196 papers
Bioengineering sits at the vibrant intersection of biology and engineering, where scientists design new tools to understand life and solve real-world problems. From creating artificial organs to developing smart materials that mimic natural tissues, this field turns complex biological questions into tangible innovations that improve human health and our environment.
At Gist.Science, we track every new preprint in this category directly from bioRxiv. Our team processes each submission to provide both plain-language explanations for curious minds and detailed technical summaries for researchers, ensuring these cutting-edge discoveries are accessible to everyone. Below are the latest papers in bioengineering that have recently appeared on bioRxiv.
This study demonstrates that a novel sublingual, orally disintegrating tablet vaccine (Capot) utilizing bacterial extracellular vesicles encapsulated in a calcium phosphate nanoshell effectively induces durable salivary sIgA immunity and provides robust protection against periodontitis in mice and non-human primates without causing inflammation.
This study utilizes simulations of SHG-informed fiber networks to demonstrate that while collagen fiber crimp does not influence diffusion tensor MRI metrics, fiber dispersion significantly alters axial and radial diffusivity as well as fractional anisotropy, thereby clarifying the relationship between microstructural variations and DTI measurements in tendons and ligaments.
This paper presents a computational model that simulates the interplay between geometry, mechanics, and stochasticity in tissue growth to enable the quantitative analysis of morphology and the inference of dynamic growth mechanisms from static experimental data, demonstrated through applications in 3D-printed scaffolds and cortical bone formation.
This preclinical study demonstrates that HipSAFE, a deep learning tool utilizing EfficientNet-Lite0 to analyze ultrasound images captured by non-expert operators, achieves diagnostic accuracy comparable to or exceeding that of radiologists for detecting hip fractures, offering a promising solution for improved triaging in rural and resource-constrained settings.
The paper introduces AnewOmni, a unified all-atom generative framework trained on over 5 million biomolecular complexes that utilizes programmable graph prompts to successfully design functional small molecules, peptides, and nanobodies across diverse molecular modalities, thereby establishing a foundation for general molecular reasoning in regimes where data and intuition are limited.
This paper introduces TunLSCI, a deep learning-based framework that reconstructs high-fidelity cerebral blood flow images from ultra-low-illumination laser speckle contrast data, thereby reducing phototoxicity by approximately 157-fold and enabling stable, long-term longitudinal monitoring of cerebral perfusion in vivo.
This study presents a novel terahertz metamaterial absorber sensor featuring a central diamond-shaped threefold hexagon structure that achieves near-perfect triple-band absorption and high polarization conversion efficiency to effectively distinguish between healthy and Glioblastoma cells via microwave imaging.
This study demonstrates that coating individual *Chlamydomonas reinhardtii* cells with protective metal-phenolic networks not only enhances stress survival but also induces a reversible quiescent state that redirects carbon flux to significantly boost starch accumulation under light and lipid accumulation in darkness.
This paper demonstrates that the Gradient Impulse Response Function (GIRF) can effectively correct eddy current-induced sidebands in non-water-suppressed proton MR spectroscopy, enabling the recovery of metabolite signals and revealing that water suppression typically causes magnetization transfer effects that underestimate metabolite concentrations.
The paper introduces CRADLE-1, an automated framework that leverages fine-tuned protein language models and a multi-model workflow to accelerate multi-property lead optimization across diverse protein modalities by 4–7x compared to rational design, demonstrating that sequence-function data can largely supersede structural information in a black-box, lab-in-the-loop process.