Kalavacherla, S. | Search bioRxiv

bioRxiv2026

Neutralizing vaccination against viral interleukin-6 from a primate rhadinovirus

Herpesviruses are ancient viruses that devote a significant fraction of their protein-coding potential to interference with host immunity. Diverse herpesviruses encode proteins with similarity to host MHC molecules, interferon regulatory factors, complement regulators, G-protein coupled receptors, chemokines, and cytokines. Certain gammaherpesviruses, including Kaposi sarcoma-associated herpesvirus (KSHV) and rhesus rhadinovirus (RRV), encode viral cytokines similar to host IL-6, called viral IL-6 (vIL-6). The potential of vIL-6 to act as a protective vaccine antigen against these rhadinoviruses remains under-explored. We isolated a new RRV strain, which encodes virion surface proteins similar to those of previous isolates and a vIL-6 that is nearly identical. We found that, although unmodified vIL-6 is a poor immunogen eliciting low neutralizing titers, vIL-6-Fc fusion protein elicits high-titer responses after either one or two doses. We detected no adverse effects related to cytokine activity, nor interference with concomitantly administered vaccine against the RRV glycoprotein B. Our results demonstrate an approach for eliciting neutralizing antibodies against a highly conserved viral protein that contributes to rhadinovirus persistence and oncogenesis.

immunology↗

bioRxiv2024

Learning from Machine Learning: advancing from static to dynamic facial function quantification

ObjectivesWe assess an open-source Python machine learning algorithms efficacy in image and video analysis of facial palsy (FP) patients. MethodsImages and videos of 60 patients with varying FP severities performing standard movements were obtained from MEEI Facial Palsy database. Landmarks generated on images by the open-source algorithm (adapted from OpenCV and Dlib libraries) and Emotrics (standard for two-dimensional FP analysis) were compared. Considering the human eye as the standard for accuracy, three raters marked perceived errors in each algorithms tracking of five facial features. Cumulative error distributions between both algorithms were compared via normalized root mean square error. FP severity and facial feature-specific error rates were compared using ANOVA tests. Raters also analyzed open-source algorithm-generated video landmarks; similar statistical comparisons between open-source algorithms image and video-based analyses were performed. ResultsCumulative error distribution between both algorithms image analyses was most robust for normal function; significant discrepancies were observed in mild/moderate flaccidity and nearly-normal/complete synkinesis. Both algorithms had similar error rates across all facial features (p=0.76) and FP severities (p=0.37). In the open-source algorithms video analysis, mild synkinesis (24.7%) and severe flaccidity (19.7%) had the highest error rates. Comparing image and video analyses generated by the open-source algorithm, video analyses had lower error rates across all FP severities (p<0.001). ConclusionsWe report for the first time the feasibility and relative accuracy of a Python open-source algorithm for dynamic facial landmark tracking in FP videos. The demonstrated superiority of landmark tracking with videos over images can improve objective FP quantification.

bioinformatics↗