Biomedical Microdevices最新文献

Neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), represent a growing health burden, while early diagnosis remains challenging because established diagnostic approaches, including cerebrospinal fluid analysis and neuroimaging, are invasive, expensive, and unsuitable for decentralized point-of-care testing. Recent progress in electrochemical and paper-based biosensors has enabled low-cost, portable, and multiplexed biomarker analysis. However, their clinical translation remains limited by low biomarker concentrations, matrix interference, nonspecific adsorption, sensor-to-sensor variability, storage stability, and the need for validation using clinical samples. Herein, a paper-based multiplex electrochemical aptasensor was developed for simultaneous impedimetric detection of amyloid beta-42 (Aβ-42) and alpha synuclein-60 (αS-60), biomarkers associated with AD and PD, respectively. The device comprised screen-printed carbon and silver electrodes on a cellulose substrate, two independently functionalized working electrodes, zinc oxide nanorods (ZnO-NRs) as electroactive signal-amplification layers, and target-specific DNA aptamers as biorecognition elements. Electrochemical impedance spectroscopy was used for label-free measurement of target-induced changes in charge-transfer resistance. The sensor exhibited a linear response from 0.1 ng/mL to 10 µg/mL for both biomarkers, with limits of detection of 4.41 ng/mL for Aβ-42 and 2.62 ng/mL for αS-60. The platform further detected both analytes in artificial cerebrospinal fluid and artificial saliva, demonstrating that the paper-based sensing interface remains responsive in physiologically mimicked matrices. Overall, this study provides a low-cost, disposable, label-free, and multiplexed proof-of-concept platform for simultaneous AD- and PD-associated biomarker detection. Although the system demonstrates promising performance in artificial matrices, further optimization of sensitivity, surface immobilization, antifouling behaviour, long-term storage, and validation using real clinical saliva and CSF samples will be required before practical diagnostic deployment.

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Circulating tumor cells (CTCs), shed from primary tumors into the peripheral bloodstream, are important biomarkers for early cancer diagnosis and disease monitoring. Deterministic lateral displacement (DLD) microfluidic devices enable label-free CTC isolation through continuous, size-based separation. However, DLD performance is highly sensitive to coupled geometric and operating parameters, making optimal design challenging. Conventional trial-and-error approaches are computationally expensive and provide limited insight into parameter interactions, necessitating data-driven optimization. Here, a Bayesian optimization (BO) framework integrating high-fidelity numerical simulations with Gaussian process (GP) surrogate modeling is developed for efficient DLD design. Using pillar gap size, post-array tilt parameter and flow rate as design variables, BO iteratively constructs a surrogate of the nonlinear target-particle lateral-displacement response and guides adaptive sampling. The GP model achieved high predictive accuracy, with an (:{R}^{2}) of 0.9873 and a mean absolute error of (:1.24text{}mu:text{m}) on an independent validation dataset. Compared with Evolution Strategy (ES), BO reduced the evaluations required to identify the best-performing design within the prescribed design space by 67.2% and the time-to-solution by 80.0%, while outperforming Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and conventional parametric sweep approaches. The BO-identified design produced a target-particle lateral displacement of (:75.51text{}mu:text{m}). Post-optimization particle-size and outlet-region analyses identified an effective critical diameter of approximately (:11.4text{}mu:text{m}text{}) and demonstrated distinct collection of the investigated NSCLC CTC target surrogate and smaller blood-cell-relevant background-size particles across three release positions. The GP surrogate also enables response-surface mapping, global sensitivity analysis and uncertainty quantification. Results identify the post-array tilt parameter as the dominant factor governing target-particle lateral displacement, while flow rate strongly interacts with geometric variables. Uncertainty analysis confirms stable performance under perturbations. The proposed framework provides an efficient, interpretable, and reliability-aware approach for DLD microfluidic device design.

Bloodstream infections (BSIs) lead to sepsis, an acute inflammatory response associated with high mortality. Current clinical treatment pathways rely on culture-based diagnostics to identify the infectious agent and determine its susceptibility to antibiotics, delaying targeted therapy. Recent approaches to accelerate this focus on the isolation of bacteria directly from blood cultures for rapid downstream analysis using haemolytic agents to replace the gold-standard purity plate subculture. However, many of these methods impact bacterial viability during the early stages of growth. Rapid phenotypic antimicrobial susceptibility testing requires a pure inoculum of actively dividing bacteria to deliver early results (within 3 h), but most isolation chemistries are developed for mass spectrometry identification which does not require viable bacteria. This is particularly relevant for Gram-positive pathogens, as these are more susceptible to detergent agents used for haemolysis. This study investigated five different blood lysis protocols for the isolation of two different bacteria; Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) from positive blood bottles. Bacterial viability was determined with microfluidic impedance cytometry and standard colony counting, which was also used to determine the extraction efficiency. For all five protocols, the numbers of extracted bacteria were generally high for the two organisms when determined with colony counting (following overnight incubation). However, when determined immediately after extraction with impedance cytometry, the counts were lower for S. aureus. The bacterial viability was also investigated with optical flow cytometry immediately after extraction showing inhibition of S. aureus growth with Sepsityper^® but not with the milder detergent saponin.

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Cardiac rhythm is a critical clinical indicator for cardiac arrhythmias and adverse events during drug toxicity studies. In vivo, cardiomyocyte responses to pharmacological agents occur within minutes and are strongly influenced by dynamic drug delivery through blood flow. However, conventional 2D and 3D static culture systems fail to replicate these fluid flow kinetics, limiting their physiological relevance for assessing beat rate responses. Here, we present Mera, a microphysiological system (MPS) developed by Hooke Bio, designed to support long-term culture and functional analysis of 3D cardiac spheroids composed of human induced pluripotent stem cell-derived cardiomyocytes and cardiac fibroblasts. Mera enables dynamic perfusion, allowing investigation of cardiomyocyte beat rates under physiologically relevant flow conditions. The platform is designed to support up to 640 spheroids per run across its modular architecture, and integrates automated imaging, fluid handling, and user-friendly software, operating under controlled physiological conditions (37 °C, 5% CO₂); flow rates are tunable between 0 and 12.5 mL/min to mimic in vivo environments. In the present study, we demonstrate its use at a smaller experimental scale (up to n = 18 per condition), with full-scale throughput and parallelisation remaining to be validated in future work. Pharmacological testing with verapamil, isoproterenol, calcium chloride, and propranolol demonstrated real-time, reversible modulation of beat rate under flow, including recovery following drug-induced suppression. System variability was comparable to a temperature-controlled reference platform, supporting robust statistical analysis. Dose-response studies yielded IC₅₀ values consistent with literature, confirming physiological relevance. Collectively, these results demonstrate that Mera provides a reproducible, human-relevant platform for modelling cardiomyocyte beat-rate responses to pharmacological modulation under dynamic flow. By enabling dynamic drug exposure and automated analysis, Mera represents a promising approach methodology (NAM) for cardiac drug response testing; incorporation of contractility and electrophysiological readouts will be an important next step toward supporting its use in predictive cardiac safety assessment.

Deformable Discoidal Polymeric Nanoconstructs (DPN) represent a biomimetic drug-delivery platform designed to overcome the limitations of conventional spherical nanoparticles. Their discoidal geometry, tunable deformability, and PLGA/PEG-based composition promote vascular margination, reduce phagocytic sequestration, prolong circulation, and enhance accumulation at pathological sites. This review highlights the design principles, fabrication strategies, therapeutic applications, and translational potential of DPN. In oncology, DPN have been engineered to improve the loading and sustained release of docetaxel through multi-passage fabrication, prodrug conjugation, and hierarchical micro-combinatorial hydrogel particles (µCGP). These discoidal particles enhanced therapeutic efficacy in preclinical models of triple-negative breast cancer, glioblastoma, and lung metastases. Beyond cancer, surface-functionalized DPN carrying tissue plasminogen activator improved clot targeting and thrombolysis while limiting neurological toxicity in ischemic stroke models. Overall, DPN integrate geometry, mechanics, and surface functionality to achieve vascular confinement, targeted delivery, and improved safety. Their modular design, biocompatible materials, and favorable preclinical performance support further development as versatile platforms for cancer therapy, metastatic disease, and thrombotic disorders.

Nephrotic syndrome (NS) is characterized by heavy proteinuria and is frequently accompanied by a hypercoagulable, hemorheologically abnormal state. Although glomerular filtration barrier (GFB) failure is central to proteinuria, the contribution of altered microhemodynamic cues to endothelial barrier injury remains incompletely defined. The endothelial glycocalyx (EG) is both a permeability barrier and a mechanosensor of fluid shear stress (FSS). We combined an adriamycin (ADR)-induced NS rat model with vascular-on-a-chip (VOAC) and glomerulus-on-a-chip (GOAC) platforms. In vivo, coagulation, hemorheology, EG structure and components, inflammatory adhesion molecules, and urinary protein excretion were assessed, with prednisone acetate and sulodexide used as mechanistically distinct comparators. In vitro, endothelial cells were exposed to defined flow-rate conditions to isolate the effect of reduced mechanical stimulation on EG integrity, cytoskeletal organization, inflammatory activation, and GOAC permeability. ADR-treated rats developed hypercoagulability and increased blood viscosity together with marked EG loss and proteinuria. Sulodexide ameliorated hemorheological abnormalities and restored the integrity of the EG, whereas prednisone acetate predominantly reduced proteinuria and inflammatory adhesion-molecule expression. In the chip models, stepwise reductions in perfusion flow rate were associated with progressive EG loss, cytoskeletal disorganization, endothelial activation, and increased glomerular barrier permeability. These complementary models support reduced flow-associated mechanical stimulation as a plausible contributor to EG dysfunction and glomerular barrier leakage in NS. These data did not confirm that ADR-induced injury was entirely mediated by hemodynamic alterations; rather, they uncovered an EG-centered mechanobiological pathway that may operate alongside direct toxic and inflammatory insults.

Single-cell analysis has become an essential approach for understanding cellular heterogeneity and its implications in biological function, disease progression and therapeutic response. Microwell platforms provide a versatile approach for single-cell analysis by spatially confining individual cells while maintaining access for imaging, perturbation and downstream molecular measurements. This review examines how microwell engineering determines the information that can be obtained from individual cells. We first discuss how cell-loading strategies, well geometry, platform architecture and material selection influence cell capture, spatial organisation and experimental accessibility. Then, review major applications of microwell platforms in cellular behaviour and cell–cell interactions, secretome analysis, genomic and transcriptomic profiling, and drug screening and precision medicine. Across these applications, microwells provide a distinct advantage by preserving cell identity and enabling spatial, temporal, functional and molecular measurements to be linked within the same workflow. However, these capabilities also generate increasingly complex datasets that are difficult to analyse using conventional approaches. We therefore examine the emerging integration of artificial intelligence (AI) for automated image analysis, cell tracking, phenotype classification, behavioural analysis and prediction of cellular and drug responses. Above all, this review distinguishes AI approaches that have been directly demonstrated in microwell-based studies from those developed in the broader single-cell field that remain prospective for microwell applications. Finally, future opportunities for multimodal AI, foundation models, large language model agents and autonomous laboratory systems will be discussed. By linking microwell engineering with AI-driven analysis, this review highlights how experimental design can determine the information available for computational analysis and identifies opportunities to move microwell-based single-cell research from measurement towards prediction and autonomous discovery.

Nanomedicine has progressed far beyond its early role as an experimental drug-carrier toolbox and today stands as a clinically validated enabling technology. Liposomal formulations and albumin-bound nanoparticles have transformed cancer therapy, while lipid nanoparticle (LNP) platforms accelerated the rapid development and global deployment of SARS-CoV-2 mRNA vaccines—demonstrating how nanoscale engineering can reshape therapeutic response, manufacturing speed, and public health impact. With these successes as foundation, the field is rapidly expanding into more sophisticated delivery systems including cell-hitchhiking nanoparticles, biomimetic multivalent vaccines, implantable immunotherapy depots, high-loading polymeric micelles, organ-targeted gene and RNA carriers, and bio-hybrid extracellular vesicle and mitochondrial therapies. These platforms aim not only to transport drugs, but to modulate immunity, direct regeneration, and enable precision intervention at the cellular and molecular scale. This perspective summarizes the current landscape of biomedical nanotechnologies and outlines how their continued evolution positions nanomedicine as an enabling science driving the next generation of therapeutics.

Microfluidic paper-based analytical devices (µPADs) have gained attention for clinical diagnostics due to low cost, simplicity, and suitability for point-of-care testing. However, high reagent and sample consumption, limited analytical efficiency, and non-optimized designs still restrict broader use. To overcome these limitations, we propose a miniaturized and re-optimized µPAD design for colorimetric dopamine that reduces the required assay time and minimizes reagent and sample consumption while maintaining reproducible analytical performance in biological samples. To achieve this goal, six distinct µPAD geometries were designed and evaluated to examine the effect of device architecture on colorimetric signal development, followed by optimization of the selected geometry and reaction conditions. Based on its temporal color development profile and signal intensity, the most suitable configuration was selected for further optimization This optimized platform was further evaluated or reagent consumption, response time, reproducibility, and analytical performance in biological samples. The optimized miniaturized µPAD design achieved more than a six-fold reduction in reagent usage and a two-minute decrease in drying time. The optimized configuration showed an LOQ of 0.0517 µmol/L in buffer, while the selectivity experiments showed signal deviations within ± 3% for the tested interferents. Furthermore, validation in spiked human serum and plasma samples yielded recoveries of 101–102.5%, supporting the feasibility of the proposed µPAD for dopamine analysis in complex biological matrices. Overall, these findings demonstrate the potential of the proposed design as a low-volume and reagent-efficient platform for point-of-care colorimetric sensing and provide a practical basis for further evaluation toward preliminary clinical screening applications.

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