281 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 single deep-learning pipeline, utilizing a standardized preprocessing framework and an anatomically informed cross-species montage, can achieve robust three-state sleep staging in both humans and rodents and successfully transfer a model trained exclusively on rodent data to human EEG recordings without any retraining.
This study demonstrates that large-scale AI analysis of language, speech, and movement in clinical interviews can effectively disentangle the overlapping symptoms of Autism and ADHD by identifying distinct behavioral signatures, such as narrative divergence and vocal pitch for Autism versus specific motor hyperactivity for ADHD.
This study demonstrates that optogenetically activating WNT signaling in a subset of human pluripotent stem cells within a mixed aggregate drives self-organization into distinct germ layers via non-autonomous TGF{beta}/BMP induction, providing a robust in vitro model that recapitulates key mechanisms of human gastrulation.
This study reveals that residual polarization ellipticity in waveplate-compensated polarization-resolved second harmonic generation microscopy, which persists despite optimal compensation, likely stems from the interaction between the broad spectral bandwidth of femtosecond lasers and wavelength-dependent birefringence in optical elements, thereby limiting the accuracy of quantitative measurements.
This study demonstrates that RNA interference (RNAi) targeting key metabolic genes in the whitefly *Bemisia tabaci* significantly reduces its population and induces high mortality in transgenic cassava, offering a promising long-term solution to control the vector of devastating cassava diseases in East Africa.
This study combines human experiments and modeling to demonstrate that while the Virtual Pivot Point strategy and trunk dynamics effectively manage stability and energy on gentle slopes, humans recruit a multi-joint knee and ankle strategy to navigate steeper terrain, offering critical insights for exoskeleton design.
This study presents a robust photoelectrochemical biosensing platform that integrates clamp-inhibited loop-mediated isothermal amplification with enzyme-free singlet oxygen transduction to achieve highly selective and sensitive detection of KRAS mutations in complex wild-type backgrounds, demonstrating clinical utility through validation in cell lines and patient tissues.
This study optimized the Zebrafish Embryo Genotyper (ZEG) automated system by refining chip design and operational parameters, achieving over 95% DNA collection sensitivity and embryo survival while increasing DNA yield by more than 50% without adverse effects on development.
This paper proposes the Plasmonic-AI-Organoid (PAO) framework, a systems-level architecture that integrates geometry-controlled plasmonic modeling, Bayesian kinetic inference via MCMC, and organoid validation to optimize next-generation biosensors through an active learning loop that significantly reduces computational costs while enhancing analytical performance.
This study demonstrates that the level of anatomical fidelity in computational models of the common iliac veins critically influences both the statistical characterization of shape variability and the accuracy of hemodynamic risk predictions for deep vein thrombosis, revealing that simplified geometries significantly overestimate low wall shear stress areas compared to patient-specific 3D reconstructions.