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Yong Huang

Researcher at Beijing Institute of Technology

Publications -  179
Citations -  2531

Yong Huang is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Optical coherence tomography & Medicine. The author has an hindex of 26, co-authored 158 publications receiving 1963 citations. Previous affiliations of Yong Huang include Sun Yat-sen University & Center for Devices and Radiological Health.

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A multiphase transitioning peptide hydrogel for suturing ultrasmall vessels

TL;DR: A multi-phase transitioning peptide hydrogel that can be injected into the lumen of vessels to facilitate suturing is reported, adding a new tool to the armamentarium for micro- and supermicrosurgical procedures.
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Spatial-temporal clusters and risk factors of hand, foot, and mouth disease at the district level in Guangdong Province, China.

TL;DR: The findings showed a strong association between HFMD and meteorological factors, which may assist in predicting HFMD incidence and children years old were more susceptible to HFMD.
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Distortion-free freehand-scanning OCT implemented with real-time scanning speed variance correction

TL;DR: This study incorporates the theoretical speckle model into the decorrelation function and explicitly correlated the cross-correlation coefficient to the lateral displacement between adjacent A-scans to develop and study a freehand-scanning OCT system capable of real-time scanning speed correction and distortion-free imaging.
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Tissue-engineered regeneration of completely transected spinal cord using induced neural stem cells and gelatin-electrospun poly (lactide-co-glycolide)/polyethylene glycol scaffolds.

TL;DR: Induced mouse embryonic fibroblasts directly reprogrammed into neural stem cells (iNSCs) were investigated as a cell source and shown to survive, with the ability to self-renew and undergo neural differentiation into neurons and glial cells within the 3D scaffolds in vivo.
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Deep Belief Network Modeling for Automatic Liver Segmentation

TL;DR: An automatic feature learning algorithm based on the deep belief network (DBN) for liver segmentation based on training by a DBN for unsupervised pretraining and supervised fine tuning is proposed.