Objectives: This study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours.Methods: Multimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours.Results: In terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886–0.996], and 0.956 [0.873–1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887–1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887–1.000], and 1.000 [0.999–1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867–1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990–1.000] and 1.000 [0.999–1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999–1.000] under the early fusion strategy.Conclusion: This study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.

Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are forms of degenerative retinal disorders that may result in vision impairment or even permanent blindness. Early detection of these conditions is essential to maintaining a patient’s quality of life. The fundus photography technique is non-invasive, safe, and rapid way of assessing the function of the retina. It is widely used as a diagnostic tool for patients who suffer from fundus-related diseases. Using fundus images to analyze these two diseases is a challenging exercise, since there are rarely obvious features in the images during the incipient stages of the disease. In order to deal with these issues, we have proposed a deep learning method called FunSwin. The Swin Transformer constitutes the main framework for this method. Additionally, due to the characteristics of medical images, such as their small number and relatively fixed structure, transfer learning strategy that are able to increase the low-level characteristics of the model as well as data enhancement strategy to balance the data are integrated. Experiments have demonstrated that the proposed method outperforms other state-of-the-art approaches in both binary and multiclass classification tasks on the benchmark dataset.

Introduction: Fusobacterium nucleatum (F. nucleatum) infection has been confirmed to be associated with the development, chemoresistance, and immune evasion of colorectal cancer (CRC). The complex relationship between the microorganism, host cells, and the immune system throughout all stages of CRC progression, which makes the development of new therapeutic methods difficult.Methods: We developed a new dendritic cell (DC) vaccine to investigate the antitumor efficacy of CRC immunotherapy strategies. By mediating a specific mode of interaction between the bacteria, tumor, and host, we found a new plant-derived adjuvant, tubeimuside I (TBI), which simultaneously improved the DC vaccine efficacy and inhibited the F. nucleatum infection. Encapsulating TBI in a nanoemulsion greatly improved the drug efficacy and reduced the drug dosage and administration times.Results: The nanoemulsion encapsulated TBI DC vaccine exhibited an excellent antibacterial and antitumor effect and improved the survival rate of CRC mice by inhibiting tumor development and progression.Discussion: In this study, we provide a effective strategy for developing a DC-based vaccine against CRC and underlies the importance of further understanding the mechanism of CRC processes caused by F. nucleatum.

A copolymer (PVA- g -PEG) of polyethylene glycol (PEG) and polyvinyl alcohol (PVA) was synthesized by grafting PEG chains onto PVA backbone to prepare the silver nanoparticles/polymer composite (AgNPs).