Qing Lv

One of the fundamental principles underlying tissue engineering approaches is that newly formed tissue must maintain sufficient vascularization to support its growth. Efforts to induce vascular growth into tissue-engineered scaffolds have recently been dedicated to developing novel strategies to deliver specific biological factors that direct the recruitment of endothelial cell (EC) progenitors and their differentiation. The challenge, however, lies in orchestration of the cells, appropriate biological factors, and optimal factor doses. This study reports an approach as a step forward to resolving this dilemma by combining an ex vivo gene transfer strategy and EC transplantation. The utility of this approach was evaluated by using 3D poly(lactide-co-glycolide) (PLAGA) sintered microsphere scaffolds for bone tissue engineering applications. Our goal was achieved by isolation and transfection of adipose-derived stromal cells (ADSCs) with adenovirus encoding the cDNA of VEGF. We demonstrated that the combination of VEGF releasing ADSCs and ECs results in marked vascular growth within PLAGA scaffolds. We thereby delineate the potential of ADSCs to promote vascular growth into biomaterials.

Breast cancer is one of the most frequently diagnosed solid cancers. Mammography is the most commonly used screening technology for detecting breast cancer. Traditional machine learning methods of mammographic image classification or segmentation using manual features require a great quantity of manual segmentation annotation data to train the model and test the results. But manual labeling is expensive, time-consuming, and laborious, and greatly increases the cost of system construction. To reduce this cost and the workload of radiologists, an end-to-end full-image mammogram classification method based on deep neural networks was proposed for classifier building, which can be constructed without bounding boxes or mask ground truth label of training data. The only label required in this method is the classification of mammographic images, which can be relatively easy to collect from diagnostic reports. Because breast lesions usually take up a fraction of the total area visualized in the mammographic image, we propose different pooling structures for convolutional neural networks(CNNs) instead of the common pooling methods, which divide the image into regions and select the few with high probability of malignancy as the representation of the whole mammographic image. The proposed pooling structures can be applied on most CNN-based models, which may greatly improve the models' performance on mammographic image data with the same input. Experimental results on the publicly available INbreast dataset and CBIS dataset indicate that the proposed pooling structures perform satisfactorily on mammographic image data compared with previous state-of-the-art mammographic image classifiers and detection algorithm using segmentation annotations.

Dynamic flow culture bioreactor systems have been shown to enhance in vitro bone tissue formation by facilitating mass transfer and providing mechanical stimulation. Our laboratory has developed a biodegradable poly (lactic acid glycolic acid) (PLAGA) mixed scaffold consisting of lighter-than-water (LTW) and heavier-than-water (HTW) microspheres as potential matrices for engineering tissue using a high aspect ratio vessel (HARV) rotating bioreactor system. We have demonstrated enhanced osteoblast differentiation and mineralization on PLAGA scaffolds in the HARV rotating bioreactor system when compared with static culture. The objective of the present study is to improve the mechanical properties and bioactivity of polymeric scaffolds by designing LTW polymer/ceramic composite scaffolds suitable for dynamic culture using a HARV bioreactor. We employed a microsphere sintering method to fabricate three-dimensional PLAGA/nano-hydroxyapatite (n-HA) mixed scaffolds composed of LTW and HTW composite microspheres. The mechanical properties, pore size and porosity of the composite scaffolds were controlled by varying parameters, such as sintering temperature, sintering time, and PLAGA/n-HA ratio. The PLAGA/n-HA (4:1) scaffold sintered at 90 degrees C for 3 h demonstrated the highest mechanical properties and an appropriate pore structure for bone tissue engineering applications. Furthermore, evaluation human mesenchymal stem cells (HMSCs) response to PLAGA/n-HA scaffolds was performed. HMSCs on PLAGA/n-HA scaffolds demonstrated enhanced proliferation, differentiation, and mineralization when compared with those on PLAGA scaffolds. Therefore, PLAGA/n-HA mixed scaffolds are promising candidates for HARV bioreactor-based bone tissue engineering applications.