Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study.

BACKGROUND: Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. METHODS: 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. RESULTS: The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. CONCLUSION: Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.

[Community Characteristics of ANAMMOX Bacteria in Subsurface Flow Constructed Wetland (SSFCW) for Processing of Aquaculture Waster Water].

Anaerobic ammonium oxidation (ANAMMOX) is one of the important functions in waste water treatment by subsurface flow constructed wetland (SSFCW), however, there are few studies on ANAMMOX in SSFCW environment at present. The community characteristics of ANAMMOX in the SSFCW of processing aquaculture waste water were explored in this study. In order to analyze the structure, diversity and abundance of ANAMMOX bacteria, several 16S rRNA clone libraries were constructed and real-time PCR targeting specific 16S rRNA genes together with diversity analysis was adopted. The obtained results showed that the SSFCW identified a total of three unknown clusters and two known clusters including Candidatus brocadia and Candidatus kuenenia. The dominant cluster was Candidatus brocadia. The highest diversity levels of ANAMMOX bacteria occurred in autumn (H', 1.21), while the lowest in spring (H', 0.64). The abundance of ANAMMOX bacteria in SSFCW environment ranged from 8.0 x 10(4) to 9.4 x 10(6) copies x g(-1) of fresh weight and the copy number of total bacterial 16S rRNA genes ranged from 7.3 x 10(9) to 9.1 x 10(10) copies x g(-1) of fresh weight during culture cycle. There were significant differences in the ANAMMOX bacteria abundances of different stratum and seasons in SSFCW environment, but the differences in total bacterial abundances were not obvious. In addition, the differences in ANAMMOX bacteria abundances in different stratum and seasons in SSFCW environment were irregular in different culture cycle. According to the distribution characteristics of ANAMMOX bacteria in the wetland, the denitrification effect of SSFCW could be improved by changing the supplying manners of aquaculture wastewater and adjusting the structure of wetland. The research results will provide reference for further optimizing the SSFCW and improving the efficiency of purification.