RESUMO
In this study, we isolated a lytic Pseudomonas aeruginosa phage (vB_PaeP_ASP23) from the sewage of a mink farm, characterized its complete genome and analyzed the function of its putative lysin and holin. Morphological characterization and genome annotation showed that phage ASP23 belonged to the Krylovirinae family genus Phikmvvirus, and it had a latent period of 10 min and a burst size of 140 pfu/infected cell. In minks challenged with P. aeruginosa, phage ASP23 significantly reduced bacterial counts in the liver, lung, and blood. The whole-genome sequencing showed that its genome was a 42,735-bp linear and double-stranded DNA (dsDNA), with a G + C content of 62.15%. Its genome contained 54 predicted open reading frames (ORFs), 25 of which had known functions. The lysin of phage ASP23 (LysASP), in combination with EDTA, showed high lytic activity against P. aeruginosa L64. The holin of phage ASP23 was synthesized by M13 phage display technology, to produce recombinant phages (HolASP). Though HolASP exhibited a narrow lytic spectrum, it was effective against Staphylococcus aureus and Bacillus subtilis. However, these two bacteria were insensitive to LysASP. The findings highlight the potential of phage ASP23 to be used in the development of new antibacterial agents.
RESUMO
Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and the evaluation results are greatly influenced by doctors. In this study, a desktop upper limb rehabilitation robot was designed and a quantitative evaluation system of upper limb motor function for stroke patients was proposed. The kinematics and dynamics data of stroke patients during active training were collected by sensors. Combined with the scores of patients' upper limb motor function by rehabilitation doctors using the Wolf Motor Function Test (WMFT) scale, three different quantitative evaluation models of upper limb motor function based on Back Propagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were established. To verify the effectiveness of the quantitative evaluation system, 10 healthy subjects and 21 stroke patients were recruited for experiments. The experimental results show that the BPNN model has the best evaluation performance among the three quantitative evaluation models. The scoring accuracy of the BPNN model reached up to 87.1%. Moreover, there was a significant correlation between the models' scores and the doctors' scores. The proposed system can help doctors to quantitatively evaluate the upper limb motor function of stroke patients and accurately master the rehabilitation progress of patients.
