Modeling the neuroimmune system in Alzheimer's and Parkinson's diseases.

Parkinson's disease (PD) and Alzheimer's disease (AD) are neurodegenerative disorders caused by the interaction of genetic, environmental, and familial factors. These diseases have distinct pathologies and symptoms that are linked to specific cell populations in the brain. Notably, the immune system has been implicated in both diseases, with a particular focus on the dysfunction of microglia, the brain's resident immune cells, contributing to neuronal loss and exacerbating symptoms. Researchers use models of the neuroimmune system to gain a deeper understanding of the physiological and biological aspects of these neurodegenerative diseases and how they progress. Several in vitro and in vivo models, including 2D cultures and animal models, have been utilized. Recently, advancements have been made in optimizing these existing models and developing 3D models and organ-on-a-chip systems, holding tremendous promise in accurately mimicking the intricate intracellular environment. As a result, these models represent a crucial breakthrough in the transformation of current treatments for PD and AD by offering potential for conducting long-term disease-based modeling for therapeutic testing, reducing reliance on animal models, and significantly improving cell viability compared to conventional 2D models. The application of 3D and organ-on-a-chip models in neurodegenerative disease research marks a prosperous step forward, providing a more realistic representation of the complex interactions within the neuroimmune system. Ultimately, these refined models of the neuroimmune system aim to aid in the quest to combat and mitigate the impact of debilitating neuroimmune diseases on patients and their families.

Bio-macromolecular design roadmap towards tough bioadhesives.

Emerging sutureless wound-closure techniques have led to paradigm shifts in wound management. State-of-the-art biomaterials offer biocompatible and biodegradable platforms enabling high cohesion (toughness) and adhesion for rapid bleeding control as well as robust attachment of implantable devices. Tough bioadhesion stems from the synergistic contributions of cohesive and adhesive interactions. This Review provides a biomacromolecular design roadmap for the development of tough adhesive surgical sealants. We discuss a library of materials and methods to introduce toughness and adhesion to biomaterials. Intrinsically tough and elastic polymers are leveraged primarily by introducing strong but dynamic inter- and intramolecular interactions either through polymer chain design or using crosslink regulating additives. In addition, many efforts have been made to promote underwater adhesion via covalent/noncovalent bonds, or through micro/macro-interlock mechanisms at the tissue interfaces. The materials settings and functional additives for this purpose and the related characterization methods are reviewed. Measurements and reporting needs for fair comparisons of different materials and their properties are discussed. Finally, future directions and further research opportunities for developing tough bioadhesive surgical sealants are highlighted.

Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles.

Controlling droplet breakup characteristics such as size, frequency, regime, and droplet quality within flow-focusing microfluidic devices is critical for different biomedical applications of droplet microfluidics such as drug delivery, biosensing, and nanomaterial preparation. The development of a prediction platform capable of forecasting droplet breakup characteristics can significantly improve the iterative design and fabrication processes required for achieving desired performance. The present study aims to develop a multipurpose platform capable of predicting the working conditions of user-specific droplet size and frequency and reporting the quality of the generated droplets, regime, and hydrodynamical breakup characteristics in flow-focusing microdevices with different cross-junction tilt angles. Four different neural network-based prediction platforms were compared to accurately estimate capsule size, generation rate, uniformity, and circle metric. The trained capsule size and frequency networks were optimized using the heuristic optimization approach for establishing the Pareto optimal solution plot. To investigate the transition of the droplet generation regime (i.e., squeezing, dripping, and jetting), two different classification models (LDA and MLP) were developed and compared in terms of their prediction accuracy. The MLP model outperformed the LDA model with a cross-validation measure evaluated as 97.85%, demonstrating that the droplet quality and regime prediction models can provide an engineering judgment for the decision maker to choose between the suggested solutions on the Pareto front. The study followed a comprehensive hydrodynamical analysis of the junction angle effect on the dispersed thread formation, pressure, and velocity domains in the orifice.

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors.

Alternative fuel sources, such as hydrogen-enriched natural gas (HENG), are highly sought after by governments globally for lowering carbon emissions. Consequently, the recognition of hydrogen as a valuable zero-emission energy carrier has increased, resulting in many countries attempting to enrich natural gas with hydrogen; however, there are rising concerns over the safe use, storage, and transport of H2 due to its characteristics such as flammability, combustion, and explosivity at low concentrations (4 vol%), requiring highly sensitive and selective sensors for safety monitoring. Microfluidic-based metal-oxide-semiconducting (MOS) gas sensors are strong tools for detecting lower levels of natural gas elements; however, their working mechanism results in a lack of real-time analysis techniques to identify the exact concentration of the present gases. Current advanced machine learning models, such as deep learning, require large datasets for training. Moreover, such models perform poorly in data distribution shifts such as instrumental variation. To address this problem, we proposed a Sparse Autoencoder-based Transfer Learning (SAE-TL) framework for estimating the hydrogen gas concentration in HENG mixtures using limited datasets from a 3D printed microfluidic detector coupled with two commercial MOS sensors. Our framework detects concentrations of simulated HENG based on time-series data collected from a cost-effective microfluidic-based detector. This modular gas detector houses metal-oxide-semiconducting (MOS) gas sensors in a microchannel with coated walls, which provides selectivity based on the diffusion pace of different gases. We achieve a dominant performance with the SAE-TL framework compared to typical ML models (94% R-squared). The framework is implementable in real-world applications for fast adaptation of the predictive models to new types of MOS sensor responses.

Sheath-assisted versus sheathless dielectrophoretic particle separation.

Lab-on-chip devices are widely being used for binary and ternary cell/particle separation applications. Among the lab-on-chip methods, dielectrophoresis (DEP) is a cost-effective and label-free method, with great capabilities for size-based separation of cells and particles, which is mostly performed in sheath-assisted forms. However, the elimination of the sheath flows offers advantages such as ease of operation and higher sample throughput. In this work, we present a comparison of sheath-assisted and sheathless DEP separation of three sizes of microparticles using tilted electrodes. The sheath-assisted design was capable of separating the 5, 10, and 15 µm particles with a separation efficiency as high as 98.0% for 15 µm particles. By adding a DEP focusing region, a sheathless DEP separator was proposed, which offered higher throughputs (up to 10 times) at the cost of lowering the separation efficiency (a reduction up to 10.3% for 15 µm) compared to the sheath-assisted design. To enhance the separation efficiency, a combination of the DEP focusing accompanied by weak sheath flows from both sides was proposed. This design achieved the highest sample separation yield in the outlets (as high as 98.7% for 15 µm) with a sample throughput of more than 4.2 µL/min. This study provides insights into the choice of an appropriate platform for any application in which the yield, purity, throughput, and portability must be considered.

High Throughput Screening of Cell Mechanical Response Using a Stretchable 3D Cellular Microarray Platform.

Cells in vivo are constantly subjected to multiple microenvironmental mechanical stimuli that regulate cell function. Although 2D cell responses to the mechanical stimulation have been established, these methods lack relevance as physiological cell microenvironments are in 3D. Moreover, the existing platforms developed for studying the cell responses to mechanical cues in 3D either offer low-throughput, involve complex fabrication, or do not allow combinatorial analysis of multiple cues. Considering this, a stretchable high-throughput (HT) 3D cell microarray platform is presented that can apply dynamic mechanical strain to cells encapsulated in arrayed 3D microgels. The platform uses inkjet-bioprinting technique for printing cell-laden gelatin methacrylate (GelMA) microgel array on an elastic composite substrate that is periodically stretched. The developed platform is highly biocompatible and transfers the applied strain from the stretched substrate to the cells. The HT analysis is conducted to analyze cell mechano-responses throughout the printed microgel array. Also, the combinatorial analysis of distinct cell behaviors is conducted for different GelMA microenvironmental stiffnesses in addition to the dynamic stretch. Considering its throughput and flexibility, the developed platform can readily be scaled up to introduce a wide range of microenvironmental cues and to screen the cell responses in a HT way.

Sheath-assisted focusing of microparticles on lab-on-a-chip platforms.

Lab-on-a-chip (LOC) technologies can take advantage of sheath flows for particle/cell focusing before sensing or sorting. The integration of focusing with other microscale manipulation techniques (e.g., sorting) creates a trade-off between the throughput of the device and its performance. Therefore, exploring the effective parameters for cells/particles focusing enables us to improve the desired output of LOC devices. A common configuration for sheath-assisted focusing is Y junctions, which are parametrically studied in this paper. First, a computational model was developed and validated by comparing it with our experimental results. Using COMSOL Multiphysics modeling, the effects of multiple parameters were studied. These parameters include the sheath flow ratio (sheath flow over total flow), width ratio (width of the sheath inlet over the total width), junction angles, and particle size on the focusing width and the distribution of the particles within the focusing region. Then, the numerical data were used to develop two generalized linear models to predict the focusing width of the particles and the standard deviation of the position of the particles. The results showed that the focusing width is greatly impacted by the sheath flow rate ratio. Further, the standard deviation of the position of the particles, which represents the concentration of the particles, is mostly dependent on the flow rate ratio, width ratio, and particle size. Our results provide a better understanding of how the device geometrical and operational factors affect the position of the particles in the development of high-performance on-chip sensing and sorting of both cells and particles.

Nano-porous anodic alumina: fundamentals and applications in tissue engineering.

Recently, nanomaterials have been widely utilized in tissue engineering applications due to their unique properties such as the high surface to volume ratio and diversity of morphology and structure. However, most methods used for the fabrication of nanomaterials are rather complicated and costly. Among different nanomaterials, anodic aluminum oxide (AAO) is a great example of nanoporous structures that can easily be engineered by changing the electrolyte type, anodizing potential, current density, temperature, acid concentration and anodizing time. Nanoporous anodic alumina has often been used for mammalian cell culture, biofunctionalization, drug delivery, and biosensing by coating its surface with biocompatible materials. Despite its wide application in tissue engineering, thorough in vivo and in vitro studies of AAO are still required to enhance its biocompatibility and thereby pave the way for its application in tissue replacements. Recognizing this gap, this review article aims to highlight the biomedical potentials of AAO for applications in tissue replacements along with the mechanism of porous structure formation and pore characteristics in terms of fabrication parameters.

Graphene-Coated Spandex Sensors Embedded into Silicone Sheath for Composites Health Monitoring and Wearable Applications.

This study presents a low-cost, tunable, and stretchable sensor fabricated based on spandex (SpX) yarns coated with graphene nanoplatelets (GnP) through a dip-coating process. The SpX/GnP is wrapped into a stretchable silicone rubber (SR) sheath to protect the conductive layer against harsh conditions, which allows for fabricating washable wearable sensors. Dip-coating parameters are optimized to obtain the maximum GnP coating rate. The covering sheath is tailored to achieve high stretchability beyond the sensing limit of 104% for SpX/GnP/SR sensors. Adjustable sensitivity is attained by manipulating SpX immersion times broadening its application for a wide range of strains: Gauge factors as high as two orders of magnitude are achieved at tensile strains greater than ≈40%. The fabricated sensors are tested for two applications: First, the SpX/GnP sensors are integrated into composite fabrics (with no negative impact on the structural integrity of the part) for screening the yarn displacements, resin flow, solidification during the hot press forming process, and structural health monitoring under mechanical loads with minimal cross-sensitivity to temperature/humidity. Second, the capability of SpX/GnP/SP sensors in detection of a wide range of bodily motions (from the joint motion to arterial blood pressure) is demonstrated.

Label-Free Capacitive Biosensor for Detection of Cryptosporidium.

Cryptosporidium, an intestinal protozoan pathogen, is one of the leading causes of diarrhea in healthy adults and death in children. Detection of Cryptosporidium oocysts has become a high priority to prevent potential outbreaks. In this paper, a label-free interdigitated-based capacitive biosensor has been introduced for the detection of Cryptosporidium oocysts in water samples. Specific anti-Cryptosporidium monoclonal antibodies (IgG3) were covalently immobilized onto interdigitated gold electrodes as the capture probes, and bovine serum albumin was used to avoid non-specific adsorption. The immobilization of the antibodies was confirmed by measuring the change in the contact angle. The detection was achieved by measuring the relative change in the capacitive/dielectric properties due to the formation of Cryptosporidium-antibody complex. The biosensor has been tested for different concentrations of Cryptosporidium. The results show that the biosensor developed can accurately distinguish different numbers of captured cells and densities on the surface of the biosensor. The number of Cryptosporidium oocysts captured on the electrode surface was confirmed using a fluorescein isothiocyanate (FITC) immunofluorescence assay. The response from the developed biosensor has been mainly dependent on the concentration of Cryptosporidium under optimized conditions. The biosensor showed a linear detection range between 15 and 153 cells/mm² and a detection limit of 40 cells/mm². The label-free capacitive biosensor developed has a great potential for detecting Cryptosporidium in environmental water samples. Furthermore, under optimized conditions, this label-free biosensor can be extended for detection of other biomarkers for biomedical and environmental analyses.

A review of sorting, separation and isolation of cells and microbeads for biomedical applications: microfluidic approaches.

Several biomedical analyses are performed on particular types of cells present in body samples or using functionalized microparticles. Success in such analyses depends on the ability to separate or isolate the target cells or microparticles from the rest of the sample. In conventional procedures, multiple pieces of equipment, such as centrifuges, magnets, and macroscale filters, are used for such purposes, which are time-consuming, associated with human error, and require several operational steps. In the past two decades, there has been a tendency to develop microfluidic techniques, so-called lab-on-a-chip, to miniaturize and automate these procedures. The processes used for the separation and isolation of the cells and microparticles are scaled down into a small microfluidic chip, requiring very small amounts of sample. Differences in the physical and biological properties of the target cells from the other components present in the sample are the key to the development of such microfluidic techniques. These techniques are categorized as filtration-, hydrodynamic-, dielectrophoretic-, acoustic- and magnetic-based methods. Here we review the microfluidic techniques developed for sorting, separation, and isolation of cells and microparticles for biomedical applications. The mechanisms behind such techniques are thoroughly explained and the applications in which these techniques have been adopted are reviewed.

3D Printing-Based Integrated Water Quality Sensing System.

The online and accurate monitoring of drinking water supply networks is critically in demand to rapidly detect the accidental or deliberate contamination of drinking water. At present, miniaturized water quality monitoring sensors developed in the laboratories are usually tested under ambient pressure and steady-state flow conditions; however, in Water Distribution Systems (WDS), both the pressure and the flowrate fluctuate. In this paper, an interface is designed and fabricated using additive manufacturing or 3D printing technology-material extrusion (Trade Name: fused deposition modeling, FDM) and material jetting-to provide a conduit for miniaturized sensors for continuous online water quality monitoring. The interface is designed to meet two main criteria: low pressure at the inlet of the sensors and a low flowrate to minimize the water bled (i.e., leakage), despite varying pressure from WDS. To meet the above criteria, a two-dimensional computational fluid dynamics model was used to optimize the geometry of the channel. The 3D printed interface, with the embedded miniaturized pH and conductivity sensors, was then tested at different temperatures and flowrates. The results show that the response of the pH sensor is independent of the flowrate and temperature. As for the conductivity sensor, the flowrate and temperature affect only the readings at a very low conductivity (4 µS/cm) and high flowrates (30 mL/min), and a very high conductivity (460 µS/cm), respectively.

Laterally Confined Microfluidic Patterning of Cells for Engineering Spatially Defined Vascularization.

A biofabrication strategy for creating planar multiscale protein, hydrogel, and cellular patterns, and simultaneously generating microscale topographical features is developed that laterally confines the patterned cells and direct their growth in cell permissive hydrogels.

Microfluidics Integrated Biosensors: A Leading Technology towards Lab-on-a-Chip and Sensing Applications.

A biosensor can be defined as a compact analytical device or unit incorporating a biological or biologically derived sensitive recognition element immobilized on a physicochemical transducer to measure one or more analytes. Microfluidic systems, on the other hand, provide throughput processing, enhance transport for controlling the flow conditions, increase the mixing rate of different reagents, reduce sample and reagents volume (down to nanoliter), increase sensitivity of detection, and utilize the same platform for both sample preparation and detection. In view of these advantages, the integration of microfluidic and biosensor technologies provides the ability to merge chemical and biological components into a single platform and offers new opportunities for future biosensing applications including portability, disposability, real-time detection, unprecedented accuracies, and simultaneous analysis of different analytes in a single device. This review aims at representing advances and achievements in the field of microfluidic-based biosensing. The review also presents examples extracted from the literature to demonstrate the advantages of merging microfluidic and biosensing technologies and illustrate the versatility that such integration promises in the future biosensing for emerging areas of biological engineering, biomedical studies, point-of-care diagnostics, environmental monitoring, and precision agriculture.

High throughput microfluidics-based synthesis of PEGylated liposomes for precise size control and efficient drug encapsulation.

The low scalability and reproducibility of existing synthesis methods have hindered the translation of liposome nanoparticles as carriers for targeted drug delivery from conventional laboratory techniques to mass production. To this end, in this study, we present a high-throughput microfluidics-based approach for the synthesis of PEGylated liposomes with a primary focus on achieving precise size control and efficient encapsulation of hydrophobic drug molecules. In this platform, liposomes were self-assembled through a controllable mixing of lipids (EYPC, cholesterol, and DSPE-PEG 2000) dissolved in ethanol and an aqueous solution. The key parameters, including the chip design, total flow rate, flow rate ratio, lipid concentrations, as well as variations in buffer (HEPES and NaCl) and solvent composition (commercial and reagent-grade ethanol) were explored in detail. Through comprehensive parametric studies, we gained valuable insights into the influence of these variables on the size distribution of liposomes and succeeded in producing highly reproducible liposomes ranging from approximately 60 nm (corresponding to small unilamellar vesicles) to 150 nm (representing large unilamellar vesicles), all while maintaining a polydispersity index (PDI) of less than 0.2. To assess the encapsulation efficiency of hydrophobic drug molecules, Nile red (NR) was employed as a surrogate. We meticulously examined the impact of NR concentration on the drug encapsulation process, resulting in up to 74% drug encapsulation efficiency within the PEGylated liposomes. This research offers crucial advances in liposome synthesis and drug delivery, providing a high-throughput, controllable method for PEGylated liposomes with potential in pharmaceutical and biomedical fields.

Highly selective molecularly imprinted polymer nanoparticles (MIP NPs)-based microfluidic gas sensor for tetrahydrocannabinol (THC) detection.

A highly selective microfluidic integrated metal oxide gas sensor for THC detection is reported based on MIP nanoparticles (MIP NPs). We synthesized MIP NPs with THC recognition sites and coated them on a 3D-printed microfluidic channel surface. The sensitivity and selectivity of coated microfluidic integrated gas sensors were evaluated by exposure to THC, cannabidiol (CBD), methanol, and ethanol analytes in 300-700 ppm at 300 °C. For comparison, reference signals were obtained from a microfluidic channel coated with nonimprinted polymers (NIP NPs). The MIP and NIP NPs were characterized using scanning electron microscopy (SEM) and Raman spectroscopy. MIP and NIP NPs channels response data were combined and classified with 96.3% accuracy using the Fine KNN classification model in MATLAB R2021b Classification Learner App. Compared to the MIP NPs coated channel, the NIP NPs channel had poor selectivity towards THC, demonstrating that the THC recognition sites in the MIP structure enabled selective detection of THC. The findings demonstrated that the recognition sites of MIP NPs properly captured THC molecules, enabling the selective detection of THC compared to CBD, methanol, and ethanol.

Microplastic in situ detection based on a portable triboelectric microfluidic sensor.

Microplastics (MPs) are a size-based category of plastic pollutants between 1 µm to 5 mm in particle size that are ubiquitous in land and water resources due to anthropogenic activity. Current methodologies for MPs identification and characterization require laboratory instruments and specialized training. In the present study, a miniaturized microfluidic triboelectric sensor (M-TES) is proposed for the rapid detection of MPs from water samples. The viability and versatility of this device is illustrated for in situ measurement of the size and concentration of polystyrene (PS) micro-particles in water. The M-TES sensor relies on inducing and measuring electrical charges generated by microplastic flow within water droplets passing through a microfluidic channel. The experimental samples encompass pure polystyrene (PS) microparticles ranging from 500 nm to 10 µm, as well as actual samples collected from a coffee machine. The results reveal that the sensor's response exhibits a linear correlation with the increase in both microplastic size and concentration. The proposed sensing system could distinguish between microplastics of different sizes and concentrations. The results demonstrate the applicability of the M-TES in the field of sensors for environmental monitoring.

Microfluidic integrated gas sensors for smart analyte detection: a comprehensive review.

The utilization of gas sensors has the potential to enhance worker safety, mitigate environmental issues, and enable early diagnosis of chronic diseases. However, traditional sensors designed for such applications are often bulky, expensive, difficult to operate, and require large sample volumes. By employing microfluidic technology to miniaturize gas sensors, we can address these challenges and usher in a new era of gas sensors suitable for point-of-care and point-of-use applications. In this review paper, we systematically categorize microfluidic gas sensors according to their applications in safety, biomedical, and environmental contexts. Furthermore, we delve into the integration of various types of gas sensors, such as optical, chemical, and physical sensors, within microfluidic platforms, highlighting the resultant enhancements in performance within these domains.

Magnetic Levitation System Isolates and Purifies Airborne Viruses.

Detection of viable viruses in the air is critical in order to determine the level of risk associated with the airborne diffusion of viruses. Different methods have been developed for the isolation, purification, and detection of viable airborne viruses, but they require an extensive processing time and often present limitations including low physical efficiency (i.e., the amount of collected viruses), low biological efficiency (i.e., the number of viable viruses), or a combination of all. To mitigate such limitations, we have employed an efficient technique based on the magnetic levitation (Maglev) technique with a paramagnetic solution and successfully identified distinct variations in levitation and density characteristics among bacteria (Escherichia coli), phages (MS2), and human viruses (SARS-CoV-2 and influenza H1N1). Notably, the Maglev approach enabled a significant enrichment of viable airborne viruses in air samples. Furthermore, the enriched viruses obtained through Maglev exhibited high purity, rendering them suitable for direct utilization in subsequent analyses such as reverse transcription-polymerase chain reaction (RT-PCR) or colorimetric assays. The system is portable, easy to use, and cost-efficient and can potentially provide proactive surveillance data for monitoring future outbreaks of airborne infectious diseases and allow for the induction of various preventative and mitigative measures.

On-chip-based electrochemical biosensor for the sensitive and label-free detection of Cryptosporidium.

Cryptosporidium, an intestinal protozoan pathogen, is one of the leading causes of death in children and diarrhea in healthy adults. Detection of Cryptosporidium has become a high priority to prevent potential outbreaks. In this paper, a simple, easy to fabricate, and cost-effective on-chip-based electrochemical biosensor has been developed for the sensitive and label-free detection of Cryptosporidium oocysts in water samples. The sensor was fabricated using standard lithography using a mask with a 3-electrode design and modified by self-assembling a hybrid of a thiolated protein/G and the specific anti-Cryptosporidium monoclonal antibodies (IgG3). The electrochemical impedance spectroscopy (EIS) was employed to quantitate C. parvum in the range of 0 to 300 oocysts, with a detection limit of approximately 20 oocysts/5 µL. The high sensitivity and specificity of the developed label-free electrochemical biosensor suggest that this novel platform is a significant step towards the development of fast, real-time, inexpensive and label-free sensing tool for early warning and immediate on-site detection of C. parvum oocysts in water samples, as compared to the traditional methods (such as PCR and microscopy). Furthermore, under optimized conditions, this label-free biosensor can be extended to detect other analytes and biomarkers for environmental and biomedical analyses.