Genetically encoded sender-receiver system in 3D mammalian cell culture

Engineering spatial patterning in mammalian cells, employing entirely genetically encoded components, requires solving several problems. These include how to code secreted activator or inhibitor molecules and how to send concentration-dependent signals to neighboring cells, to control gene expression. The Madin-Darby Canine Kidney (MDCK) cell line is a potential engineering scaffold as it forms hollow spheres (cysts) in 3D culture and tubulates in response to extracellular hepatocyte growth factor (HGF). We first aimed to graft a synthetic patterning system onto single developing MDCK cysts. We therefore developed a new localized transfection method to engineer distinct sender and receiver regions. A stable reporter line enabled reversible EGFP activation by HGF and modulation by a secreted repressor (a truncated HGF variant, NK4). By expanding the scale to wide fields of cysts, we generated morphogen diffusion gradients, controlling reporter gene expression. Together, these components provide a toolkit for engineering cell-cell communication networks in 3D cell culture.Centro Regional de Estudios Genómico

A hysteretic multiscale formulation for nonlinear dynamic analysis of composite materials

This article has been made available through the Brunel Open Access Publishing Fund.A new multiscale finite element formulation is presented for nonlinear dynamic analysis of heterogeneous structures. The proposed multiscale approach utilizes the hysteretic finite element method to model the microstructure. Using the proposed computational scheme, the micro-basis functions, that are used to map the microdisplacement components to the coarse mesh, are only evaluated once and remain constant throughout the analysis procedure. This is accomplished by treating inelasticity at the micro-elemental level through properly defined hysteretic evolution equations. Two types of imposed boundary conditions are considered for the derivation of the multiscale basis functions, namely the linear and periodic boundary conditions. The validity of the proposed formulation as well as its computational efficiency are verified through illustrative numerical experiments

A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules

Background: Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk. Methods: 502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores. Findings: 499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77–0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70–0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80–0.93) compared to 0.67 (95% CI 0.55–0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75–0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63–0.85). 18 out of 22 (82%) malignant nodules in the Herder 10–70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention. Interpretation: The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. Funding This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316)

Shaking table tests and numerical analyses on a scaled dry-joint arch undergoing windowed sine pulses

The damages occurred during recent seismic events have emphasised the vulnerability of vaulted masonry structures, one of the most representative elements of worldwide cultural heritage. Although a certain consensus has been reached regarding the static behaviour of masonry arches, still more efforts are requested to investigate their dynamic behaviour. In this regard, the present paper aims to investigate the performance of a scaled dry-joint arch undergoing windowed sine pulses. A feature tracking based measuring technique was employed to evaluate the displacement of selected points, shading light on the failure mechanisms and gathering data for the calibration of the numerical model. This was built according to a micro-modelling approach of the finite element method, with voussoirs assumed very stiff and friction interface elements. Comparisons with existing literature are also stressed, together with comments about scale effects.This work was partly financed by FEDER funds through the Competitivity Factors Operational Programme-COMPETE and by national funds through FCT-Foundation for Science and Technology within the scope of the Project POCI-01-0145-FEDER-007633.info:eu-repo/semantics/publishedVersio