In June 2016,a disease among the cultured rock carp (Procypris rabaudi) in Yongchuan of Chongqing Municipality occurred.The aim of this study was to investigate biological characteristics and provide reference for Aeromonas veronii identification diagnosis and treatment.Pathogenic bacteria strain YY01 from the dying fishes were examined and isolated.Strain YY01's taxonomic status was identified by observing the morphology,studying the physiological and biochemical characters and sequencing the 16S rRNA and housekeeping gene gyrB.Its pathogenicity was checked by artificial infection experiment and virulence genes.Furthermore,effective medicine was detected by drug sensitivity.The 16S rRNA and gyrB gene sequence of the strain YY01 was more than 99％ homology with that of Aeromonas veronii,suggesting that the pathogen was Aeromonas veronii,which was also identified by the results of biochemical analysis.The LD50 of strain YY01 to rcok carp was 5.06 × 104 CFU/g.Four virulence genes were detected from YY01,including aerolysin (aer),hemolysin (Hly),Outer Membrane Protein Gene A (OmpA) and adhesion (Aha) genes.Antibiotic sensitivity assays showed that among 40 antibiotics tested,22 were sensitive and 11 were resistant.In conclusion,the strain YY01 is identified as Aeromonas veronii and it is proved to have strong pathogenicity.

OBJECTIVEThe super-high-molecular-weight poly-DL-lactic acid (PDLLA), with the molecular weight of 900 kD, is a newly emerging biomaterial and potentially used in the therapy of bone fracture because of its excellent mechanical property. However the biocompatibility of this material has not been reported so far, therefore this experiment was designed to examine whether the super-high-molecular-weight PDLLA was harmful to creatures, when it was implanted in the body of animals for a long period.

METHODSThe material was prepared in small cuboids, with the size of 1.0 mm x 1.5 mm x 2.0 mm, and these blocks were implanted into the masseteric space of SD rats and, the activity of the SD-rats was monitored continuously. The animals were sacrificed in the 3rd, 6th, 9th, 12th months after the operation and, the specimens were taken out from the animals. The examination included anatomical, pathological and haematological methods. The data were analyzed with SPSS 8.0.

RESULTSThe wound healed well after the operation. Super-high-molecular-weight PDLLA degraded 6 months after the implantation. In the 3rd month after the operation, a thin fiber membrane around the materials was formed. In the 6th month, the membrane was much thinner than that in the 3rd month and completely disappeared in the 9th month. The pathological examination showed that slightly inflammatory reaction appeared in the tissue around these blocks in the 3rd month, but the inflammatory reactions were gradually remitted in the following 6th, 9th and 12th months. Further, the haematological examination did not show any abnormity during the 12-month observation period.

CONCLUSIONThe super-high-molecular-weight PDLLA can be degrade when it is implanted into the body of creatures, which proves its good biocompatibility.

Animals ; Biocompatible Materials ; chemistry ; metabolism ; Biodegradation, Environmental ; Bone Substitutes ; Implants, Experimental ; Lactic Acid ; chemistry ; metabolism ; Male ; Mandible ; metabolism ; surgery ; Molecular Weight ; Polyesters ; Polymers ; chemistry ; metabolism ; Rats ; Rats, Sprague-Dawley

Objective To construct YOLOv5x deep learning network model based on SPECT whole body bone scanning,and to observe its value for diagnosing benign and malignant bone lesions.Methods Totally 699 patient who underwent SPECT bone scanning were enrolled,with a total of 5 182 bone lesions,including 3 105 malignant and 2 077 benign lesions.Then 1 121 bone images were divided into training set(n=897),validation set(n=112)or test set(n=112)at the ratio of 8∶1∶1.After augmentation on training set and validation set,the data were inputted to YOLOv5x deep learning network for training to obtain a recognition model.The sensitivity,specificity and accuracy of this model for diagnosing benign and malignant bone lesions were analyzed,and the consistency between its diagnosis results and gold standards was evaluated based on test set.Results The sensitivity,specificity and accuracy of bone scanning YOLOv5x deep learning network model for identifying malignant bone lesions was 95.75％,87.87％and 91.60％,respectively,and for identifying benign bone lesions was 91.62％,94.38％and 93.14％,respectively.The area under the curve(AUC)of this model for identifying bone lesions on bone scanning images was 0.98,for malignant and benign bone lesions was 0.97 and 0.98,respectively.The consistency between the diagnosis results of this model for malignant and benign bone lesions and the gold standards were both good(Kappa=0.83,0.86,both P＜0.05).Conclusion YOLOv5x deep learning network model based on SPECT whole body bone scanning was helpful for diagnosing benign and malignant bone lesions.