The reliability of colorimetry is precise(ly) as expected.

Figure S6. Gene ontology (GO) analysis of the differentially expressed genes between C1 and C2. (JPG 403 kb

Facile preparation of colorful liquid marbles and liquid marbles used in water pollutant detection

<p>Liquid marbles are drops coated with a hydrophobic powder. Different liquid marbles can be easily distinguished by the diverse color appearance. Here, we first demonstrate colorful liquid marbles, which were encapsulated with colored superhydrophobic stearate powders. A simple method was used to generate colorful superhydrophobic stearate powders via chemical reactions between sodium stearate and inorganic salts. In addition, the contact angle meter was used to monitor the horizontal profiles of liquid marbles to investigate the lifetime of liquid marbles on the stainless steel surface at room conditions. More important, we have described a proof of qualitative detection of oil pollution and measured the minimum detection limit of some water-soluble pollutants by liquid marbles.</p

DataSheet1_Construction of an original anoikis-related prognostic model closely related to immune infiltration in gastric cancer.ZIP

Background: Anoikis is considered as a particular type of programmed cell death, the weakness or resistance of which contributes greatly to the development and progression of most malignant solid tumors. However, the latent impact of anoikis-related genes (ARGs) on gastric cancer (GC) is still ambiguous. Based on these, this study established an anoikis-related prognostic model of GC to identify the prognosis of patients and provide more effective treatment in clinical practice.Methods: First, we extracted four public datasets containing the gene expression and clinicopathological information of GC, which were worked as the training and validating sets, separately. Then, an anoikis-related survival-predicted model of GC was developed via Lasso and COX regression analyses and verified by using the Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) curve analyses. Next, we assigned GC patients to two groups characterized by the risk score calculated and analyzed somatic mutation, functional pathways, and immune infiltration between the different two groups. Finally, a unique nomogram was offered to clinicians to forecast the personal survival probability of GC patients.Results: Based on seven anoikis-related markers screened and identified, a carcinogenic model of risk score was produced. Patients placed in the high-score group suffered significantly worse overall survival (OS) in four cohorts. Additionally, the model revealed a high sensitivity and specificity to prognosticate the prognoses of GC patients [area under the ROC curve (AUC) at 5-year = 0.713; GSE84437, AUC at 5-year = 0.639; GSE15459, AUC at 5-year = 0.672; GSE62254, AUC at 5-year = 0.616]. Apart from the excellent predictive performance, the model was also identified as an independent prediction factor from other clinicopathological characteristics. Combining anoikis-related prognostic model with GC clinical features, we built a more comprehensive nomogram to foresee the likelihood of survival of GC patients in a given year, showing a well-accurate prediction performance.Conclusion: In summary, this study created a new anoikis-related signature for GC, which has potentially provided new critical insights into survival prediction and individualized therapy development.</p

Prognostic Value of Carbonic Anhydrase IX Immunohistochemical Expression in Renal Cell Carcinoma: A Meta-Analysis of the Literature

<div><p>Background</p><p>Carbonic anhydrase IX (CAIX) protein has been correlated with progression and survival in patients with renal cell carcinoma (RCC). The prognostic value of CAIX in RCC however, remains inconclusive according to published works. This study aimed to analyze CAIX as a biological marker to predict RCC patient prognosis.</p><p>Methods</p><p>A literature search of the PubMed and Web of Knowledge databases was performed to retrieve original studies from their inception to December of 2013. Fifteen studies, collectively including a total of 2611 patients with renal cell carcinoma, were carefully reviewed. Standard meta-analysis methods were applied to evaluate the prognostic impact of CAIX expression on patient prognosis. The hazard ratio (HR) and its 95% confidence interval (CI) were recorded for the relationship between CAIX expression and survival, and the data were analyzed using Review Manager 5.2 software and Stata software 11.0.</p><p>Results</p><p>In patients with RCC, low CAIX expression was associated with poor disease-specific survival (HR = 1.89, 95% CI: 1.20–2.98, <i>P</i> = 0.006), unfavorable progression-free survival (HR = 2.62, 95% CI: 1.14–6.05, <i>P</i> = 0.02) and worse overall survival (HR = 2.03, 95% CI: 1.28–3.21, <i>P</i> = 0.002). Furthermore, low CAIX expression was significantly associated with the presence of lymph node metastases (odds ratio (OR) = 0.31, 95% CI = 0.15–0.62, <i>P</i> = 0.0009) and distant metastases (OR = 0.66, 95% CI = 0.46–0.96, <i>P</i> = 0.03) and predicted a higher tumor grade (OR = 0.41, 95% CI = 0.31–0.54, <i>P</i><0.00001).</p><p>Conclusions</p><p>Low CAIX expression most likely indicates poor prognosis in RCC patients. Moreover, low CAIX expression was significantly associated with unfavorable clinicopathological factors. To strengthen our findings, further well-designed prospective studies should be conducted to investigate the role of CAIX expression in RCC.</p></div

Meta-analysis of the association between low CAIX expression and lymph node metastasis.

<p>Meta-analysis of the association between low CAIX expression and lymph node metastasis.</p

Meta-analysis of CAIX expression and progression-free survival.

<p>Meta-analysis of CAIX expression and progression-free survival.</p

Basic characteristics of included studies and quality assessment.

<p>Abbreviation: NA, not available; OS, overall survival; DSS, disease-specific survival; PFS, progression-free survival.</p><p>Basic characteristics of included studies and quality assessment.</p

Funnel plot of CAIX expression and disease-specific survival on A, all inclusion studies; B, by excluding the low quality score studies (quality score≤6).

<p>SE = standard error, HR = hazard ratio.</p

Meta-analysis of the association between low CAIX expression and grade.

<p>Meta-analysis of the association between low CAIX expression and grade.</p

Meta-analysis of CAIX expression and overall survival on A, all inclusion studies; B, by excluding the low quality score studies (quality score≤6).

<p>Meta-analysis of CAIX expression and overall survival on A, all inclusion studies; B, by excluding the low quality score studies (quality score≤6).</p