Ultrafast and real-time on-chip quantitative polymerase chain reaction (ROC-qPCR) with sequence-specific signaling.

Biomimetic MOF/MIPs carbohydrate microfluidic paper chip: A versatile sialic acid detection platform from point-of-care screening to food rapid analysis.

Thermoreversible cell-derived extracellular matrix only hydrogel (CEOgel): Development, characterization, and applications.

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men, with bone representing the predominant metastatic site. Progress in treating bone metastatic disease is hindered by lack of preclinical models that faithfully recapitulate the bone microenvironment while reducing animal use. Biomaterial-based platforms offer a powerful alternative, enabling controlled reconstruction of bone composition, metabolic cues, and tumour-matrix interactions. A defining feature of PCa progression is citrate-centred metabolic reprogramming. While healthy prostate epithelial cells accumulate and secrete citrate, aggressive PCa cells import and oxidise it to sustain growth. Given the citrate-rich nature of prostate tissue and bone, we hypothesised that bone-derived citrate may be exploited by metastatic PCa cells to support bone colonisation. We developed a bone-mimetic platform by functionalising hydroxyapatite nanocrystals with citrate (HA-Nc-Cit) and incorporating them into collagen-based 3D matrices within a microfluidic chip. HA-Nc-Cit were characterised and citrate release quantified. Metastatic PCa cells were analysed for migration, viability, clonogenicity, metabolic reprogramming, and citrate transporter expression. HA-Nc-Cit released physiologically relevant citrate levels. Citrate exposure enhanced migration of androgen-independent PC3 cells and, within collagen type I-enriched matrices, increased clonogenicity, upregulated plasma membrane citrate transporter, suppressed glycolysis, and promoted lactate fermentation and mitochondrial biogenesis, without affecting respiratory chain or lipid metabolism. Citrate buffering supported PC3 clonal survival under acidic stress mimicking tumour acidification. In conclusion, citrate-functionalised HA-Nc promotes bone tropism of aggressive PCa by enhancing migratory potential, modulating tumour metabolism, and buffering extracellular acidification, underscoring the value of biomaterial-based models for studying bone-tumour interactions and guiding therapeutic development. STATEMENT OF SIGNIFICANCE: PCa often spreads to bone, but current models fail to capture the complexity of bone environment, limiting progress in treatment development. In this study, we created a 3D bone-mimicking system by binding citrate, a key bone metabolite, to hydroxyapatite nanocrystals mimicking bone mineral and embedding them in collagen-based matrices. This platform shows how citrate not only fuels PCa cells but also buffers the acidic conditions they create, making bone more prone to tumour growth. Unlike traditional models, such biomaterial-based approach combines mineral chemistry, metabolism, and pH regulation in a controlled setting. This work introduces a tool to study bone-tumour interactions and guide future therapies for metastatic PCa.

3D printing integrated trapezoidal spiral chip coupled with ICP-MS for single-cells analysis.

MASEA: A microfluidic system for in situ evaluation of tumor angiogenesis in PDO-endothelial co-culture.

Biomimetic flowing vessel-on-a-chip recruiting glycocalyx features for investigating dexmedetomidine function.

Microfluidic chips have relatively short test cycles, and their ability to reproduce tissue structure, environment and function with a certain degree of complexity greatly improves the accuracy of toxicity test models. In this study, a vascularised microfluidic chip containing lung-hepatocytes was constructed to test the toxicity of total particulate matter (TPM) in mainstream cigarette smoke. After exposure to TPM for 3 consecutive days, there was a significant increase in LDH release from chip cells, significant changes in the MUC5AC and ZO-1 proteins of Calu-3 and a significant increase in the relative expression level of the CYP1A1 gene of HepG2 in a dose-dependent manner. These results indicate that TPM can penetrate through Calu-3 cultured at the air-liquid interface in the upper chamber of the chip and HUVEC in the middle chamber, affecting HepG2 in the lower chamber. This reflects the entire invivo exposure process of TPM under 3D conditions. The chip constructed in this study can assess key toxicological endpoints of TPM. It not only simulates the primary pulmonary response to TPM but also captures the subsequent hepatic metabolic activation. This provides a more comprehensive and physiologically relevant toxicity assessment and offers a new method for TPM toxicity testing.

Improving overall health and preventing complications is crucial for timely and effective treatment of diabetes patients. In this direction, accurate measurement and detection of glucose concentration in blood is essential. The aim of this study is to develop an affordable, reliable and accurate Point-of-Care (POC) diagnostic platform for glucose concentration detection using microfluidic and colorimetric principles. A microfluidic chip is fabricated which provides the base for efficient colorimetric reaction. The proposed system requires only ~ 20 µL of sample per microwell, with a colorimetric reaction time of 3-4min. The chip is positioned inside a compact, USB-powered, 3D printed image capture device affixed with a high-resolution camera so that variables such as camera position, focal distance and lighting conditions can be controlled. The analysis workflow is adaptable for integration with embedded systems or laptops, making it suitable for real-time deployment in Point-of-Care settings without the need for smartphones or calibration tools. The images captured inside the device (1280 in total, corresponding to 16 glucose concentration levels ranging from 50 to 200mg/dL) were labeled based on known concentration levels and subjected to standardized preprocessing. Key preprocessing steps included Region of Interest (ROI) extraction using a fixed box detection algorithm, normalization of pixel values, image resizing to 128 × 128 pixels, and standardization using ImageNet parameters. These ensured uniformity and robustness prior to feature extraction and machine learning classification. The classifiers used are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and K-Nearest Neighbours (KNN). The RF machine learning classifier achieved the highest cross-validation accuracy of 87.47% and precision of 89.14%, demonstrating its ability to effectively distinguish between different glucose concentration levels. The confusion matrix and ROC curve analysis further validated the model's robustness, with minimal misclassifications and a high mean AUC value of 0.9509. The results thus highlight the potential of image-based glucose concentration estimation as a cost-effective and scalable solution for real-time monitoring in biomedical applications.

Identifying blood groups accurately is critical for safe medical practices, especially in emergencies, surgeries, and prenatal care. Conventional methods often depend on visual inspection of agglutination reactions, which can be error-prone, particularly when using small sample volumes. In this work, we introduce an efficient and intelligent system for blood type detection that combines digital microfluidics with advanced deep learning and antigen-based decision logic. A Vision Transformer (ViT) model was trained to recognize agglutination patterns in droplet-based blood images, followed by an automated mapping of standard ABO/Rh rules to assign the corresponding blood group based on antigen presence. The proposed system achieved 100% accuracy specifically in detecting antigen-antibody agglutination reactions, with perfect scores across precision, recall, specificity, and F1-score. Our method reduces the need for large reagent volumes and minimizes testing cost, while also improving reliability. In future work, our aim is to embed this system into a compact, microfluidics paper-based device, enabling low-cost, AI-assisted blood typing in portable point of care settings.

Hand, foot, and mouth disease (HFMD) is a common childhood infection caused by enteroviruses, which exhibit distinct regional and seasonal epidemiological patterns. Wastewater-based epidemiology is a crucial tool for monitoring population infection dynamics and viral subtype distribution. However, the lack of effective on-site viral detection methods limits timely early warning and effective surveillance of infectious disease outbreaks. This study developed a one-pot RT-RPA/CRISPR-Cas12a assay-based, string-powered flywheel microfluidic chip for the multiplex detection of HFMD viruses in wastewater. First, by leveraging the regulatory effect of heparin sodium on CRISPR/Cas12a activity, a one-pot RT-RPA/CRISPR-Cas12a system was constructed to detect four major subtypes of HFMD virus (EV-A71, CV-A16, CV-A6, and CV-A10). Subsequently, this method was integrated into a pull-wire, flywheel-type, dual-axis centrifugal microfluidic chip, named the Heparin-Inhibited CRISPR-Associated System Chip (HICAS-Chip), enabling integrated enrichment, purification, elution, and multiplexed detection. The HICAS-Chip allowed visual detection of nucleic acids at 10 aM sensitivity within 1 h, corresponding to the sensitivity of the one-pot RT-RPA/CRISPR-Cas12a assay. During a year-long wastewater monitoring program in Guiyang City, China, the HICAS-Chip identified EV-A71 and CV-A10 as the predominant circulating subtypes, with incidence peaks observed in June, November, and December. The wastewater detection results obtained using HICAS-Chip showed high concordance (95.83%) with RT-qPCR assays. This platform provides an efficient portable device for the early detection and continuous monitoring of HFMD epidemic trends by wastewater-based epidemiology.

This paper proposes an integrated microfluidic system that can identify, count, prepare, and sort magnetic hydrogel microparticles using alternating current (AC) magnetic susceptibility measurements. The integrated microfluidic system was composed of a microplanar coil for AC magnetic susceptibility measurements and a multifunctional microfluidic chip above the coil. Through the flow-focusing method, Fe3O4/PEGDA (Fe3O4/polyethylene glycol diacrylate) magnetic hydrogel microdroplets were generated in a microchannel, then passed over a microplanar coil, and then magnetized to generate a stray field. The strength of the stray field was related to the AC magnetic susceptibility and changed the coil's induced voltage. By detecting the voltage change, the magnetic hydrogel microparticles could be accurately identified and counted. Furthermore, magnetic hydrogel microparticles with sufficient magnetic content were passively separated from nonmagnetic or weakly magnetic particles using a gradient magnetic field. The system integrated the preparation, identification, counting, and sorting of magnetic hydrogel microparticles and also realized AC magnetic susceptibility measurements, which is difficult to accomplish using other methods. This system shows great potential for biomedical testing and drug-carrying engineering applications, as the ability to identify and count magnetic hydrogel microparticles based on their AC magnetic susceptibility enables precise determination of drug dosage in targeted delivery, while the passive separation of particles with sufficient magnetic content from nonmagnetic or weakly magnetic ones using a gradient magnetic field allows for controlled release and targeted accumulation of therapeutic carriers.

Underground hydrogen storage, involving periodic injection and extraction of hydrogen gas, serves as a crucial approach for achieving energy peak shaving and accommodating large-scale renewable energy. However, microorganisms residing underground may undergo metabolic reactions when stimulated by hydrogen, producing gases and altering rock surface properties. This could potentially influence hydrogen migration and storage behavior, yet the underlying mechanisms remain poorly understood. To address this, this study introduces microbial reactions within a microfluidic chip and combines hydrogen displacement experiments under varying pressure differentials to reveal two distinct phenomena by which microbial activity influences hydrogen flow pathways. It was observed that when microbially produced gas communicates with hydrogen, it triggers a readjustment of flow pathways and accelerates the advancement of the hydrogen front. In addition, when microbially produced gas forms stable gas mass downstream in the pore network, it induces shifts in dominant flow pathways. Contact angle measurements further confirm that microbial metabolism significantly reduces pore surface wettability, though this wettability change exhibits pronounced spatial heterogeneity. Displacement results under varying pressure differentials reveal that at low pressures, reduced capillary resistance facilitates sweep range and higher hydrogen saturation. Conversely, at high pressures, viscous flow dominates; weakened wettability accelerates breakthrough while inhibiting lateral branch development, ultimately reducing overall saturation. This study provides novel experimental evidence for understanding microbe-flow interactions during underground hydrogen storage.

Extracellular vesicles (EVs) and EV-derived microRNAs (EV-miRNAs) are emerging as valuable nanoscale circulating biomarkers for tumor progression and immune responses. Conventional detection methods, such as quantitative reverse transcription polymerase chain reaction (qRT-PCR), require large sample volumes and labor-intensive purification, limiting the analysis of EV-miRNAs from scarce samples. In this work, we present an integrated DropFET device that combines a high-density active-matrix digital microfluidic (AM-DMF) chip with a silicon nanowire array field-effect transistor (SiNW array FET) sensor for fully integrated EV-miRNA detection. Based on a bio-cascade strategy, EVs are efficiently captured with dual-antibody-functionalized magnetic beads, lysed in situ on-chip, and the released miRNAs are directionally delivered to the SiNW sensor for electrical sensing. With a detection limit of 10-17 M, the device discriminates single-base mismatches and reliably distinguishes EV-miRNA expression differences between normal and M1 disease states. This integrated approach enables low-volume EV enrichment and ultrasensitive EV-miRNA detection, offering a promising platform for clinical analysis of rare samples.

This study investigated the rapid drug delivery capabilities of neutrophil-like cells using a microfluidic chip-based mechanical deformation approach, with an emphasis on glioblastoma treatment at the cellular level. We designed a microfluidic chip comprising multiple constriction gaps and parallel microchannels to enable efficient drug loading into HL-60 cells and neutrophil-like cells derived from differentiated HL-60 cells (dHL-60 cells). Optimization was performed using dye molecules, including FITC-dextran (4 kDa, 20 kDa) and FITC-BSA, with delivery efficiency and the cell recovery rate serving as critical evaluation parameters. The optimal performance was achieved at a gap size of 8 μm and a flow rate of 150 μL min-1; for FITC-BSA, a concentration of 300 μg mL-1 was deemed suitable. Under these conditions, neutrophil-like cells loaded with albumin-bound paclitaxel (PTX-ALB) were successfully and rapidly prepared, yielding a delivery efficiency of 55.93 ± 19.7% and a drug loading of 784.20 ± 74.6 ng per 105 cells, with a throughput of up to ∼107 cells per hour. The chemotactic performance of PTX-ALB-loaded neutrophil-like cells did not change significantly, and these cells could cross the endothelial barrier constructed in vitro and exerted antitumour effects on U87-eGFP cells. The antitumour effects could be further strengthened by increasing the dosage of drug-loaded cells (from 4× 105 cells to 1× 106 cells) and extending the treatment duration (from 48 hours to 72 hours), which reduced U87-eGFP cell viability to 80 ± 7% and 62 ± 3%, respectively. This microfluidic-based mechanically mediated cargo delivery platform represents a rapid, high-throughput strategy for cell-based drug loading, with broad potential in cellular therapy and immunotherapy.

Microfluidics-enabled proteomic profiling reveal iron-driven immune evasion by an antimicrobial-resistant pathogen.

Rapid, portable, and automated determination of minimum inhibitory concentration (MIC) is critical for antimicrobial susceptibility testing in decentralized settings. We present TCG-CMDA (Tree-Shaped Concentration Gradient generator enabled by Capillary Microfluidic Design Automation), a machine learning-guided platform for the automated design of capillary microfluidic chips that passively generate programmable concentration gradients. A key innovation of TCG-CMDA is its ability to achieve fully synchronized, capillary-driven flow of two preloaded agents, enabling precise and reproducible gradient formation without external pumps or valves. By integrating computational fluid dynamics (CFD) simulations, a neural network surrogate model, and quasi-Newton optimization, the platform customizes tree-shaped geometries to match target mixing profiles. We validate TCG-CMDA through dye flow visualization and MIC testing of Escherichia coli against gentamicin, showing high concordance with conventional antibiotic susceptibility testing. The resulting chip supports a streamlined workflow consisting of passive loading, autonomous gradient generation, and incubation (∼2h), followed by direct endpoint assessment, making it well suited for decentralized point-of-care applications. TCG-CMDA establishes a closed-loop, simulation-guided design paradigm for capillary microfluidic systems. Although demonstrated here for MIC-oriented antimicrobial testing, the framework is generalizable and can be extended to other gradient-dependent microfluidic applications through geometry reconfiguration and fluid-specific optimization.

Infectious diseases severely threaten global public health security, necessitating rapid and highly sensitive diagnosis. This study presents a novel multiplex diagnostic platform combining transcription-mediated amplification (TMA) with the CRISPR-Cas12a2 system for rapid and highly sensitive detection of respiratory viruses. The assay uses an integrated microfluidic chip, which can simultaneously identify influenza A/B and respiratory syncytial viruses (RSV-A/B) with optimized CRISPR RNAs and isothermal amplification, achieving detection limits as low as 102 copies/μL within 60 min. The detection system showed excellent specificity; nonspecific reactions were not observed in the presence of nucleic acids from other respiratory pathogens. Clinical validation using nasopharyngeal swabs demonstrated high concordance with real-time quantitative reverse transcription polymerase chain reaction, with most positive samples detected within 40 min. The system eliminates DNA amplification steps, reduces contamination risk, and simplifies the workflow. Using two-step reactions on a centrifugal microfluidic chip, the TMA-CRISPR-Cas12a2 platform offers a promising integrated platform for multiplex respiratory pathogen screening, thereby supporting timely diagnosis and outbreak management.

The efficient and unbiased isolation of small extracellular vesicles (sEVs) from complex biological fluids remains a major obstacle for clinical diagnostics. Here, we report a bioinspired microfluidic chip that integrates a three-dimensional high-curvature TiO2 nano-interface (3D Hic-TiO2) with a biotin-modified artificial insertion peptide (BAIP) modification for rapid enrichment of sEV. Bowl-shaped TiO2 nanospheres fabricated via electrospray provide topological nanotraps that match the size and curvature of sEVs, enabling efficient size-selective capture. Coupled with BAIP-mediated membrane affinity and herringbone-induced chaotic mixing, the BAIP-TiO2-Chip achieved >90% capture efficiency within 5min. Redox-responsive BAIP variants enabled mild release of intact sEVs for downstream analysis. When the BAIP-TiO2-Chip was applied to plasma samples from clinical prostate cancer (PCa) patients, mass spectrometry-based proteomic profiling revealed 110 differentially expressed sEV-associated proteins, including candidates involved in immune regulation and cell adhesion. In parallel, simultaneous quantification of PSA and PSMA mRNAs in sEVs could be achieved. Assisted by machine learning, a boosted decision tree model achieved 80% diagnostic accuracy in distinguishing PCa from benign conditions and healthy donors. This work presents a versatile platform for sEV isolation, enabling both transcriptomic mRNA analysis and proteomic profiling, and provides new molecular insights into PCa for improved early diagnosis.

Although polymerase chain reaction (PCR)-based molecular sensing methods offer faster and more accurate identification of bloodstream infection (BSI) pathogens compared to blood culture methods, conventional PCR platforms still face significant limitations, including bulky instrumentation, high energy consumption, and relatively long assay duration. Photothermal PCR enables accelerated thermal cycling and lower energy input; however, it often suffers from nonuniform heat distribution during ultrashort heating intervals, fluorescence quenching induced by nanomaterials, nonspecific adsorption of biomolecules, and complex device fabrication. To address these challenges, this study presents a novel microfluidic PCR system based on a patterned reduced graphene oxide/polydimethylsiloxane (rGO/PDMS) composite that enables three-dimensional photothermal heating. Beyond its simplified fabrication process, the platform demonstrates uniform heat transfer and eliminates nonspecific adsorption during amplification. Gram-negative bacteria constitute a substantial proportion of pathogens responsible for BSI. To further streamline bacterial detection from whole blood, a novel Gram-negative bacterial DNA extraction method employing antibiotic-modified magnetic beads was integrated into the workflow. The entire detection process, from sample preparation to result readout, can be completed within 40 min, with 40 PCR cycles accomplished in just 5 min. The system achieves a limit of detection for bacteria as low as 102 CFU mL-1 in whole blood.