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Front Cover: The cover image shows the design of neural networks or cellular microenvironments based on an original hydrogel microfabrication process. Polyamidoamine hydrogels patterned by an electron beam can adsorb proteins as a function of exposure dose. Individual PC12 cells grow in microfabricated wells showing neurite guidance along channels, thus obtaining interconnected neural circuits. Further details can be found in the full paper by G. Dos Reis, F. Fenili, A. Gianfelice, G. Bongiorno, D. Marchesi, P. E. Scopelliti, A. Borgonovo, A. Podestà, M. Indrieri, E. Ranucci, P. Ferruti, C. Lenardi,* and P. Milani* .

3D scaffolds for tissue engineering typically need to adopt a dynamic culture to foster cell distribution and survival throughout the scaffold. It is, therefore, crucial to know fluids' behavior inside the scaffold architecture, especially for complex porous ones. Here we report a comparison between simulated and measured permeability of a porous 3D scaffold, focusing on different modeling parameters. The scaffold features were extracted by microcomputed tomography (µCT) and representative volume elements were used for the computational fluid-dynamic analyses. The objective was to investigate the sensitivity of the model to the degree of detail of the µCT image and the elements of the mesh. These findings highlight the pros and cons of the modeling strategy adopted and the importance of such parameters in analyzing fluid behavior in 3D scaffolds.

Background The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. Methods LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. Results Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. Conclusions Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. Key points We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.