The morphological and molecular identifications of Cyclospora-like oocysts of dogs were underwent in the present study, in which the morphological characteristics of the Cyclospora-like oocysts firstly found in the stool samples of dogs, such as shape, size, acid-fast staining,sporulation and auto-fluorescene, were observed. According to the published sequence of the rDNA gene of Cyclospora in GenBank, 3 primers were designed and were used to amplify part of the 18S rDNA gene of dog-associated Cyclospora-like organism by nested PCR.The amplicons were purified and cloned into vector pMD19T. Then, the positive clones screened were sequenced and subjected to sequence homology and phylogenetic analysis. It was found that the morphological charactertistics of the Cyclospora-like oocysts in dogs were similar to that of the human Cyclospoa oocysts and the size of-the amplified fragment of 18S rDNA was proved to be 715 bp, that was identical to that of the target fragment. Based on the results of sequence homology and phylogenetic analysis, the dog-associated Cyclospora-like organism was identified as the Cyclospora species.

To establish a highly sensitive and specific method to detect the presence of Cryptosporidium homini, the RT-PCR-ELISA assay was tried, in which the primer with a biotin-labeled probe was designed to amplify fragment containing the highly variable region by multiple alignment between p23 gene of C.hominis and other Cryptosporidium spp. The RT-PCR was used to amplify the target fragment, and the amplified product was used to hybridize with the probe primer. The hybridized product was then captured on micro-plate wells coated with streptavidin and reacted with anti-digoxin antibody labeled with horse-radish peroxidase. This method of testing was then used for the detection of C.hominis in 22 clinical specimens and compared with the conventional methods of testing. It was demonstrated that the RT-PCR--ELISA for the detection of C.hominis was proved to be quite sensitive and specific. Its sensitivity was 100 times higher than that of the general PCR. From the result of clinic detection, the detection rate of RT-PCR-ELISA assay attained to 86%(19/22), while those of RT-PCR, sucrose floating method and anti-acid staining were 27%, 27% and 50% respectively. This result indicates that the RT-PCR-ELISA assay is more sensitive to detect C.hominis than the other three methods of testing.

Purpose To explore the application value of machine learning models based on cortical morphological features in the diagnosis of early Parkinson's disease(PD).Materials and Methods MRI and clinical data of 170 subjects from January 2014 to December 2017,including 100 early PD patients and 70 healthy controls,were selected from the Parkinson's Progression Markers Initiative database.Firstly,computational anatomy toolbox was used to preprocess the images to extract the fractal dimension(FD)and gyrification index(GI)of the cerebral cortex,and the differences of two indexes between early PD and healthy controls were compared.Then,all subjects were randomly divided into the train set and the test set with a 7∶3 ratio,and the optimal features were selected by t-test and recursive feature elimination.The classification model was constructed by random forest and evaluated by the receiver operating characteristic curve,and the decision curve analysis was used to evaluate the clinical value of the model.Results Compared to healthy controls,early PD patients had reduced GI in the bilaterally precentral gyrus,bilaterally rostral middle frontal cortex,bilaterally caudal middle frontal cortex,bilaterally triangular part of inferior frontal gyrus,bilaterally opercular part of inferior frontal gyrus,bilaterally orbital part of inferior frontal gyrus,the right superior frontal gyrus,the right lateral orbitofrontal cortex and the right insula(all P<0.05),but there was no significant difference in the FD(all P>0.05).The results of model evaluation showed that the area under curve values of the FD,the GI and the combined model in the train set were 0.860,0.895 and 0.939,respectively,and those in the test set were 0.762,0.821 and 0.868,respectively.The Hosmer-Lemeshow test showed that there was no statistically significant difference in the goodness of fit between the train and test set(all P>0.05).The decision curve analysis curve showed that clinical net benefit of the combined model was optimal when the probability threshold was in the range of 0.10 to 0.88.Conclusion In the early stages of the disease,cortical morphology of PD patients have changed.Machine learning model based on cortical morphology features has good diagnostic performance,and may be of important value in assisting clinical early diagnosis of PD.