Lab on a Chip最新文献

This manuscript combined two studies that assessed the effects of cannabidiol (CBD) exposure in distinct organ-on-a-chip models. Hesperos, Inc.'s Human-on-a-Chip® (HoaC) technology utilizes custom-designed human-based, serum-free, and pumpless microphysiological systems (MPS). The first study, study 1, assessed the level of off-target toxicity for CBD and its associated metabolites, 7-carboxy cannabidiol and 7-hydroxy cannabidiol, using a multi-organ heart-liver system. In study 1, CBD was delivered to systems with and without the inclusion of a liver organ module to evaluate metabolic contributions to corresponding cardiac toxicity. The results suggested that metabolism of the parent compound into its metabolites resulted in significant effects when compared to exposure to the parent compound only. Interestingly, significant distinctions in cardiac function, such as beat frequency and contractile force, were detected even though no significant cytotoxic effects were noted. The second study, study 2, utilized the same HoaC technology and employed a central nervous system (CNS) model using human induced-pluripotent stem cell derived cortical neurons. This model assessed CBD efficacy in alleviating stress-induced neural dysfunction in a single-organ model of the CNS, mimicking neuromodulation through a CBD administration route that does not involve first-pass metabolism. The results indicated a concentration-dependent rescue of CBD on cortisol-induced deficits in long-term potentiation (LTP) in this model, which was mirrored by associated changes in anandamide levels. Overall, these studies highlight the potential of human-based in vitro systems to evaluate drug toxicity and efficacy profiles specific to target mechanisms for toxicity and efficacy.

Microbially induced calcium carbonate precipitation (MICP) can transform granular media into cohesive, load-bearing materials, yet pore-scale design rules linking geometry, transport, and mineral growth remain difficult to pin down. Here we present a resin-molded microfluidic pore-network platform that mimics coarse-aggregate architectures relevant to living building materials and allows controlled variation of pore geometry. Devices loaded with Sporosarcina pasteurii were imaged using paired brightfield and transmitted-light polarization microscopy to resolve structure and quantify birefringent CaCO₃. Automated image segmentation constrained analysis to the pore space, enabling high-throughput extraction of precipitation coverage, particle density, and feature size across hundreds of fields. Across the tested range, precipitation outcomes depended on channel height and porosity, whereas pillar diameter showed no measurable effect on coverage when porosity and height were fixed. Lower porosity tended to increase particle density but limit feature growth, while higher porosity and taller channels favored larger, coalescing deposits and more spatially continuous films. These patterns are consistent with transport shaping which regime occurs: channel height and porosity together appear to set diffusive replenishment and byproduct dilution, shifting mineralization between a nucleation-dominated regime of many small deposits and a coalescence-dominated regime of fewer, larger features.

Microplastic pollution in groundwater has become one of the most challenging environmental issues. However, there is a lack of portable instruments for on-site detection of low-abundance microplastics. To address this, we developed a portable instrument for intelligent analysis of low-abundance microplastics in groundwater by integrating the dielectrophoretic (DEP) assembly of a four-phase rotating electric field with an object detection framework based on YOLO26. Firstly, a microfluidic chip was designed and fabricated to construct a portable instrument for rapid assembly, and an object detection model was developed to identify microplastics with diverse sizes, shapes, and colors. Secondly, a computational model was established to theoretically investigate the phase-dependent variation in the electric field and the microplastic assembly by analyzing particle velocities and trajectories. The feasibility of this portable instrument was demonstrated using polystyrene microplastics, which was consistent with the numerical simulations. Thirdly, the assembly performance of the chip was explored for microplastics of different polymer types, and the voltage-amplitude and solution-conductivity dependencies of microplastics assembly were systematically characterized. Depending on these, we used this portable instrument to achieve the intelligent analysis of spherical microplastics, including the size and abundance. Finally, the portable instrument was engineered for the detection of low-concentration microplastics in groundwater to rapidly acquire the size and abundance of microplastics with irregular shapes. This instrument can realize rapid assembly and intelligent analysis of low-concentration microplastics in groundwater and holds great potential in the guarantee of the drinking-water safety with advantages of intelligence, portability, and multiparametricity.

Maintaining precise isothermal conditions in portable nucleic acid amplification tests (NAATs) is critical for reproducible results but remains challenging with conventional single-sided thin-film heaters, which exhibit temperature gradients and strong dependence on ambient conditions. To close this gap, we engineered ThermiQuant™ VitroMini, a dual-sided heater design that achieves volumetric-level temperature uniformity using thin-film heaters while preserving optical transparency for real-time colorimetric loop-mediated isothermal amplification (LAMP) analysis on microfluidic paper-based analytical devices (μPADs). The device integrates two independently regulated indium tin oxide (ITO) heaters (8 Ω each) controlled by independent proportional-integral-derivative (PID) algorithms. Heaters were evaluated under controlled ambient environments of 4 °C (refrigerated), 23 °C (room temperature), and 50 °C (oven). Analytical tests were performed using a colorimetric LAMP assay targeting the SARS-CoV-2 orf7ab gene on μPADs preloaded with dried LAMP reagents, with time-lapse images (30 seconds interval) analyzed via Amplimetrics™ software. VitroMini maintained 65 ± 0.5 °C across 4 to 50 °C ambient conditions and achieved a limit of detection of 34 copies per reaction (4.5 copies per μL) and limit of quantification of 1000 copies per reaction (133 copies per μL), with quantification time (T_q) linearly correlated with log₁₀ DNA concentration. Dual-sided heating eliminated temperature bias, condensation artifacts, and ambient-dependent variability while preserving optical transparency for real-time quantitative LAMP reaction. ThermiQuant™ VitroMini bridges the gap between benchtop volumetric heaters and portable diagnostic devices, offering a compact, low-power platform for quantitative colorimetric molecular analysis on paper with potential for decentralized and field-deployable applications.

Deterministic lateral displacement (DLD) is a widely used microfluidic technique for continuous size-based separation. However, a conventional uniform DLD array is characterized by a single critical diameter (D_c) fixed by its geometry, restricting its ability to fractionate complex samples containing multiple particle populations. Here we present a DLD platform that spatially programs the geometrically estimated D_c after fabrication by thermally modulating the pillar geometry along a single array. Two independently controlled Peltier elements establish a longitudinal temperature field that progressively changes the pillar diameter and inter-pillar gap, generating an estimated D_c(x) profile ranging from 4.7 to 19.2 μm within an array fabricated with uniform initial geometry. As particles traverse the array, size-dependent transitions between bump and zigzag migration modes occur at different positions, producing distinct cumulative lateral displacements and enabling sequential separation within a single device. Using the three-outlet architecture, the device further enables reconfigurable grouping of a four-component particle mixture into three outlet fractions by shifting the applied temperature window. Furthermore, the platform selectively isolated tumor cells, white blood cells, and red blood cells from diluted whole-blood samples spiked with MCF-7 cells. A descriptive assay indicated high post-processing viability of the MCF-7 cells. By enabling post-fabrication programming of the operating separation profile, this work demonstrates how the same physical array can be reconfigured for different separation tasks by changing the applied temperature window.

In this study, we present and analyze a deep learning framework designed to achieve real-time particle localization from successive electrical impedance measurements with a limited training dataset. The goal is to provide the research community with a comprehensive analysis of the role of the different constituent elements of a network and a way to optimize their parameters. All the code is made available (see SI). The proposed algorithm combines a temporal convolutional network (TCN), a bidirectional long short-term memory (BiLSTM), a fully connected network (FCN), and an automated parameter tuning engine. Although vision-based systems could be used to track the position of micrometer-sized particles, this modality has practical limitations due to the size of the equipment, its complexity, and the narrow field of view. Thus, an emerging approach for localization is to exploit the correlation between electrical impedance measurements and the position of the successive particles. While physics-based algorithms have been proposed for localizing particles from impedance measurements, they rely on analytical assumptions on feature correlation. Learning-based approaches remove this bias by directly mapping patterns from raw data. However, state-of-the-art models typically require massive training datasets and offer minimal transparency regarding parameter selection. To contribute to the deployment of such algorithms in the lab-on-a-chip community, we introduce a framework that unifies feature extraction with a data-driven architectural tuning engine, designed to maintain predictive capacity under data-scarcity constraints. This paper analyzes the role of each element of the algorithm with respect to the particularities of particle localization, with the data made available alongside the code. The methodological robustness of the framework is validated via a micrometer-scale particle localization case study. Using a small training dataset of only 53 polystyrene beads of 8 μm and 5 μm, the optimized model establishes a localization accuracy of 3.34 μm ± 1.24 μm, while demonstrating real-time prediction times as low as 0.58 ms to support its potential for closed-loop control applications. While this work validates the methodology using uniform beads to evaluate network behavior independently of biological variability, the underlying optimization framework is inherently generic and so, applicable to more complex, heterogeneous biological samples, and will contribute to the deployment of data-driven approaches in the community.

This study presents a frequency-programmable, acoustofluidic particle routing and separation platform that enables frequency-dependent adjustment of the trajectories and destinations of micro- and submicrometer particles in continuous flows, thereby achieving outlet-switchable particle separation and on-demand switching of particle routing destinations. These capabilities arise from our platform's ability to steer traveling/standing surface acoustic waves (SAWs) via frequency tuning. To achieve these particle routing and separation and SAW control abilities, particularly, our platform employs two azimuthally tapered, frequency-steerable interdigital transducers (AFIDTs), each with azimuthally continuously tapered electrode widths and spacings. Arranged with rotational symmetry, paired AFIDTs can generate steerable standing SAWs whose node-line orientation rotates with excitation frequency. This frequency-steering function was validated by laser vibrometry and particle patterning. We then successfully demonstrated on-demand, frequency-programmable adjustment of micro- and submicrometer particle routes and switching of target outlets, when continuously flowing micro- and submicrometer particles (e.g., polystyrene beads, silica particles, and Jurkat cells) in a microchannel with three exits. Moreover, mixed 5 and 10 μm particles were effectively separated by exploiting size-dependent acoustic radiation forces. Numerical simulations of the SAW and streaming fields corroborated experimental observations. Our frequency-steerable AFIDT platform provides a compact, label-free approach for on-demand, programmable particle routing/separation, with strong potential for integration into microfluidic separation and diagnostic systems.

Micro/nanostructures exhibit distinctive advantages in cell capture and spatial distribution analysis. Nonetheless, conventional cell research approaches face challenges including strong equipment dependence, complex operational procedures, and difficulties in achieving precise spatial confinement. In this study, three-dimensional microcage arrays are fabricated via FL-TPP, enabling the construction of structures with customizable inner diameters and achieving highly precise, three-dimensional biomimetic, and high-throughput cell capture. The fabrication of structures with controllable inner diameters is realized. Through the synergistic effects of gravity and hydrodynamic forces, the microcage arrays efficiently and selectively capture microspheres and fibroblasts of specific sizes. In addition, microcages with varying inner diameters significantly modulate cell behavior and effectively avoid cell damage. This work broadens the applicability of micro/nanostructures in tissue engineering, including cell capture and the construction of biomimetic microenvironments.