A new prognostic model of esophageal squamous cell carcinoma based on Cloud-least squares support vector machine.

Background: In view of the low accuracy of the prognosis model of esophageal squamous cell carcinoma (ESCC), this study aimed to optimize the least squares support vector machine (LSSVM) algorithm to determine the uncertain prognostic factors using a Cloud model, and consequently, to establish a new high-precision prognosis model of ESCC. Methods: We studied 4,771 ESCC patients(training samples) from the Surveillance, Epidemiology, and End Results (SEER) database and 635 ESCC patients(validation samples) from the Henan Provincial Center for Disease Control and Prevention (HCDC) database, with the same exclusion criteria and inclusion criteria for both databases, and obtained permission to obtain a research data file in the SEER database from the National Cancer Institute. The independent risk factors were analyzed using the log-rank method, survival curves, univariate and multivariate Cox analysis. Finally, the independent prognostic factors were used to construct the nomogram, random forest and Cloud-LSSVM prognostic models were utilized for validation. Results: The overall median survival time of the SEER database was 14 months (HCDC samples was 46 months), the mean survival time was 26.5 months (HCDC samples was 36.8 months), and the 3-year survival rate was 65.8%. This is because most of the patients with Henan samples are early ESCC, and most of the Seer patients are T3 and T4 people. The multivariate Cox analysis showed that age at diagnosis (P<0.001), sex (P=0.001), race (P=0.002), differentiation grade (P<0.001), pathologic T category (P<0.001), and pathologic M category (P<0.001) were the factors affecting the prognosis of ESCC patients. The SEER data and HCDC database results showed that the accuracy of the Cloud-LSSVM (C-index =0.71, 0.689) model is higher than the differentiation grade (C-index =0.548, 0.506), random forest (C-index =0.649, 0.498), and nomogram (C-index =0.659, 0.563). This new model can realize the unity of the randomness and fuzziness of the Cloud model and utilize the powerful learning and non-linear mapping abilities of LSSVM. Conclusions: Due to the difference of clans between training samples and test samples, the accuracy of prediction is generally not high, but the accuracy of Cloud-LSSVM model is much higher than other models. The new model provides a clear prognostic superiority over the random forest, nomogram, and other models.

A Prognostic Model Based on mRNA Expression Analysis of Esophageal Squamous Cell Carcinoma.

Background: The aim of this study was to identify prognostic markers for esophageal squamous cell carcinoma (ESCC) and build an effective prognostic nomogram for ESCC. Methods: A total of 365 patients with ESCC from three medical centers were divided into four cohorts. In the discovery phase of the study, we analyzed transcriptional data from 179 cancer tissue samples and identified nine marker genes using edgeR and rbsurv packages. In the training phase, penalized Cox regression was used to select the best marker genes and clinical characteristics in the 179 samples. In the verification phase, these marker genes and clinical characteristics were verified by internal validation cohort (n = 58) and two external cohorts (n = 81, n = 105). Results: We constructed and verified a nomogram model based on multiple clinicopathologic characteristics and gene expression of a patient cohort undergoing esophagectomy and adjuvant radiochemotherapy. The predictive accuracy for 4-year overall survival (OS) indicated by the C-index was 0.75 (95% CI, 0.72-0.78), which was statistically significantly higher than that of the American Joint Committee on Cancer (AJCC) seventh edition (0.65). Furthermore, we found two marker genes (TM9SF1, PDZK1IP) directly related to the OS of esophageal cancer. Conclusion: The nomogram presented in this study can accurately and impersonally predict the prognosis of ESCC patients after partial resection of the esophagus. More research is required to determine whether it can be applied to other patient populations. Moreover, we found two marker genes directly related to the prognosis of ESCC, which will provide a basis for future research.

Active capping technology: a new environmental remediation of contaminated sediment.

The management and treatment of contaminated sediment is a worldwide problem and poses major technical and economic challenges. Nowadays, various attempts have been committed to investigating a cost-effective way in contaminated sediment restoration. Among the remediation options, in situ capping turns out to be a less expensive, less disruptive, and more durable approach. However, by using the low adsorption capacity materials, traditional caps do not always fulfill the reduction of risks that can be destructive for human health, ecosystem, and even natural resources. Active caps, therefore, are designed to employ active materials (activated carbon, apatite, zeolite, organoclay, etc.) to strengthen their adsorption and degradation capacity. The active capping technology promises to be a permanent and cost-efficient solution to contaminated sediments. This paper provides a review on the types of active materials and the ways of these active materials employed in recent active capping studies. Cap design considerations including site-specific conditions, diffusion/advection, erosive forces, and active material selection that should be noticed in an eligible remediation project are also presented.

Quaternary ammonium compounds (QACs): a review on occurrence, fate and toxicity in the environment.

Quaternary ammonium compounds (QACs) are widely applied in household and industrial products. Most uses of QACs can be expected to lead to their release to wastewater treatment plants (WWTPs) and then dispersed into various environmental compartments through sewage effluent and sludge land application. Although QACs are considered to be aerobically biodegradable, the degradation is affected by its chemical structures, dissolved oxygen concentration, complexing with anionic surfactants, etc. High abundance of QACs has been detected in sediment and sludge samples due to its strong sorption and resistance to biodegradation under anoxic/anaerobic conditions. QACs are toxic to a lot of aquatic organisms including fish, daphnids, algae, rotifer and microorganisms employed in wastewater treatment systems. And antibiotic resistance has emerged in microorganisms due to excessive use of QACs in household and industrial applications. The occurrence of QACs in the environment is correlated with anthropogenic activities, such as wastewater discharge from WWTPs or single source polluters, and sludge land application. This article also reviews the analytical methods for determination of QACs in environmental compartments including surface water, wastewater, sewage sludge and sediments.

Effects of sediment geochemical properties on heavy metal bioavailability.

As the largest container and resource of metals, sediment has a special role in the fate of metals. Factors influencing bioavailability of heavy metals in sediment have never been comprehensively considered and the sediment properties still fail to understand and even controversial. In this review, the mechanisms of sediment properties such as acid-volatile sulfides (AVS), organic matter, texture (clay, silt or sand) and geology, organism behaviors as well as those influencing the bioavailability of metals were analyzed. Under anoxic condition, AVS mainly reduce the solubility and toxicity of metals, while organic matters, Fe-Mn oxides, clay or silt can stabilize heavy metals in elevated oxidative-reductive potential (ORP). Other factors including the variation of pH, redox potential, aging as well as nutrition and the behavior of benthic organism in sediment also largely alter metals mobility and distribution. These factors are often inter-related, and various toxicity assessment methods used to evaluate the bioavailability of trace metals have been also discussed. Additionally, we expect that some novel synthetic materials like polysulfides, nano-materials, provide the substantial amendments for metals pollution in sediment.