 received intubation and 3 died during NIV). The median time from initiation of NIV to intubation was 4.8 days (IQR: 3.4–8.1). Compared with the data collected at initiation of NIV, the heart rate, respiratory rate, pH, and PaCO2 significantly improved after 1–2 h of NIV both in the NIV success and late failure of NIV groups. Nosocomial pneumonia (odds ratio (OR) = 75, 95% confidence interval (CI): 11–537), heart rate at initiation of NIV (1.04, 1.01–1.06 beat per min), and pH at 1–2 h of NIV (2.06, 1.41–3.00 per decrease of 0.05 from 7.35) were independent risk factors for late failure of NIV. In addition, the Glasgow coma scale (OR = 0.50, 95% CI: 0.34–0.73 per one unit increase) and PaO2/FiO2 (0.992, 0.986–0.998 per one unit increase) were independent protective factors for late failure of NIV. In addition, patients with late failure of NIV had longer ICU stay (median 9.5 vs. 6.6 days) and higher hospital mortality (92% vs. 3%) compared with those with NIV success. Conclusions. Nosocomial pneumonia; heart rate at initiation of NIV; and consciousness, acidosis, and oxygenation at 1–2 h of NIV were associated with late failure of NIV in patients with COPD exacerbation. And, late failure of NIV was associated with increased hospital mortality

Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level

<p>Abstract</p> <p>Background</p> <p>Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples.</p> <p>Results</p> <p>In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types.</p> <p>Conclusions</p> <p>Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.</p

Excellent Properties of Ni-15 wt.% W Alloy Electrodeposited from a Low-Temperature Pyrophosphate System

Electrodeposited Ni-W alloy coatings are considered to be one of the most suitable candidate coatings to replace carcinogenic hexavalent chromium coatings. In this work, Ni-W alloys are electrodeposited from pyrophosphate baths containing different concentrations of Na2WO4 2H2O (CW) at 40 °C. Both CW and the applied current density can affect the W content in the coatings. The effect of CW becomes weaker with the increased current density. The Ni-W alloys with 15 ± 5 wt.% W (Ni-15 wt.% W) are obtained from the bath containing 40 g L−1 CW at a high current of 8 A dm−2. The microhardness, corrosion resistance and hydrogen evolution reaction (HER) are measured with a microhardness tester and an electrochemical workstation. The modified properties are studied by heat treatment from 200 to 700 °C. The highest microhardness of 895.62 HV and the better HER property is presented after heat treatment at 400 °C, while the best corrosion resistance in 3.5 wt.% NaCl solution appears at 600 °C

Early assessment of the efficacy of noninvasive ventilation tested by HACOR score to avoid delayed intubation in patients with moderate to severe ARDS

Background: Use of noninvasive ventilation (NIV) in patients with moderate to severe ARDS is controversial. We aimed to use HACOR (combination of heart rate, acidosis, consciousness, oxygenation and respiratory rate) score to comprehensively assess the efficacy of NIV in ARDS patients with PaO 2 /FiO 2  ⩽ 150 mmHg. Methods: Secondary analysis was performed using the data collected from two databases. We screened the ARDS patients who used NIV as a first-line therapy. Patients with PaO 2 /FiO 2  ⩽ 150 mmHg were enrolled. NIV failure was defined as requirement of intubation. Results: A total of 224 moderate to severe ARDS patients who used NIV as a first-line therapy were enrolled. Of them, 125 patients (56%) experienced NIV failure and received intubation. Among the intubated patients, the survivor had shorter time from initiation of NIV to intubation than nonsurvivors (median 10 vs 22 h, p  1 as responders (n  = 102) and the rest to non-responders (n  = 122). Compared to non-responders, the responders had higher HACOR score before NIV. However, the HACOR score was lower in the responders than non-responders after 1–2 h, 12 h, and 24 h of NIV. The responders also had lower NIV failure rate (36% vs 72%, p  < 0.01) and lower 28-day mortality (32% vs 47%, p  = 0.04) than non-responders. Conclusions: NIV failure was high among patients with moderate to severe ARDS. Delayed intubation is associated with increased mortality. The reduction of HACOR score after 1–2 h of NIV can identify the patients who respond well to NIV

Rapid Electrodeposition of Fe–Ni Alloy Foils from Chloride Baths Containing Trivalent Iron Ions

This work presents the rapid electrodeposition of Fe&ndash;Ni alloy foils from chloride baths containing trivalent iron ions at a low pH (&lt;0.0). The effect of the concentration of Ni2+ ions on the content, surface morphology, crystal structure, and tensile property of Fe&ndash;Ni alloys is studied in detail. The results show that the co-deposition of Fe and Ni is controlled by the adsorption of divalent nickel species at low current density and the ionic diffusion at high current density. The current density of preparing smooth and flexible Fe&ndash;Ni alloy foils is increased by increasing the concentration of Ni2+ ions, consequently the deposition rate of Fe&ndash;Ni alloy foils is increased. For example, at 0.6 M Ni2+ ions, the current density can be applied at 50 A&middot;dm&minus;2, along with a high deposition rate of ~288 &mu;m&middot;h&minus;1

Schematic overview of most differentially expressed modules identification.

<p>Identifying the most differentially expressed modules include three key steps. First, the GO co-expressed network is constructed by combined the protein-protein interaction network, which was from the HPRD and BioGRID database, and GO gene sets together. The edges of network were weighed by co-expression level between their corresponding linked nodes. Second, functional modules were identified by the weighted Girvan-Newman algorithm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Newman1" target="_blank">[32]</a>. Finally, functional modules were ranked on their differential levels between recurrent and non-recurrent tumors which were evaluated by the p-SAGE algorithm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Huang1" target="_blank">[38]</a>.</p

The percentage of known colorectal cancer (CRC) genes in top 50–500 MDMs inferred from German dataset.

<p>Known CRC genes were collected from the PubGene (A) or OMIM (B). The percentages were compared with those in top differentially expressed genes (t-test genes) with the same number of genes in top ranked N modules, or GO gene sets with the same amount of top ranked N modules.</p

The prognosis prediction performance.

<p>The comparison of AUC (A) and accuracy (B) for three datasets: Different coloring schemes and shape indicate three independent datasets (orange circle: German dataset; blue diamond: Barrier dataset; green square: GSE5206 dataset). TX_Y methods (X: top 500 or 1000 MDMs; Y: 10 or 18 reference tumors or Leave-One-Out method (LOO)). The filled symbols denote the mean of AUCs; The comparison of accuracies(C), sensitivities (D) and specificities (E) for prognosis prediction between our method and present methods with same datasets, including the LOO results from Lin07 (L) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Lin1" target="_blank">[3]</a>, Garman08 (G) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Garman2" target="_blank">[42]</a>, Barrier06 (B) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Barrier1" target="_blank">[5]</a>, and also the Barrier06's results obtained using 34 tumors (TS34), 18 tumors (TS18) or 10 tumors(TS 10) as the training set. The filled symbols are mean value. *The points in the dotted circle are the outcomes from the methods which were validated using makers discovered by the one and the same dataset.</p