Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China.

Contamination of agricultural soil by heavy metals has become a global issue concerning food security and human health risk. In this study, a soil investigation was conducted to evaluate metals accumulation, potential ecological and health risks as well as to identify sources of metals in paddy soils in Hanzhong City, which is located in a sedimentary basin. Ninety-two (92) surface soil samples (bulk soil) and their corresponding rice samples, 21 irrigation water samples, and 18 fertilizer samples were collected from two typical counties and quantified for the heavy metals (i.e., As, Cd, Cu, Hg, Pb, and Zn) concentrations. The results showed that As, Cd, and Zn were the main contaminants in soils in the studied area. Additionally, elevated Hg content in soils might also pose risks to the local ecosystem. Cadmium and As demonstrated high mobility, and their average contents in rice grains were slightly higher than the permissible threshold (0.20 mg kg-1). Moreover, Pb, As, and Cd intake via rice consumption might result in potential risks to local residents. Metal distribution revealed that pollution in the studied area is non-homogeneous, and agricultural activities (As, Cu, and Cd), transportation emission (Cu and Pb), coal combustion (Hg and As), and smelting activities (Zn, Pb, and Cu) were ascertained as the potential sources based on the Positive matrix factorization (PMF) analysis results.

Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data.

Triple negative breast cancers (TNBCs) are highly heterogeneous and aggressive without targeted treatment. Here, we aim to systematically dissect TNBCs from a prognosis point of view by building a subnetwork atlas for TNBC prognosis through integrating multi-dimensional cancer genomics data from The Cancer Genome Atlas (TCGA) project and the interactome data from three different interaction networks. The subnetworks are represented as the protein-protein interaction modules perturbed by multiple genetic and epigenetic interacting mechanisms contributing to patient survival. Predictive power of these subnetwork-derived prognostic models is evaluated using Monte Carlo cross-validation and the concordance index (C-index). We uncover subnetwork biomarkers of low oncogenic GTPase activity, low ubiquitin/proteasome degradation, effective protection from oxidative damage, and tightly immune response are linked to better prognosis. Such a systematic approach to integrate massive amount of cancer genomics data into clinical practice for TNBC prognosis can effectively dissect the molecular mechanisms underlying TNBC patient outcomes and provide potential opportunities to optimize treatment and develop therapeutics.