Highlights Radiographic chest images can be used to more accurately detect COVID-19 and assess disease severity. Among different imaging modalities, chest X-ray radiography has advantages of low cost, low radiation dose, wide accessibility and easy-to-operate in general or community hospitals. This study aims to develop and test a new deep learning model of chest X-ray images to detect COVID-19 induced pneumonia. For this purpose, we assembled a relatively large chest X-ray image dataset involving 8,474 cases, which are divided into three groups of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. After applying a preprocessing algorithm to detect and remove diaphragm regions depicting on images, a histogram equalization algorithm and a bilateral filter are applied to process the original images to generate two sets of filtered images. Then, the original image plus these two filtered images are used as inputs of three channels of the CNN deep learning model, which increase learning information of the model. In order to fully take advantages of the pre-optimized CNN models, this study uses a transfer learning method to build a new model to detect and classify COVID-19 infected pneumonia. A VGG16 based CNN model was originally trained using ImageNet and fine-tuned using chest X-ray images in this study. To reduce the bias in training and testing the CNN model, dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class in all three COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) groups. Testing on a subset of 2544 cases, the CNN model yields 94.5% accuracy in classifying three subsets of cases and 98.1% accuracy in detecting COVID-19 infected pneumonia cases, which are significantly higher than the model directly trained using the original images without applying two image preprocessing steps to remove diaphragm and generate two filtered images.

Rationale In response to injury, the rodent heart is capable of virtually full regeneration via cardiomyocyte proliferation very early in life. This regenerative capacity, however, is diminished as early as one week post-natal and remains lost in adulthood. The mechanisms that dictate post injury cardiomyocyte proliferation early in life remain unclear. Objective To delineate the role of miR-34a, a regulator of age-associated physiology, in regulating cardiac regeneration secondary to myocardial infarction (MI) in neonatal and adult mouse hearts. Methods and Results Cardiac injury was induced in neonatal and adult hearts through experimental MI via coronary ligation. Adult hearts demonstrated overt cardiac structural and functional remodeling, whereas neonatal hearts maintained full regenerative capacity and cardiomyocyte proliferation, and recovered to normal levels within one week time. As early as one week post-natal, miR-34a expression was found to have increased and was maintained at high levels throughout the lifespan. Intriguingly, seven days following MI, miR-34a levels further increased in the adult but not neonatal hearts. Delivery of a miR-34a mimic to neonatal hearts prohibited both cardiomyocyte proliferation and subsequent cardiac recovery post-MI. Conversely, locked nucleic acid-based anti-miR-34a treatment diminished post-MI miR-34a upregulation in adult hearts and significantly improved post-MI remodeling. In isolated cardiomyocytes, we found that miR-34a directly regulated cell cycle activity and death via modulation of its target genes, including Bcl2, Cyclin D1, and Sirt1. Conclusions miR-34a is a critical regulator of cardiac repair and regeneration post-MI in neonatal hearts. Modulation of miR-34a may be harnessed for cardiac repair in adult myocardium.

Microfabrication technology provides a highly versatile platform for engineering hydrogels used in biomedical applications with high-resolution control and injectability. Herein, we present a strategy of microfluidics-assisted fabrication photo-cross-linkable gelatin microgels, coupled with providing protective silica hydrogel layer on the microgel surface to ultimately generate gelatin-silica core–shell microgels for applications as in vitro cell culture platform and injectable tissue constructs. A microfluidic device having flow-focusing channel geometry was utilized to generate droplets containing methacrylated gelatin (GelMA), followed by a photo-cross-linking step to synthesize GelMA microgels. The size of the microgels could easily be controlled by varying the ratio of flow rates of aqueous and oil phases. Then, the GelMA microgels were used as in vitro cell culture platform to grow cardiac side population cells on the microgel surface. The cells readily adhered on the microgel surface and proliferated over time while maintaining high viability (∼90%). The cells on the microgels were also able to migrate to their surrounding area. In addition, the microgels eventually degraded over time. These results demonstrate that cell-seeded GelMA microgels have a great potential as injectable tissue constructs. Furthermore, we demonstrated that coating the cells on GelMA microgels with biocompatible and biodegradable silica hydrogels via sol–gel method provided significant protection against oxidative stress which is often encountered during and after injection into host tissues, and detrimental to the cells. Overall, the microfluidic approach to generate cell-adhesive microgel core, coupled with silica hydrogels as a protective shell, will be highly useful as a cell culture platform to generate a wide range of injectable tissue constructs.