Nile Tilapia: A Model for Studying Teleost Color Patterns.

The diverse color patterns of cichlid fishes play an important role in mate choice and speciation. Here we develop the Nile tilapia (Oreochromis niloticus) as a model system for studying the developmental genetics of cichlid color patterns. We identified 4 types of pigment cells: melanophores, xanthophores, iridophores and erythrophores, and characterized their first appearance in wild-type fish. We mutated 25 genes involved in melanogenesis, pteridine metabolism, and the carotenoid absorption and cleavage pathways. Among the 25 mutated genes, 13 genes had a phenotype in both the F0 and F2 generations. None of F1 heterozygotes had phenotype. By comparing the color pattern of our mutants with that of red tilapia (Oreochromis spp), a natural mutant produced during hybridization of tilapia species, we found that the pigmentation of the body and eye is controlled by different genes. Previously studied genes like mitf, kita/kitlga, pmel, tyrb, hps4, gch2, csf1ra, pax7b, and bco2b were proved to be of great significance for color patterning in tilapia. Our results suggested that tilapia, a fish with 4 types of pigment cells and a vertically barred wild-type color pattern, together with various natural and artificially induced color gene mutants, can serve as an excellent model system for study color patterning in vertebrates.

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs). METHODS: Routine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192). RESULTS: DLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839-0.927), 0.879 (95% CI, 0.813-0.928), and 0.809 (95% CI, 0.746-0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability. CONCLUSIONS: The CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs. KEY POINTS: • The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs. • The performance of the deep learning feature was superior to that of the radiomics feature. • The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone.