Perfluorooctyl bromide nanoemulsions holding MnO2 nanoparticles with dual-modality imaging and glutathione depletion enhanced HIFU-eliciting tumor immunogenic cell death

Tumor-targeted immunotherapy is a remarkable breakthrough, offering the inimitable advantage of specific tumoricidal effects with reduced immune-associated cytotoxicity. However, existing platforms suffer from low efficacy, inability to induce strong immunogenic cell death (ICD), and restrained capacity of transforming immune-deserted tumors into immune-cultivated ones. Here, an innovative platform, perfluorooctyl bromide (PFOB) nanoemulsions holding MnO2 nanoparticles (MBP), was developed to orchestrate cancer immunotherapy, serving as a theranostic nanoagent for MRI/CT dual-modality imaging and advanced ICD. By simultaneously depleting the GSH and eliciting the ICD effect via high-intensity focused ultrasound (HIFU) therapy, the MBP nanomedicine can regulate the tumor immune microenvironment by inducing maturation of dendritic cells (DCs) and facilitating the activation of CD8+ and CD4+ T cells. The synergistic GSH depletion and HIFU ablation also amplify the inhibition of tumor growth and lung metastasis. Together, these findings inaugurate a new strategy of tumor-targeted immunotherapy, realizing a novel therapeutics paradigm with great clinical significance.

Segmentation of ground glass pulmonary nodules using full convolution residual network based on atrous spatial pyramid pooling structure and attention mechanism / 生物医学工程学杂志

Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.

Study on the effect and mechanism of SKA1 activated by E2F1 transcription in promoting the proliferation of liver cancer cells / 中华内分泌外科杂志

Objective:To investigate whether SKA1 is a key molecule regulating malignant proliferation of liver cancer, and further explore its mechanism to provide molecular theoretical basis for subsequent targeted therapy.Methods:The data of liver cancer from TCGA database were analyzed by bioinformatics technology. The expression of SKA1 in liver cancer was analyzed. At the same time, we also analyzed the relationship between the expression of SKA1 and the prognosis of patients with liver cancer. The hepatoma cell line overexpressing SKA1 was constructed by liposome-mediated cell transfection technique, and the effect of SKA1 on the proliferation of hepatoma cells was further tested by CCK-8 and plate cloning assay. At the same time, we found that E2F1 is also highly expressed in liver cancer, using bioinformatics technology to analyze the correlation between SKA1 and E2F1 expression, further detecting the binding site of E2F1 in the SKA1 promoter region, and using dual luciferase technology to detect E2F1 against SKA1. Transcriptional activation.Results:KA1 was highly expressed in liver cancer tissues, and the overall survival rate of liver cancer patients with high SKA1 expression was 49.8%, lower than that of patients with low SKA1 expression, showing a negative correlation. E2F1 is also highly expressed in liver cancer tissues, and the survival time of patients with liver cancer with high E2F1 expression is significantly lower than that in the low expression group, which was negatively correlated with poor prognosis. SKA1 overexpression could increase the proliferation ability of liver cancer cells by nearly 50%. SKA1 is regulated by the E2F1 transcription factor, and the E2F1 transcription factor is combined with the SKA1 promoter to transcriptionally activate the expression of SKA1 in liver cancer cells.Conclusion:E2F1 transcriptional activation of SKA1 promotes proliferation of hepatoma cells, leading to poor prognosis in patients with liver cancer

The effectiveness of deep learning techniques in differentiating benign and malignant lung nodules on target CT scans / 中华放射学杂志

Objective@#To evaluate the effectiveness of deep learning model trained on routine CT scans when identity the malignant and benign lung nodule on target CT scans dataset.@*Methods@#This retrospective study enrolled 923 patients with lung nodules found by chest CT scan in Shanghai Chest Hospital from January 2016 to December 2018. A total of 969 nodules with pathological report were analyzed. The deep learning based pulmonary malignant prediction method in a fine-grained classification manner was used to make the prediction, and the AUC (the area under the curve), accuracy, sensitivity and specificity of routine CT scans and target CT scans were compared, and Delong test and IDI (Integrated Discrimination Improvement) were employed to provide statistical results. Furthermore, statistical methods were used to investigate the differences between the benign and malignant classification of nodules on routine CT and on target CT.@*Results@#In the benign and malignant discrimination task, AUC, accuracy, sensitivity and specificity on the routine scans were 0.81, 82.0%, 86.0% and 56.6% respectively, while the AUC, accuracy, sensitivity and specificity on the target scans were 0.84, 85.0%, 88.8% and 60.5% respectively. The IDI was 0.056 (Z test, P<0.05), and there was statistically significant difference in ROC (Delong test, P=0.01).@*Conclusions@#The deep learning model trained on the data set of routine CT scans can achieve better diagnostic efficiency in target CT scans data.

CT scan of frontal recess / 临床耳鼻咽喉头颈外科杂志

OBJECTIVE@#To make 3-D reconstruction of frontal recess by high speed spiral CT, which can be helpful to nasal endoscopic frontal sinus operation.@*METHOD@#Fifty-one cases (102 laterals) of frontal recess 3-D reconstruction by 16 line high speed spiral CT were enrolled in the research, which included 58 laterals with chronic frontal sinusitis and 44 laterals of normal nasal sinus. The structure of frontal recess, the agger nasi and the adhere style of uncinate process were recognized. The parameter of frontal recess was measured. Finally the data of two groups were compared and analyzed.@*RESULT@#CT 3-D reconstruction of frontal recess could display the frontal sinus, frontal endosome and frontal recess. The shape of frontal recess varied greatly in different cases, which depended on the near structure especially agger nasi and uncinate process. The difference of average Y axes inner diameter between agger nasi and frontal endosome was significant. The difference of average Y axes inner diameter between frontal endosome and anterior nasal spine, between the line of frontal endosome to anterior nasal spine and the line of Aeby's plane and between bhullar cell and anterior nasal spine were not significant in two groups.@*CONCLUSION@#The drainage flow of frontal recess depends on the near structures especially on the agger nasi and uncinate process. The prevalence of agger nasi is high, and the position of it is constancy, as far agger nasi can be an anatomic landmark of frontal sinus operation. The position of frontal endosome is constancy. The scalloped area from anterior nasal spine 50-60 degrees to the line of Aeby's plane and within 100 mm radius is safety section to nasal endoscopic frontal sinus operation. CT 3-D reconstruction of this area is helpful to avoid insult.

Non-small cell lung cancer: evaluation of the relationship between fibrosis and washout feature at dynamic contrast enhanced CT / 中华放射学杂志

Objective To correlate dynamic parameters at contrast enhanced CT and interstitial fibrosis grade of non-small cell lung cancer (NSCLC). Methods Twenty-nine patients with NSCLC were evaluated by multi-slice CT. Images were obtained before and at 20,30,45,60,75,90,120,180,300,540,720,900 and 1200 s after the injection of contrast media, which was administered at a rate of 4 ml/s for a total of 420 mg I/kg body weight. Washout parameters were calculated. Lung cancer specimens were stained with hematoxylin-eosin stain and collagen and elastica double stain. Spearman test was made to analyze correlation between dynamic parameters and interstitial fibrosis grade of tumor. Results Twentynine NSCLC demonstrated washout at 20 min 12. 1 (0. 32-58.0 ) HU, washout ratio at 20 minutes 15.3% (0. 3%-39.2% ), slope of washout at 20 minutes 0. 0152 %/s ( 0. 0007%/s-0. 0561%/s ).Interstitial fibrosis of 29 lesions was graded as grade Ⅰ (10), grade Ⅱ (14) and grade Ⅲ (5). There were significant correlation between washout at 20 min ( r = - 0. 402, P ＜ 0. 05 ), washout ratio at 20 min ( r =-0.372,P＜0.05), slope of washout ratio (r = -0.459,P ＜0.05) and interstitial fibrosis grade in tumors. Conclusion NSCLC washout features at dynamic multi-detector CT correlates with interstitial fibrosis in the tumor.