A key challenge in tissue engineering is overcoming cell death in the scaffold interior due to the limited diffusion of oxygen and nutrients therein. We hypothesized here that immobilizing a gradient of vascular endothelial growth factor would guide endothelial cells into the interior of the scaffold thereby enhancing angiogenesis. The protein was immobilized onto a collagen scaffold through carbodiimide chemistry by one of the three methods experimented: placing 5 µl of the solution at the center of the scaffold to create a ~2 ng/ml/mm gradient in a radial direction. D4T endothelial cells were observed to be guided by this VEGF-165 gradient deep into the center of the scaffold compared to both uniformly immobilized VEGF-165 and VEGFfree controls. We concluded that the VEGF-165 gradient scaffolds promoted the migration, and not proliferation, of cells deep into the scaffold. These gradient scaffolds provide the foundation for future in vivo tissue engineering studies.iii

Purpose: The novel coronavirus disease (COVID-19) pandemic has swept the globe, with a domino effect on medical education and training. In this study, we surveyed Canadian radiology residents to understand the impact of the pandemic on their residency training, strategies utilized by the residency programs in mitigating those impacts, and factors important to residents in the selection of educational resources on COVID-19. Methods: A 10-item questionnaire was distributed to 460 resident members of the Canadian Association of Radiologists. The survey was open for 2 weeks, with a reminder sent at half-way mark. Results: We received 96 responses (response rate: 20.9%). The 4 highest affected domains of training were daytime case volumes (92.4%), daytime schedules (87.4%), internal and external assessments (86.5%), and vacation/travel (83.3%). Virtual teaching rounds (91.7%), change in schedules to allow staying home (78.1%), and virtual/phone readouts (72.9%) were the most utilized strategies by the Canadian radiology residency programs. Overall stress of exposure to the disease was moderate to low (86.5%). A minority of the residents were redeployed (6.2%), although most (68.8%) were on standby for redeployment. Residents preferred published society guidelines (92.3%), review papers (79.3%), video lectures (79.3%), and web tools (76.9%) for learning about COVID-19 imaging manifestations. Conclusion: The COVID-19 pandemic has had a significant impact on various domains of the Canadian radiology residency programs, which has been mitigated by several strategies employed by the training programs.

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http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13122349/-/DC1.

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During animal development, cells need to sense and adapt to mechanical forces from their environment. Ultimately, these forces are transduced through the actomyosin cortex. How the cortex simultaneously responds to and creates forces during cytokinesis is not well understood. Here we show that, under mechanical stress, cortical actomyosin flow can switch polarization during cytokinesis in the C. elegans embryo. In unstressed embryos, longitudinal cortical flow contributes to contractile ring formation, while rotational cortical flow is additionally induced in uniaxially loaded embryos, i.e. embryos compressed between two plates. Rotational flow depends on astral microtubule signals and is required for the redistribution of the actomyosin cortex in loaded embryos. Rupture of longitudinally aligned cortical fibers during cortex rotation releases tension, initiates orthogonal longitudinal flow and, thereby, contributes to furrowing in loaded embryos. Moreover, actomyosin regulators involved in RhoA regulation, cortical polarity and chirality are all required for rotational flow, and become essential for cytokinesis under mechanical stress. In sum, our findings extend the current framework of mechanical stress response during cell division and show scaling of orthogonal cortical flows to the amount of mechanical stress.

We previously showed that the sequential, but not simultaneous, culture of endothelial cells (ECs), fibroblasts (FBs), and cardiomyocytes (CMs) resulted in elongated, beating cardiac organoids. We hypothesized that the expression of Cx43 and contractile function are mediated by vascular endothelial growth factor (VEGF) released by nonmyocytes during the preculture period. Cardiac organoids (~200 μm diameter) were cultivated in microchannels to enable rapid screening. Three experimental groups were formed: (i) Simultaneous Preculture (ECs+FBs for 48 h, followed by CMs), (ii) Sequential Preculture (ECs for 24 h, FBs for 24 h, followed by CMs), and (iii) Simultaneous Triculture (ECs+FBs+CMs). Controls included CMs only, FBs only, and ECs only groups, and preculture with ECs only or FBs only. The highest VEGF levels were found in the Preculture groups [Simultaneous Preculture, 8.9±2.7 ng/(mL·h(-1)); Sequential Preculture, 16.6±3.4 ng/(mL·h(-1))], as compared with Simultaneous Triculture where VEGF was not detectable, as shown by enzyme-linked immunosorbent assay. Analytical flow cytometry showed that VEGFR2 was expressed by ECs (86%±2 VEGFR2+), FBs (44%±1 VEGFR2+), and CMs (49%±2 VEGFR2+), showing that all three cell types were capable of responding to changes in VEGF. Addition of anti-VEGF neutralizing IgG (0.4 μg/mL) to Simultaneous Preculture resulted in 3-fold decrease in Cx43 mRNA and 1.5-fold decrease in Cx43 protein, while Simultaneous Triculture supplemented with VEGF ligand (30 ng/mL) had a threefold increase in Cx43 mRNA and a twofold increase in Cx43 protein. Addition of a small molecule inhibitor of the VEGFR2 receptor (19.4 nM) to Sequential Preculture caused a 1.4-fold decrease in Cx43 mRNA and a 4.1-fold decrease in Cx43 protein. Cx43 was localized within CMs, and not within FBs or ECs. Enriched CM organoids and Sequential Preculture organoids grown in the presence of VEGFR2 inhibitor displayed low levels of Cx43 and poor functional properties. Taken together, these results suggest that endogenous VEGF-VEGFR2 signaling enhanced Cx43 expression and cardiac function in engineered cardiac organoids.

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.