Microscopic and ultra-microscopic vascular sutures are indispensable in the surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, such as vascular patency, narrow, and blocked, determines the success rate of the operation. If we can grasp the golden window period after blood vessel suture and before muscle tissue suture to achieve accurate and objective assessment of blood vessel status, it can not only reduce medical costs but also improve social benefits. Doppler optical coherence tomography enables high-speed, high-resolution imaging of biological tissues, especially microscopic and ultra-microscopic blood vessels. Using Doppler optical coherence tomography to image the sutured blood vessels, not only the three-dimensional structure of the blood vessels, but also the blood flow information can be obtained. By extracting the contour of blood vessel wall and the contour of blood flow area, the three-dimensional shape of blood vessel can be reconstructed in three dimensions, which provides parameter support for the assessment of blood vessel status. In this work, we propose a neural network-based multi-classification deep learning model that can simultaneously and automatically extract blood vessel boundaries from Doppler OCT vessel intensity images and the contours of blood flow regions from corresponding Doppler OCT vessel phase images. Compared with the traditional random walk segmentation algorithm and cascade neural network method, the proposed model has better performance. This method is easier to realize system integration and has great potential for clinical evaluation. It is expected to be applied to the evaluation of microscopic and ultra-microscopic vascular status in microvascular anastomosis.

The emergence of drug resistant pathogenic bacteria is increasingly challenging conventional antibiotics. Plant derived flavonoids are considered as potential alternatives to antibiotics due to their antimicrobial properties. However, the mechanisms by which flavonoids modulate pathogenic microorganisms’ growth are not fully understood. In our previous studies we found that Rutin, a kind of flavonoids showed inhibition against Klebsiella pneumoniae strains. In order to better understand its inhibitory mechanism on strain growth, we used both Rutin and Luteolin as treatments to incubate K. pneumoniae ATCC700603 strain. After incubation for 4 hours, the homogenous and differential effects of two different flavonoids on the growth of K. pneumoniae were evaluated. The integrated metabolomic and transcriptomic analysis was performed. The expression levels of 5,483 genes and the contents of 882 metabolites were measured. The differentially expressed genes (DEGs) and metabolites (DEMs) were screened and analyzed. The correlation between DEGs and DEMs were also studied. Our results showed that Rutin could inhibit the strain growth by changing metabolic pathways and ABC transporters pathways. Our study also revealed FU841_RS17580 and FU841_RS19145 as functional genes that played vital roles in the strain growth.