Copper oxide nanoparticles (CuO NPs) are used for a variety of purposes in a wide range of commercially available products. Some CuO NPs probably end up in the aquatic systems, thus raising concerns about aqueous exposure toxicity, and the impact of CuO NPs on liver development and neuronal differentiation remains unclear. In this study, particles were characterized using Fourier transform infrared spectra, scanning electron microscopy, and transmission electron microscopy. Zebrafish embryos were continuously exposed to CuO NPs from 4 hours postfertilization at concentrations of 50, 25, 12.5, 6.25, or 1 mg/L. The expression of gstp1 and cyp1a was examined by quantitative reverse transcription polymerase chain reaction. The expression of tumor necrosis factor alpha and superoxide dismutase 1 was examined by quantitative reverse transcription polymerase chain reaction and Western blotting. Liver development and retinal neurodifferentiation were analyzed by whole-mount in situ hybridization, hematoxylin–eosin staining, and immunohistochemistry, and a behavioral test was performed to track the movement of larvae. We show that exposure of CuO NPs at low doses has little effect on embryonic development. However, exposure to CuO NPs at concentrations of 12.5 mg/L or higher leads to abnormal phenotypes and induces an inflammatory response in a dose-dependent pattern. Moreover, exposure to CuO NPs at high doses results in an underdeveloped liver and a delay in retinal neurodifferentiation accompanied by reduced locomotor ability. Our data demonstrate that short-term exposure to CuO NPs at high doses shows hepatotoxicity and neurotoxicity in zebrafish embryos and larvae.
Background The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma (PDAC) patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning (DL) model based on 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) for a fully automatic prediction of preoperative pathological grading of pancreatic cancer. Methods A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent 18F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set, and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by sevenfold cross-validation. Results The Dice score of the developed PET/CT-based tumor segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively. Conclusion To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision-making.
Background :The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma(PDAC)patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning(DL)model based on 18F-FDG-PET/CT for a fully automatic prediction of preoperative pathological grading of pancreatic cancer. Results :A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent 18F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by 7-fold cross-validation.The Dice score of the developed PET/CT based tumour segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively. Conclusion :To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision making.
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