Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features extraction, a radiomics signature was constructed in derivation cohort（346 CRC patients）. A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen（CEA） level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation（separate from the cohort used to build the model） was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results： The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell＇s concordance index（c-index）： 0.817; 95% confidence interval（95% CI）： 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination（c-index： 0.803; 95% CI： 0.794–0.812）.Conclusions： Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

A multi-scale modelling framework to guide management of plant invasions in a transboundary context

Background：Attention has recently been drawn to the issue of transboundary invasions,where species introduced and naturalized in one country cross international borders and become problematic in neighbouring countries.Robust modelling frameworks,able to identify the environmental drivers of invasion and forecast the current and future potential distribution of invasive species,are needed to study and manage invasions.Limitations due to the lack of species distribution and environmental data,or assumptions of modelling tools,often constrain the reliability of model predictions.Methods：We present a multiscale spatial modelling framework for transboundary invasions,incorporating robust modelling frameworks（Multimodel Inference and Ensemble Modelling） to overcome some of the limitations.The framework is illustrated using Hakea sericea Schrad.（Proteaceae）,a shrub or small tree native to Australia and invasive in several regions of the world,including the Iberian Peninsula.Two study scales were considered：regional scale（western Iberia,including mainland Portugal and Galicia） and local scale（northwest Portugal）.At the regional scale,the relative importance of environmental predictors sets was evaluated and ranked to determine the main general drivers for the species distribution,while the importance of each environmental predictor was assessed at the local scale.The potential distribution of H.sericea was spatially projected for both scale areas.Results：Model projections for western Iberia suggest that a large area is environmentally suitable in both Portugal and Spain.Climate and landscape composition sets were the most important determinants of this regional distribution of the species.Conversely,a geological predictor（schist lithology） was more important in explaining its local-scale distribution.Conclusions：After being introduced to Portugal,H.sericea has become a transboundary invader by expanding in parts of Galicia（Spain）.The fact that a larger area is predicted as environmentally suitable in Spain raises concerns regarding its potential continued expansion.This highlights the importance of transboundary cooperation in the early management of invasions.By reliably identifying drivers and providing spatial projections of invasion at multiple scales,this framework provides insights for the study and management of biological invasions,including the assessment of transboundary invasion risk.

3D bioprinted hyaluronic acid‑based cell‑laden scaffold for brain microenvironment simulation

Treatments for lesions in central nervous system(CNS)are always faced with challenges due to the anatomical and physiological particularity of the CNS despite the fact that several achievements have been made in early diagnosis and precision medicine to improve the survival and quality of life of patients with brain tumors in recent years.Understanding the complexity as well as role of the microenvironment of brain tumors may suggest a better revealing of the molecular mechanism of brain tumors and new therapeutic directions,which requires an accurate recapitulation of the complex microenvironment of human brain in vitro.Here,a 3D bioprinted in vitro brain matrix-mimetic microenvironment model with hyaluronic acid(HA)and normal glial cells(HEBs)is developed which simulates both mechanical and biological properties of human brain microenvironment in vivo through the investigation of the formulation of bioinks and optimization of printing process and parameters to study the effects of different concentration of gelatin(GA)within the bioink and different printing structures of the scaffold on the performance of the brain matrix-mimetic microenvironment models.The study provides experimental models for the exploration of the multiple factors in the brain microenvironment and scaffolds for GBM invasion study.

Exotic plant species with longer seed bank longevity and lower seed dry mass are more likely to be invasive in China

Globalization of social and economic activities has led to the large-scale redistribution of plant species.It is still unclear how the traits aid the successful invasion of alien species.Here,we downloaded global plant trait data from the TRY-Plant Trait Database and classified alien species in China into four groups:high,medium,need attention and harmless according to their distribution and degree of harm to local plant communities based on existed studies.The relationship between plant functional traits and invasion level was clarified,and we established a prediction model based on plant functional traits and taxonomy.The results showed that species with smaller seeds,smaller individuals,lower special leaf area and longer seed bank longevity(SL)are more likely to be an invasive species after introduction to foreign ecosystems.In summary,exotic species with longer SL and lower seed dry mass are more likely to be invasive in China.We also trained two predictive models to check if we can predict a species’invasion.Combining the two models together,statistically,we could predict if a species is invasive from its traits and taxonomy with a 91.84%accuracy.This model could help local governments,managers and stakeholders to evaluate shall we introduce some plant species in China.