Biology subjects

Rund, L.

Publications and source records attributed to Rund, L..

4 recordsLinked to original sources

bioRxiv2025

Hybrid peptide DNA nanomaterials enable potent and broad-spectrum virus neutralization

The continued emergence of antigenic drift and drug-resistant viral strains highlights the need for antiviral strategies that deliver robust efficacy, broad subtype coverage, and minimal off-target toxicity. We demonstrate a potent and broad-spectrum strategy that employs hybrid biomaterials of Urumin (a host defense peptide) and a honeycomb (HC) DNA origami through spatially organized multivalent presentation for enhanced antiviral efficacy. Molecular dynamics simulations reveal that Urumin penetrates and destabilizes the hemagglutinin (HA) trimer core, disrupting influenza A viral (IAV) entry. Arranging Urumin in trimeric clusters on the HC enables potent multivalent binding to trimeric HAs on IAV, enhancing antiviral efficacy at nanomolar concentrations, [~]1,000-fold more effective than free Urumin. In vitro assays confirm HC-Urumin outperforms free Urumin in blocking viral entry and preserving cell viability in more IAV subtypes. In vivo studies show that compared to free Urumin, HC-Urumin treatment reduces disease severity, preserves physiological behavior, and decreases mortality in infected mice, while maintaining virus-specific adaptive immune responses without altering humoral immunity. Our study offers an advanced and effective materials platform and strategy for broad-spectrum, low-dose intervention against human and animal IAVs, which can be adapted to combat other viruses by patterning corresponding host defense peptides on custom designed DNA nanostructures.

bioengineering↗

bioRxiv2021

Automated identification of piglet brain tissue from MRI images using Region-Based Convolutional Neural Networks

Magnetic Resonance Imaging is an important tool for characterizing volumetric changes of the piglet brain during development. Typically, an early step of an imaging analysis pipeline is brain extraction, or skull stripping. Brain extractions are usually performed manually; however, this approach is time-intensive and can lead to variation between brain extractions when multiple raters are used. Automated brain extractions are important for reducing the time required for analyses and improving the uniformity of the extractions. Here we demonstrate the use of Mask R-CNN, a Region-based Convolutional Neural Network (R-CNN), for automated brain extractions of piglet brains. We validate our approach using Nested Cross-Validation on six sets of training/validation data drawn from 32 pigs. Visual inspection of the extractions shows acceptable accuracy, Dice coefficients are in the range of 0.95-0.97, and Hausdorff Distance values in the range of 4.1-8.3 voxels. These results demonstrate that R-CNNs provide a viable tool for skull stripping of piglet brains.

neuroscience↗

bioRxiv2021

Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS)

Inadequate myelination in the central nervous system is associated with neurodevelopmental complications. Thus, quantitative, high spatial resolution measurements of myelin levels are highly desirable. We used spatial light interference microcopy (SLIM), a highly sensitive quantitative phase imaging (QPI) technique, to correlate the dry mass content of myelin in piglet brain tissue with dietary changes and gestational size. We combined SLIM micrographs with an AI classifying model that allows us to discern subtle disparities in myelin distributions with high accuracy. This concept of combining QPI label-free data with AI for the purpose of extracting molecular specificity has recently been introduced by our laboratory as phase imaging with computational specificity (PICS). Training on nine thousand SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate gestational size and diet type with an accuracy of 82% and 80%, respectively. To our knowledge, this type of evaluation is impossible to perform by an expert pathologist or other techniques.

bioinformatics↗

bioRxiv2020

Quantifying myelin content in brain tissue using color spatial light interference microscopy (cSLIM)

Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses.

neuroscience↗