354 papers
This study utilizes multimodal surface-enhanced Raman spectroscopy and RNA sequencing of plasma extracellular vesicles to characterize molecular heterogeneity in type 2 diabetes, revealing distinct spectral and microRNA signatures across normal-weight and overweight subgroups of Asian and Non-Hispanic White patients.
Through evolutionary engineering of *Corynebacterium glutamicum*, researchers developed a high-performance strain with enhanced XylA and IolT1 activities that enables efficient whole-cell conversion of D-glucose to the low-calorie sweetener D-allulose at mesophilic temperatures, rivaling traditional high-temperature immobilized enzyme processes.
This study demonstrates that a conservative multi-echo ICA modeling approach, which orthogonalizes regressors to preserve task-related signals, significantly outperforms aggressive denoising methods in motor-task fMRI analyses, particularly when dealing with high task-correlated head motion and low signal-to-noise ratios.
This study introduces an anti-buoyancy spheroid trap (AS-Trap) to enable standardized, long-term quantitative profiling of 3T3-L1 adipocyte spheroids, revealing a progressive density reduction driven by lipid accumulation that mirrors native white adipose tissue and closely resembles in vivo adipocyte morphology.
This study demonstrates that an electroceutical approach effectively counteracts dexamethasone-induced skeletal muscle atrophy and promotes functional recovery in both young and aged models by restoring muscle fiber size and upregulating hypertrophy-related genes, offering a promising non-pharmacological strategy for treating muscle wasting in older populations.
This paper presents a novel three-dimensional, medium-throughput in vitro model system designed to study stromal cell migration across scaffold-defect interfaces and screen collagen-based biomaterials for enhancing critical-sized craniomaxillofacial bone repair.
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 reveals that while deep learning tools for optic nerve axon quantification demonstrate strong performance in their original studies, they exhibit significant generalizability gaps and reduced accuracy when validated on independent datasets, highlighting the urgent need for standardized multi-center testing before widespread adoption.
This paper presents a novel nicotine biosensor developed by screening microbial genomes for specific redox enzymes, successfully creating an electrochemical device capable of detecting nicotine across physiologically relevant concentrations in various bodily fluids.
This study elucidates the cleavage-driven reorganization mechanism behind the color-switching of DNA-templated silver nanocluster (Subak) substrates and leverages this insight to engineer a highly sensitive, ratiometric RNase detection platform (rSubak) that significantly outperforms commercial standards in amplification-free viral diagnostics.
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 utilizes spatial transcriptomics to identify Slc13a5 as a critical regulator of bone mechanoadaptation, demonstrating that its conditional deletion in osteolineage cells enhances mineralization and reduces resorption in response to mechanical loading, thereby highlighting it as a potential therapeutic target for bone fragility.
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 introduces Non-destructive Transcriptomics via Vesicular Export (NTVE), a platform that enables longitudinal, multi-time-point monitoring of RNA expression dynamics in living cells by packaging and exporting stabilized RNA barcodes and endogenous transcripts via engineered virus-like particles, thereby allowing real-time transcriptomic profiling without cell lysis.
This study establishes a modular mRNA-lipid nanoparticle platform utilizing the clinically validated lipid MC3 to achieve sustained, safe, and functional restoration of spermatogenesis across diverse genetic male infertility models, ultimately enabling the generation of healthy offspring.
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.
This paper presents a miniaturized, wireless bioelectronic stimulator built on a liquid crystal polymer substrate that enables stable, autonomous propulsion of a human iPSC-derived cardiomyocyte-powered biohybrid robot in aqueous media without the need for immersed leads.
This study demonstrates that monocytes function as robust biological sensors of aging and frailty, as a deep learning model (scTRAIT) trained on their single-cell behaviors can accurately predict and forecast frailty status in older adults.
This paper introduces a custom MATLAB-based shape factor analysis framework, specifically utilizing radial length variance, to provide a more reliable and comprehensive quantitative method for assessing spheroid and organoid morphology and invasiveness compared to standard shape descriptors.