Clinical implications and mechanism of histopathological growth pattern in colorectal cancer liver metastases

Liver is the most common site of metastases of colorectal cancer,and liver metastases present with distinct histopathological growth patterns(HGPs),including desmoplastic,pushing and replacement HGPs and two rare HGPs.HGP is a miniature of tumor-host reaction and reflects tumor biology and pathological features as well as host immune dynamics.Many studies have revealed the association of HGPs with carcinogenesis,angiogenesis,and clinical outcomes and indicates HGP functions as bond between microscopic characteristics and clinical implications.These findings make HGP a candidate marker in risk stratification and guiding treatment decision-making,and a target of imaging observation for patient screening.Of note,it is crucial to determine the underlying mechanism shaping HGP,for instance,immune infiltration and extracellular matrix remodeling in desmoplastic HGP,and aggressive characteristics and special vascularization in replacement HGP(rHGP).We highlight the importance of aggressive features,vascularization,host immune and organ structure in formation of HGP,hence propose a novel"advance under camouflage"hypothesis to explain the formation of rHGP.

Mapping high resolution National Soil Information Grids of China

Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.