354 papers
The authors developed IntravChip, a continuously perfused microfluidic platform that mimics a vascularized primary tumor microenvironment to enable the real-time observation, quantitative collection, and super-resolution characterization of intravasated tumor cells, while also serving as a tool for screening anti-metastatic therapies.
This study demonstrates that standard nanoparticle isolation methods inadvertently co-isolate extracellular vesicles, significantly distorting protein corona profiles, and shows that depleting these vesicles is essential for accurately defining the biological identity of nanoparticles and enabling reliable biomarker discovery.
This paper introduces a high-throughput 384-well platform that accelerates the determination of vitrification concentrations by 50-fold, reveals key environmental and molecular factors influencing vitrification, and establishes a predictive mixture model to guide the rational design of low-toxicity cryopreservation formulations.
This study employs a genome-scale CRISPR activation screen to identify native proteins that enhance homology-directed repair, which are then rationally fused to Cas9 to create "TruEditors" that significantly improve precise gene editing efficiency and therapeutic applications in diverse human cell types.
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 presents a refined bioenergetics model for largemouth bass that incorporates feed composition and nutrient-specific digestibility coefficients, demonstrating significantly superior growth prediction accuracy compared to traditional gross energy-based models across both compiled and field datasets.
This study demonstrates that while simplified finite element models of the mandible can preserve spatial stress-strain trends for preliminary implant design, they significantly underestimate peak stress and strain values compared to detailed, tissue-specific models, underscoring the necessity of high-fidelity modelling for final pre-clinical evaluation of temporomandibular joint implants.
This study introduces growth-adaptive spring electronics and a specialized spike processing pipeline to achieve long-term, same-neuron mapping in the developing rat brain, revealing that the transition from synchronous to decorrelated neural activity is driven by a specific subset of neurons progressively weakening their coupling to the local population rather than a global circuit shift.
The paper introduces X-TRUDE, a process-informed characterization platform that integrates in situ pressure sensing, thermal control, and realistic flow geometries to quantitatively link intrinsic hydrogel rheology with extrusion performance, thereby enabling reliable prediction and control of extrusion variability for diverse soft matter systems.
This study reveals that chiral ligand modulation of graphene quantum dots generates distinct structural motifs where nanoscale structural chirality specifically governs passive membrane permeation, while active transport depends primarily on ligand identity and transporter recognition rather than structural chirality.
This study demonstrates that progressive synovial fibrosis and matrix stiffening in osteoarthritis drive the mechanotransduction-mediated activation of fibroblasts and macrophages, establishing a distinct pathological crosstalk that differentiates disease progression from acute surgical responses.
This study demonstrates that electrical surface polarization of apatite-based bone graft materials enhances osteoclast differentiation and resorptive activity in vitro, leading to significantly improved new bone formation and implant integration in vivo, thereby establishing it as a promising non-chemical strategy to boost bone regeneration.
This study demonstrates that reducing N-glycan branching on GM-CSF through glycoengineering significantly suppresses its bioactivity, highlighting the critical role of specific glycan structures in optimizing the efficacy of GM-CSF-based therapeutics.
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.
This paper introduces RDBCycleGAN-CBAM, a novel deep learning model that integrates residual dense blocks and convolutional block attention modules into a CycleGAN framework to significantly improve the quality of low-dose CT images while preserving structural details and reducing radiation exposure.
This paper introduces Sniffbot, an autonomous biohybrid robot that integrates a desert locust antenna with a custom "sniffing" mechanism and a novel search algorithm to achieve real-time odor localization and discrimination, even in challenging windless environments where traditional sensors fail.
This paper introduces Time-domain Epithelial Impedance Measurement (TEIM), a novel method using step current excitation and time-domain analysis to achieve sub-second extracellular impedance measurements of epithelial cell monolayers, offering a 100-fold improvement in time resolution over traditional EIS while maintaining high accuracy for monitoring rapid biological changes.
This study demonstrates that integrating bone suppression as a preprocessing step significantly enhances the accuracy and reliability of pneumothorax segmentation across various deep learning architectures by mitigating anatomical obstructions in chest radiographs.
This study introduces a novel therapeutic strategy using bispecific antibody "Shedders" to recruit the protease ADAM10 to cell-surface proteins, inducing selective extracellular domain shedding that bypasses lysosomal trafficking and effectively reinvigorates immune responses, as demonstrated by the depletion of LAG-3 and other immune receptors.
This study employs an integrated workflow combining in vivo modeling, radiology, deep learning-guided 3D imaging, and multi-omic analysis to identify myeloid and senescent cell signatures as novel molecular biomarkers of the pre-metastatic niche in lung cancer, offering a potential AI-assisted approach to evaluate the risk of local recurrence.