Identification of novel lncRNA prognostic biomarkers and their associated ceRNAs in bladder urothelial carcinoma.

Bladder urothelial carcinoma (BUCA) is a common malignant tumor with a high rate of metastasis and recurrence. The lack of specific and sensitive biomarkers for the prognostic assessment makes it important to seek alternatives. Recent studies have demonstrated that long noncoding RNAs (lncRNAs) function as competitive endogenous RNAs (ceRNAs) and play an important role in BUCA prognosis. Therefore, this study aimed to establish a prognosis-related lncRNAs-microRNAs (miRNAs)-messenger RNA (mRNA) (pceRNA) network and identify novel prognostic biomarkers. Integrated weighted coexpression analysis, functional clustering, and ceRNA network were used for the prognostic assessment of BUCA. The transcriptome sequencing datasets of lncRNA, miRNA, and mRNA from The Cancer Genome Atlas database were used for the identification of key lncRNAs and construction of the lncRNAs expression signature for prognostic prediction of BUCA patients. Then, 14 differentially expressed lncRNAs (DE-lncRNAs) were identified as candidate prognostic RNAs based on the ceRNAs network and functional clustering. In the Cox regression analysis, two (AC008676.1 and ADAMTS9-AS1) of all DE-lncRNAs were significantly associated with overall survival (OS) of BUCA patients. This two DE-lncRNA signature was significantly correlated with OS and was an independent prognostic factor, which was confirmed in an independent dataset of GSE216037. Moreover, we constructed the pceRNA network that includes 2 DE-lncRNAs, 9 DE-miRNAs, and 10 DE-mRNAs. Pathway enrichment analysis showed that AC008676.1 and ADAMTS9-AS1 are involved in several cancer-related pathways such as proteoglycans in cancer and TGF-beta signaling pathway. The novel-identified DE-lncRNA prognostic signature and the pceRNA network in this study will be valuable risk predictors and diagnostic markers for BUCA.

Engineering magnetic nano-manipulators for boosting cancer immunotherapy.

Cancer immunotherapy has shown promising therapeutic results in the clinic, albeit only in a limited number of cancer types, and its efficacy remains less than satisfactory. Nanoparticle-based approaches have been shown to increase the response to immunotherapies to address this limitation. In particular, magnetic nanoparticles (MNPs) as a powerful manipulator are an appealing option for comprehensively regulating the immune system in vivo due to their unique magnetically responsive properties and high biocompatibility. This review focuses on assessing the potential applications of MNPs in enhancing tumor accumulation of immunotherapeutic agents and immunogenicity, improving immune cell infiltration, and creating an immunotherapy-sensitive environment. We summarize recent progress in the application of MNP-based manipulators to augment the efficacy of immunotherapy, by MNPs and their multiple magnetically responsive effects under different types of external magnetic field. Furthermore, we highlight the mechanisms underlying the promotion of antitumor immunity, including magnetically actuated delivery and controlled release of immunotherapeutic agents, tracking and visualization of immune response in real time, and magnetic regulation of innate/adaptive immune cells. Finally, we consider perspectives and challenges in MNP-based immunotherapy.

A lightweight convolutional neural network model with receptive field block for C-shaped root canal detection in mandibular second molars.

Rapid and accurate detection of a C-shaped root canal on mandibular second molars can assist dentists in diagnosis and treatment. Oral panoramic radiography is one of the most effective methods of determining the root canal of teeth. There are already some traditional methods based on deep learning to learn the characteristics of C-shaped root canal tooth images. However, previous studies have shown that the accuracy of detecting the C-shaped root canal still needs to be improved. And it is not suitable for implementing these network structures with limited hardware resources. In this paper, a new lightweight convolutional neural network is designed, which combined with receptive field block (RFB) for optimizing feature extraction. In order to optimize the hardware resource requirements of the model, a lightweight, multi-branch, convolutional neural network model was developed in this study. To improve the feature extraction ability of the model for C-shaped root canal tooth images, RFB has been merged with this model. RFB has achieved excellent results in target detection and classification. In the multiscale receptive field block, some small convolution kernels are used to replace the large convolution kernels, which allows the model to extract detailed features and reduce the computational complexity. Finally, the accuracy and area under receiver operating characteristics curve (AUC) values of C-shaped root canals on the image data of our mandibular second molars were 0.9838 and 0.996, respectively. The results show that the deep learning model proposed in this paper is more accurate and has lower computational complexity than many other similar studies. In addition, score-weighted class activation maps (Score-CAM) were generated to localize the internal structure that contributed to the predictions.