The human antimicrobial peptide LL-37 has chemotactic and modulatory activities in various immune cells, including dendritic cells. Because of its characteristics, LL-37 can be considered an adjuvant for vaccine development. In this study, we confirmed the possible adjuvant activity of LL-37 in mucosal vaccine development against Middle East respiratory syndrome-coronavirus (MERS-CoV) by means of intranasal immunization in C57BL/6 and human dipeptidyl peptidase 4 (hDPP4)-transgenic (hDPP4-Tg) mice. Intranasal immunization using the receptor-binding domain (RBD) of MERS-CoV spike protein (S-RBD) recombined with LL-37 (S-RBD-LL-37) induced an efficient mucosal IgA and systemic IgG response with virus-neutralizing activity, compared with S-RBD. Ag-specific CTL stimulation was also efficiently induced in the lungs of mice that had been intranasally immunized with S-RBDLL-37, compared with S-RBD. Importantly, intranasal immunization of hDPP4-Tg mice with S-RBD-LL-37 led to reduced immune cell infiltration into the lungs after infection with MERSCoV. Finally, intranasal immunization of hDPP4-Tg mice with S-RBD-LL-37 led to enhanced protective efficacy, with increased survival and reduced body weight loss after challenge infection with MERS-CoV. Collectively, these results suggest that S-RBD-LL-37 is an effective intranasal vaccine candidate molecule against MERS-CoV infection.

Detecting sphenoid sinus fluid (SSF) is an additional finding in autopsies for diagnosing drowning. SSF can provide additional forensic evidence through laboratory tests such as diatom and electrolyte analyses. If drowning is suspected, accurately assessing the presence and volume of SSF during an autopsy is crucial. Utilizing postmortem computed tomography (PMCT) images could aid in accurately sampling SSF. Accurately segmenting the region of interest is essential for volume analysis using computed tomography images. However, manual segmentation techniques are labor-intensive and time-consuming, and their success depends on the experience of the observer. Therefore, this study aimed to develop a U-Net–based deep learning model for the automatic segmentation of SSF in drowning cases using PMCT images and to evaluate the performance of the model. We retrospectively reviewed 34 drowning cases in which both PMCT scans and forensic autopsies were performed at our institution. The U-Net architecture of deep learning was used for automatic segmentation. The proposed model achieved the Dice similarity coefficient (DSC) and Intersection over Union (IoU) of a maximum of 95.85% and 92.03%, a minimum of 0% and 0%, and an average of 77.15% and 67.18%, respectively. Although the average DSC and IoU did not show high similarity, this study showed that PMCT images can be used for automatic segmentation of SSF in drowning cases, which could improve the performance with sufficient dataset acquisition and further model training.