Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images

Purpose/Objectives(s)Salivary gland tumors are a rare, histologically heterogeneous group of tumors. The distinction between malignant and benign tumors of the parotid gland is clinically important. This study aims to develop and evaluate a deep-learning network for diagnosing parotid gland tumors via the deep learning of MR images.Materials/MethodsTwo hundred thirty-three patients with parotid gland tumors were enrolled in this study. Histology results were available for all tumors. All patients underwent MRI scans, including T1-weighted, CE-T1-weighted and T2-weighted imaging series. The parotid glands and tumors were segmented on all three MR image series by a radiologist with 10 years of clinical experience. A total of 3791 parotid gland region images were cropped from the MR images. A label (pleomorphic adenoma and Warthin tumor, malignant tumor or free of tumor), which was based on histology results, was assigned to each image. To train the deep-learning model, these data were randomly divided into a training dataset (90%, comprising 3035 MR images from 212 patients: 714 pleomorphic adenoma images, 558 Warthin tumor images, 861 malignant tumor images, and 902 images free of tumor) and a validation dataset (10%, comprising 275 images from 21 patients: 57 pleomorphic adenoma images, 36 Warthin tumor images, 93 malignant tumor images, and 89 images free of tumor). A modified ResNet model was developed to classify these images. The input images were resized to 224x224 pixels, including four channels (T1-weighted tumor images only, T2-weighted tumor images only, CE-T1-weighted tumor images only and parotid gland images). Random image flipping and contrast adjustment were used for data enhancement. The model was trained for 1200 epochs with a learning rate of 1e-6, and the Adam optimizer was implemented. It took approximately 2 hours to complete the whole training procedure. The whole program was developed with PyTorch (version 1.2).ResultsThe model accuracy with the training dataset was 92.94% (95% CI [0.91, 0.93]). The micro-AUC was 0.98. The experimental results showed that the accuracy of the final algorithm in the diagnosis and staging of parotid cancer was 82.18% (95% CI [0.77, 0.86]). The micro-AUC was 0.93.ConclusionThe proposed model may be used to assist clinicians in the diagnosis of parotid tumors. However, future larger-scale multicenter studies are required for full validation

Heterologous boost with mRNA vaccines against SARS-CoV-2 Delta/Omicron variants following an inactivated whole-virus vaccine.

The coronavirus SARS-CoV-2 has mutated quickly and caused significant global damage. This study characterizes two mRNA vaccines ZSVG-02 (Delta) and ZSVG-02-O (Omicron BA.1), and associating heterologous prime-boost strategy following the prime of a most widely administrated inactivated whole-virus vaccine (BBIBP-CorV). The ZSVG-02-O induces neutralizing antibodies that effectively cross-react with Omicron subvariants. In naïve animals, ZSVG-02 or ZSVG-02-O induce humoral responses skewed to the vaccines targeting strains, but cellular immune responses cross-react to all variants of concern (VOCs) tested. Following heterologous prime-boost regimes, animals present comparable neutralizing antibody levels and superior protection against Delta and Omicron BA.1variants. Single-boost only generated ancestral and omicron dual-responsive antibodies, probably by recall and reshape the prime immunity. New Omicron-specific antibody populations, however, appeared only following the second boost with ZSVG-02-O. Overall, our results support a heterologous boost with ZSVG-02-O, providing the best protection against current VOCs in inactivated virus vaccine-primed populations

Dual-crosslinked hyaluronan hydrogels with rapid gelation and high injectability for stem cell protection

Injectable dynamic hydrogels play a key role in cell transplantation to protect the cells from shear stress during injection. However, it still remains challenging to design dynamic hydrogels with fast gelation and high stability for protecting cells under flow due to the slow formation and exchange of most dynamic bonds. Here, a novel dual-crosslinked hydrogel system with fast dynamic crosslinks is developed by using methacrylate chitosan (CHMA) and aldehyde functionalized hyaluronate (oxidized HA, OHA). Based on the cooperation of electrostatic interaction between cationic amino of chitosan and anionic carboxyl of HA and Schiff-based crosslinking through amino and aldehyde groups, the dynamic CHMA-OHA hydrogel shows rapid gelation and high injectability. Further, the CHMA-OHA hydrogel is photopolymerized for achieving a high modulus and stability. Importantly, such hydrogels loaded with stem cells remains a cell viability (similar to 92%) after extrusion. These results indicate that the CHMA-OHA hydrogel is a promising tissue engineering biomaterial for therapeutic cell delivery and 3D printing of encapsulated cell scaffolds

mRNA vaccines encoding fusion proteins of monkeypox virus antigens protect mice from vaccinia virus challenge

Abstract The recent outbreaks of mpox have raised concerns over the need for effective vaccines. However, the current approved vaccines have either been associated with safety concerns or are in limited supply. mRNA vaccines, which have shown high efficacy and safety against SARS-CoV-2 infection, are a promising alternative. In this study, three mRNA vaccines are developed that encode monkeypox virus (MPXV) proteins A35R and M1R, including A35R extracellular domain -M1R fusions (VGPox 1 and VGPox 2) and a mixture of encapsulated full-length mRNAs for A35R and M1R (VGPox 3). All three vaccines induce early anti-A35R antibodies in female Balb/c mice, but only VGPox 1 and 2 generate detectable levels of anti-M1R antibodies at day 7 after vaccination. However, all three mRNA vaccine groups completely protect mice from a lethal dose of vaccinia virus (VACV) challenge. A single dose of VGPox 1, 2, and 3 provide protection against the lethal viral challenge within 7 days post-vaccination. Long-term immunity and protection were also observed in all three candidates. Additionally, VGPox 2 provided better passive protection. These results suggest that the VGPox series vaccines enhance immunogenicity and can be a viable alternative to current whole-virus vaccines to defend against mpox