Evaluation of Radiation Exposure and Influential Factors in Cone-Beam Computed Tomography (CBCT) of the Head and Abdomen during Interventional Procedures.

Cone-beam computed tomography (CBCT) is a widely used imaging technique in interventional radiology. Although CBCT offers great advantages in terms of improving comprehension of complex angioarchitectures and guiding therapeutic decisions, its additional degree of radiation exposure has also aroused considerable concern. In this study, we aimed to assess radiation exposure and its influential factors in patients undergoing CBCT scans of the head and abdomen during interventional procedures. A total of 752 patients were included in this retrospective study. Dose area product (DAP) and reference air kerma (RAK) were used as measures of patient dose. The results showed that the median values of DAP were 53.8 (50.5-64.4) Gy⋅cm2 for head CBCT and 47.4 (39.6-54.3) Gy⋅cm2 for that of the abdomen. Male gender and body mass index (BMI) were characterized by increased DAP and RAK values in both head and abdominal CBCT scans. Larger FOV size was associated with a higher DAP but a lower RAK value, especially in head CBCT scans. Exposure parameters under automatic exposure control (AEC) also varied according to patient BMI and gender. In conclusion, the patients received slightly higher radiation doses from head CBCT scans than from those applied to the abdomen. BMI, gender, and FOV size were the key factors that influenced the radiation dose administered to the patients during CBCT scans. Our results may help to define and minimize patients' exposure to radiation.

Chimeric antigen receptor T cells targeting FcRH5 provide robust tumour-specific responses in murine xenograft models of multiple myeloma.

BCMA-targeting chimeric antigen receptor (CAR) T cell therapy demonstrates impressive clinical response in multiple myeloma (MM). However, some patients with BCMA-deficient tumours cannot benefit from this therapy, and others can experience BCMA antigen loss leading to relapse, thus necessitating the identification of additional CAR-T targets. Here, we show that FcRH5 is expressed on multiple myeloma cells and can be targeted with CAR-T cells. FcRH5 CAR-T cells elicited antigen-specific activation, cytokine secretion and cytotoxicity against MM cells. Moreover, FcRH5 CAR-T cells exhibited robust tumoricidal efficacy in murine xenograft models, including one deficient in BCMA expression. We also show that different forms of soluble FcRH5 can interfere with the efficacy of FcRH5 CAR-T cells. Lastly, FcRH5/BCMA-bispecific CAR-T cells efficiently recognized MM cells expressing FcRH5 and/or BCMA and displayed improved efficacy, compared with mono-specific CAR-T cells in vivo. These findings suggest that targeting FcRH5 with CAR-T cells may represent a promising therapeutic avenue for MM.

FEDResNet: a flexible image encryption and decryption scheme based on end-to-end image diffusion with dilated ResNet.

Image encryption has emerged as a method of disguising an image with a noisy or meaningless appearance to prevent its content from being accessed by unauthorized users. We propose an architecture named flexible image encryption and decryption ResNet (FEDResNet) for diffusing an image in end-to-end mode. The architecture consists of an encryption network for diffusing the image and a decryption network for restoring the plaintext image from the diffused image. To enhance the security of the encrypted image, the diffused image is further processed with two optional operations: parallel scrambling and serial diffusion. Two key planes are constructed based on a user-defined key with a chaotic map to control the authority to access images. The structure and parameters of FEDResNet can be shared publicly by different users; hence, it is more flexible and convenient than previous deep-learning-based image encryption methods. A classification network is trained to classify medical images in ciphertext environments. The proposed FEDResNet is trained and tested on the ImageNet data set. Extensive experiments have been performed, and the experimental results suggest that the proposed model can achieve a high level of security with satisfactory efficiency. The experimental results also show that FEDResNet-encrypted images can be classified directly in the ciphertext domain by authorized users as accurately as plaintext images, which is a superior property that is not possessed by traditional image encryption methods.

Retrospective cohort study comparing the epidemiological and clinical characteristics between imported and local COVID-19 inpatients in Nanyang, China.

China has experienced an outbreak of COVID-19 since December 2019. This study investigated the differences between the imported and local cases of COVID-19 in Nanyang, China. In this study, a total of 129 COVID-19 confirmed cases with a clear epidemiological history admitted to hospitals in Nanyang from January 24 to February 26, 2020 were enrolled. Patients who had a travel history to or a residence history in Wuhan or in the surrounding areas in Hubei Province within 14 days before the illness onset were assigned to the imported group (n=70), and the others were assigned to the local group (n=59). The differences in epidemiological characteristics, clinical features, laboratory and imaging results, and prognosis were compared between the 2 groups. The early diagnosed cases were mainly imported cases, and the later diagnosed ones were mainly local cases. The most common first symptom was fever; moderate fever was commonly seen in imported cases whereas low fever was commonly seen in local cases. Lymphocyte counts in the imported group were lower than those in the local group. The imported group showed more advanced and severe abnormalities in the CT scan whereas the local group showed milder pulmonary abnormalities. The proportion of severe and critically severe patients in the imported group was higher than that in the local group. In conclusion, the imported cases have more severe or critically severe patients with a higher mortality rate than the local cases.

Serum level of co-expressed hub miRNAs as diagnostic and prognostic biomarkers for pancreatic ductal adenocarcinoma.

Background: Sensitive and specific non-invasive biomarkers are urgently needed in order to improve the survival of patients with pancreatic ductal adenocarcinoma (PDAC), which is the fourth leading cause of cancer-related death. We aim to identify serum hub miRNAs as potential diagnostic and prognostic biomarkers for PDAC. Methods: A total of 2578 serum miRNA expression data from 88 PDAC patients and 19 healthy subjects were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was constructed and significant modules were extracted from the network by WGCNA R package. Network modules and hub miRNAs closely related to PDAC were identified. The prognostic value of hub miRNAs was assessed by Kaplan-Meier overall survival analysis. Results: Two modules strongly associated with PDAC were identified by WGCNA, which were labeled as turquoise and brown respectively. Within each module, twenty hub miRNAs were found. At the functional level, turquoise module was mainly associated with tumorigenesis pathways such as P53 and WNT signaling pathway, while the brown module was mostly related to the pathways of cancer such as RNA transport and MAPK signaling pathway. Utilizing overall survival analyses, five "real" miRNAs were able to stratify PDAC patients into low-risk and high-risk groups. Conclusions: The association of specific Hub miRNAs with the development of pancreatic cancer was established by WGCNA analysis. Five miRNAs (mir-16-2-3p, mir-890, mir-3201, mir-602, and mir-877) were identified as potential diagnostic and prognostic biomarkers for PDAC.

The current and future potential geographic range of West Indian fruit fly, Anastrepha obliqua (Diptera: Tephritidae).

The West Indian fruit fly, Anastrepha obliqua (Macquart), is one of the most important pests throughout the Americas. CLIMEX 3.0 and ArcGIS 9.3 were used to model the current and future potential geographical distribution of this pest. Under current climatic conditions, A. obliqua is predicted to be able to establish throughout much of the tropics and subtropics, including not only North and South America, where it has been reported, but also southern Asia, northeastern Australia and Sub-Saharan Africa. The main factors limiting the pest's range expansion may be cold stress. Climate change expands the potential distribution of A. obliqua poleward as cold stress boundaries recede, but the predicted distribution in northwestern Australia and northern parts of Sub-Saharan Africa will decrease because of heat stress. Considering the widely suitable range for A. obliqua globally and in China, enhanced quarantine and monitoring measures should be implemented in areas that are projected to be suitable for the establishment of the pest under current and future climatic conditions.

[The preliminary research on information redundance in fluorescence spectral data process].

This paper presented the problem of information redundance factor in spectral data analysis. Three-component mixtures fluorescence spectra data of anthracene, pyrene and phenanthrene, which were typical polycyclic aromatic hydrocarbons (PAHs), were investigated through principal component regression analysis and move windows wavelength selection in chemometrics. Theoretical analysis and experimental result demonstrated that there was information redundancy in fluorescence signal; the data which have been optimized by redundancy techniques could show more realistic information of the test samples quantitatively, and for the multi-component mixed system with spectral overlapped seriously, analysis with the data which have been optimized through redundancy techniques could improve the sensitivity and stability of the models.

[The quantitative analysis of polycomponent PAHs by netural network based on data synthese and principal].

The present paper used synthesized data from the experiment samples to replace partial basic experiments, and increased the training samples amount from 14 to 27. In principal component analysis (PCA), the dimensionality of multivariate data was reduced to n principal components and almost all data information was kept. The PCA reduced the network's input nodes from 60 to 3 to simplify the neural network's structure. Finally, back-propagation neural network was used to train and predict these samples. It had 27 training samples, the input layer had three nodes, the hidden layer had two nodes, and the output layer had two nodes. Its excitation function is variable learning rate method. The results show that the coefficient of recovery can reach 89.6-109.0. It has reached the expected purpose.