Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

The field of medical images has been rapidly evolving since the advent of the digital medical information era.However,medical data is susceptible to leaks and hacks during transmission.This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage,as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms.First,a pre-trained GoogLeNet network is used in this paper,based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset,so that the pre-trained network can further learn the deep convolutional features in the medical dataset,and then the trained network is used to extract the stable feature vectors of medical images.Then,a two-dimensional Henon chaos encryption technique,which is more sensitive to initial values,is used to encrypt multiple different types of watermarked private information.Finally,the feature vector of the image is logically operated with the encrypted multiple watermark information,and the obtained key is stored in a third party,thus achieving zero watermark embedding and blind extraction.The experimental results confirmthe robustness of the algorithm from the perspective ofmultiple types of watermarks,while also demonstrating the successful embedding ofmultiple watermarks for medical images,and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

Research on formation density MWD instrument in engineering geological exploration

A high efficiency method is very important in geological survey for a new city in China.Geophysical parameters are Measured While Drilling(MWD),and these parameters are processed and explained on the ground,so the method can replace conventional engineering geological exploration for drilling rock sample.According petroleum engineering MWD,using the different characters of different rock absorbs γ radial,with the method of storing data in hole and explaining data on the ground,engineering geological exploration formation density MWD is researched.The MWD works stabilized,and the performance is good with precise data.

Artificial intelligence-based non-invasive tumorsegmentation, grade stratification and prognosisprediction for clear-cell renal-cell carcinoma

Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma(ccRcC),non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment.A total of 126345 computerized tomography(cT)images from four independent patient cohorts were included for analysis in this study.We propose a V Bottieneck multi-resolution and focus-organ network(VB-MrFo-Net)using a cascade framework for deep learning analysis.The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation,with a Dice score of 0.87.The nuclear-grade prediction model performed best in the logistic regression classifier,with area under curve values from 0.782 to 0.746.Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk,with a hazard ratio(HR)of 2.49[95%confidence interval(CI):1.13-5.45,P=0.023]in the General cohort.Excellent performance had also been verified in the Cancer Genome Atlas cohort,the Clinical Proteomic Tumor Analysis Consortium cohort,and the Kidney Tumor Segmentation Challenge cohort,with HRs of 2.77(95%CI:1.58-4.84,P=0.0019),3.83(95%CI:1.22-11.96,P=0.029),and 2.80(95%CI:1.05-7.47,P=0.025),respectively.In conclusion,we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRcc.The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments,which couid provide practical advicefordecidingtreatmentoptions.

Management and operation of extra-large Fangcang hospitals:experience and lessons from containing the highly contagious SARS-CoV-2 Omicron in Shanghai,China

Introduction Omicron is more contagious and stealthier than the previous strains.The basic reproduction number of Omicron is around 8–12,whereas that of the previous mainstream strain Delta is only 5–8[1].Omicron’s symptoms are relatively mild[2]compared with Delta’s symptoms;however,Omicron’s transmission ability is very strong,and its risk to children and the elderly remains high[3].In addition,the vaccine’s preventive effect on Omicron has weakened.Therefore,Omicron can easily cause a rapid outbreak in a city.The population density of megacities and the limited public health resources further exacerbate the difficulty of Omicron prevention and control.

Patient-derived organoids potentiate precision medicine in advanced clear cell renal cell carcinoma

To investigate the role of patient-derived organoid(PDO)model in the precision medicine of advanced clear cell renal cell carcinoma(ccRCC),we retrospectively analyzed the clinical data of seven cases of ccRCC diagnosed by operation and pathology in Renji Hospital from September 2021 to September 2022.The seven patients were diagnosed with advanced ccRCC with or without remote metastasis.Cytoreductive and radical nephrectomy was performed respectively.To predict the response to immunotherapy and provide personalized medicine recommendation,a PDO model based on air-liquid interface system was established from the surgical resected tumor and subsequent drug screening was performed.Hematoxylin and eosin(H&E)staining and immunohistochemistry revealed that the PDO recapitulated the histological feature of parent tumor.Immunofluorescence staining identified that CD3^(+)T cells,SMA^(+)cancer associated fibroblasts,and CD31^(+)endothelial cells were preserved in PDO models.Fluorescence activated cell sorter(FACS)revealed an evidently increased ratio of CD8^(+)/CD4^(+)T cells and apoptotic tumor cells in PDO treated with toripalimab than those treated with IgG4.The results showed that toripalimab is able to rescue the excessive death of CD8^(+)T cells by critically reversing the immune exhaustion state of ccRCC in PDO model.This research validated that PDO is a promising and faithful preclinical model for prediction of immunotherapy response in patients with ccRCC.