Objective To knock out cdc42 gene in zebrafish using CRISPR/Cas9 technology,and investigate the effect of cdc42 on early osteochondral development.Methods After the conservation of cdc42 gene sequence of different species was analyzed by multiple sequence alignment analysis,guide RNA of cdc42 gene was designed,and cdc42 knockout zebrafish was constructed by CRISPR/Cas9 technology.The expression pattern of cdc42 was detected by whole-mount in situ hybridization,and the chondrogenesis phenotype was observed by transgenic labeled fish line Tg(col2a1a:GFP),and vertebral mineralization was observed by alizarin red staining.Results Multiple sequence alignment analysis showed that cdc42 was highly conserved in human,mouse and zebrafish,and in situ hybridization results showed that cdc42 was expressed in a variety of tissues in the head and whole body,including mandibular cartilage.With the aid of guide RNA of cdc42,cdc42 knockout zebrafish was successfully constructed by CRISPR/Cas9 technology.The cdc42 mutants exhibited shortened body length(P＜0.01)and delayed cranial development at 3 d post fertilization,with small heads and eyes(P＜0.01),as well as delayed mandibular development.The Tg(col2a1a:GFP)zebrafish showed that the mutants presented abnormal morphology of Meckel's cartilage and ceratohyal cartilage cells,with disordered arrangement of chondrocytes and increased angle and decreased length in ceratohyal cartilage(P＜0.01).The homozygous mutants died at 10～13 d after fertilization.The results of alizarin red staining suggested delayed vertebral mineralization and reduced endochondral ossification of the mutants.Conclusion CRISPR/Cas9 technology successfully knocks out the cdc42 gene in zebrafish,resulting in delayed development of jaw cartilage,delayed mineralization of the vertebrae,and decreased endochondral ossification.

Objective:To build a predictive model for symptomatic radiation pneumonitis(RP) using the pretreatment CT radiomics features, clinical and dosimetric data of lung cancer patients by using machine learning method.Methods:A retrospective analysis of 103 lung cancer patients who underwent radiotherapy in the Affiliated Hospital of Jiangnan University from November 2018 to April 2020 was performed. Total normal lung tissues were segmented as an interested volume in pretreatment CT images, and then 250 radiomics features were extracted. The correlations of RP and clinical or dosimetric features were firstly investigated with univariate analysis. Then all clinical data, dosimetric data and CT radiomics features were collected and considered as predictors for modeling of RP grade ≥ 2. Features were selected through LASSO machine learning method, and the predictive model was built. Finally, nomogram for risk of RP were obtained according to the selected features.Results:The result of univariate analysis showed that symptomatic RP was significantly correlated with lung dosimetric parameters including mean lung dose (MLD), V20 Gy and V30 Gy( t=2.20, 2.34 and 2.93, P<0.05). Four features, including lung dose volume percentage V30 Gyand three radiomics features, entropy feature of GLCM, mean and median feature of wavelet histogram were selected among all clinical, dosimetric features and radiomics features. AUC of the predicted model obtained from selected features reached 0.757. For convenient clinical use, the nomogram were obtained, and then personalized RP risk prediction and early intervention could be performed according to this nomogram. Conclusions:Pretreatment CT radiomics and dosimetric features can be used in predicting symptomatic RP, which will be useful for advanced intervention treatment.