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 paper introduces D2IM-Strain, a data-driven approach that directly predicts bone strain fields from X-ray computed tomography images, significantly improving accuracy and reducing noise amplification compared to traditional displacement-derived methods.
This study demonstrates that deep-learning-guided tuning of heterodimeric coiled-coil linker stability enables the rational programming of artificial protein tube architectures, allowing for precise control over tube diameter and the formation of complex multilayered tube-in-tube structures through sequential assembly mechanisms.
This paper introduces a photothermal recycling (PTR) biosensing mechanism that utilizes plasmonic thermal effects to rapidly cycle biomolecular binders, thereby enabling continuous, subpicomolar-sensitive molecular quantification in complex biological fluids without the trade-off between measurement speed and sensitivity.
This study adapts the Merton jump-diffusion framework from quantitative finance to model tuberculosis patient survival as a state of biological solvency, demonstrating that a stochastic "distance-to-death" metric based on body mass index and HIV-driven volatility outperforms traditional Cox models in predicting mortality and enabling targeted clinical triage.
This paper validates a predictive simulation-based design optimization platform for personalizing assistive wearable devices, demonstrating that accurately predicting metabolic cost trends rather than achieving perfect biomechanical accuracy is sufficient for identifying optimal device parameters.
To address the lack of standardized reporting and accuracy assessment in microfluidic resistive pulse sensing (MRPS) for extracellular vesicle analysis, the authors developed RPSPASS, an automated software application that enhances data accuracy, ergonomics, and transparency through features like automated calibration, statistical output, and standardized reporting templates.
This study presents a modular biofabrication strategy that overcomes vascularization challenges in engineered skeletal muscle by independently maturing and subsequently assembling aligned contractile myofibers with pre-endothelialized microvascular seeds to create a functional, hierarchically organized tissue model.
This study pioneers the establishment of venom gland organoids from the abundant Colubridae family of non-front-fanged snakes, successfully demonstrating their ability to produce toxins in vitro to overcome the limitations of traditional venom extraction.
The paper introduces eBiota, an integrated platform combining graph-based search, extended flux balance analysis, and deep learning to rapidly design and simulate functional microbial communities from large seed pools for target product generation and pathogen inhibition.
This paper proposes a physics-informed, self-supervised generative model that bridges the simulation-to-experiment gap in 3D localization microscopy by training directly on unlabeled experimental data to produce high-fidelity, fully labeled synthetic images, thereby significantly enhancing the performance of supervised localization networks in complex and low signal-to-noise scenarios.