Macrophage Activation in the Dorsal Root Ganglion in Rats Developing Autotomy after Peripheral Nerve Injury.

Autotomy, self-mutilation of a denervated limb, is common in animals after peripheral nerve injury (PNI) and is a reliable proxy for neuropathic pain in humans. Understanding the occurrence and treatment of autotomy remains challenging. The objective of this study was to investigate the occurrence of autotomy in nude and Wistar rats and evaluate the differences in macrophage activation and fiber sensitization contributing to the understanding of autotomy behavior. Autotomy in nude and Wistar rats was observed and evaluated 6 and 12 weeks after sciatic nerve repair surgery. The numbers of macrophages and the types of neurons in the dorsal root ganglion (DRG) between the two groups were compared by immunofluorescence studies. Immunostaining of T cells in the DRG was also assessed. Nude rats engaged in autotomy with less frequency than Wistar rats. Autotomy symptoms were also relatively less severe in nude rats. Immunofluorescence studies revealed increased macrophage accumulation and activation in the DRG of Wistar rats. The percentage of NF200+ neurons was higher at 6 and 12 weeks in Wistar rats compared to nude rats, but the percentage of CGRP+ neurons did not differ between two groups. Additionally, macrophages were concentrated around NF200-labeled A fibers. At 6 and 12 weeks following PNI, CD4+ T cells were not found in the DRG of the two groups. The accumulation and activation of macrophages in the DRG may account for the increased frequency and severity of autotomy in Wistar rats. Our results also suggest that A fiber neurons in the DRG play an important role in autotomy.

Acoustofluidic particle trapping, manipulation, and release using dynamic-mode cantilever sensors.

We show here that dynamic-mode cantilever sensors enable acoustofluidic fluid mixing and trapping of suspended particles as well as the rapid manipulation and release of trapped micro-particles via mode switching in liquid. Resonant modes of piezoelectric cantilever sensors over the 0 to 8 MHz frequency range are investigated. Sensor impedance response, flow visualization studies using dye and micro-particle tracers (100 µm diameter), and finite element simulations of cantilever modal mechanics and acoustic streaming show fluid mixing and particle trapping configurations depend on the resonant mode shape. We found trapped particles could be: (1) rapidly manipulated on millimeter length scales, and (2) released from the cantilever surface after trapping by switching between low- and high-order resonant modes (less than 250 kHz and greater than 1 MHz, respectively). Such results suggest a potentially promising future for dynamic-mode cantilevers in separations, pumping and mixing applications as well as acoustofluidic-enhanced sensing applications.

3D Printed Programmable Release Capsules.

The development of methods for achieving precise spatiotemporal control over chemical and biomolecular gradients could enable significant advances in areas such as synthetic tissue engineering, biotic-abiotic interfaces, and bionanotechnology. Living organisms guide tissue development through highly orchestrated gradients of biomolecules that direct cell growth, migration, and differentiation. While numerous methods have been developed to manipulate and implement biomolecular gradients, integrating gradients into multiplexed, three-dimensional (3D) matrices remains a critical challenge. Here we present a method to 3D print stimuli-responsive core/shell capsules for programmable release of multiplexed gradients within hydrogel matrices. These capsules are composed of an aqueous core, which can be formulated to maintain the activity of payload biomolecules, and a poly(lactic-co-glycolic) acid (PLGA, an FDA approved polymer) shell. Importantly, the shell can be loaded with plasmonic gold nanorods (AuNRs), which permits selective rupturing of the capsule when irradiated with a laser wavelength specifically determined by the lengths of the nanorods. This precise control over space, time, and selectivity allows for the ability to pattern 2D and 3D multiplexed arrays of enzyme-loaded capsules along with tunable laser-triggered rupture and release of active enzymes into a hydrogel ambient. The advantages of this 3D printing-based method include (1) highly monodisperse capsules, (2) efficient encapsulation of biomolecular payloads, (3) precise spatial patterning of capsule arrays, (4) "on the fly" programmable reconfiguration of gradients, and (5) versatility for incorporation in hierarchical architectures. Indeed, 3D printing of programmable release capsules may represent a powerful new tool to enable spatiotemporal control over biomolecular gradients.

3D Printed Anatomical Nerve Regeneration Pathways.

An imaging-coupled 3D printing methodology for the design, optimization, and fabrication of a customized nerve repair technology for complex injuries is presented. The custom scaffolds are deterministically fabricated via a microextrusion printing principle which enables the simultaneous incorporation of anatomical geometries, biomimetic physical cues, and spatially controlled biochemical gradients in a one-pot 3D manufacturing approach.

3D printed quantum dot light-emitting diodes.

Developing the ability to 3D print various classes of materials possessing distinct properties could enable the freeform generation of active electronics in unique functional, interwoven architectures. Achieving seamless integration of diverse materials with 3D printing is a significant challenge that requires overcoming discrepancies in material properties in addition to ensuring that all the materials are compatible with the 3D printing process. To date, 3D printing has been limited to specific plastics, passive conductors, and a few biological materials. Here, we show that diverse classes of materials can be 3D printed and fully integrated into device components with active properties. Specifically, we demonstrate the seamless interweaving of five different materials, including (1) emissive semiconducting inorganic nanoparticles, (2) an elastomeric matrix, (3) organic polymers as charge transport layers, (4) solid and liquid metal leads, and (5) a UV-adhesive transparent substrate layer. As a proof of concept for demonstrating the integrated functionality of these materials, we 3D printed quantum dot-based light-emitting diodes (QD-LEDs) that exhibit pure and tunable color emission properties. By further incorporating the 3D scanning of surface topologies, we demonstrate the ability to conformally print devices onto curvilinear surfaces, such as contact lenses. Finally, we show that novel architectures that are not easily accessed using standard microfabrication techniques can be constructed, by 3D printing a 2 × 2 × 2 cube of encapsulated LEDs, in which every component of the cube and electronics are 3D printed. Overall, these results suggest that 3D printing is more versatile than has been demonstrated to date and is capable of integrating many distinct classes of materials.

Biosensor-based microRNA detection: techniques, design, performance, and challenges.

The current state of biosensor-based techniques for amplification-free microRNA (miRNA) detection is critically reviewed. Comparison with non-sensor and amplification-based molecular techniques (MTs), such as polymerase-based methods, is made in terms of transduction mechanism, associated protocol, and sensitivity. Challenges associated with miRNA hybridization thermodynamics which affect assay selectivity and amplification bias are briefly discussed. Electrochemical, electromechanical, and optical classes of miRNA biosensors are reviewed in terms of transduction mechanism, limit of detection (LOD), time-to-results (TTR), multiplexing potential, and measurement robustness. Current trends suggest that biosensor-based techniques (BTs) for miRNA assay will complement MTs due to the advantages of amplification-free detection, LOD being femtomolar (fM)-attomolar (aM), short TTR, multiplexing capability, and minimal sample preparation requirement. Areas of future importance in miRNA BT development are presented which include focus on achieving high measurement confidence and multiplexing capabilities.

Reduction of nonspecific protein adsorption on cantilever biosensors caused by transverse resonant mode vibration.

We examine if vibration of millimeter-sized cantilever sensors can release nonspecifically adsorbed proteins. Integrated electrochemical and mass-change measurement as well as fluorescence assays showed transverse surface vibration released nonspecifically bound proteins in samples prepared at 0.2-3.6 mg bovine serum albumin (BSA) per mL. Extent of release was directly related to magnitude of excitation voltage (Vex) applied to the self-actuating lead zirconate titanate (PZT) cantilever over three log units (0, 10 mV, 100 mV, and 1 V). Vibration-induced release was not instantaneous, but had an apparent first-order rate constant (kapp) which ranged from 0.02-0.1 min(-1). Results suggest significant serum albumin protein release could be achieved using excitation voltages of 1 V in millimeter-sized cantilever sensors. Complementary experiments with thiolated DNA, which binds to surface gold 〈111〉 sites with ∼ four times higher binding energy than BSA, showed negligible release under the same vibration magnitude. The results of the study suggest a direct correlation between surface-adsorbate binding energy and the effectiveness of vibration-induced release. We suggest that the release mechanism includes contributions from surface strain energy, body force, and acoustic streaming-associated hydrodynamic effects. The primary contribution of this study suggests that surface vibration of cantilever sensors may be useful in reducing nonspecific adsorption, especially for biosensing of analytes present in a complex background.

Imbalanced spectral data analysis using data augmentation based on the generative adversarial network.

Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier's F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method's broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering.

Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning.

False results and time delay are longstanding challenges in biosensing. While classification models and deep learning may provide new opportunities for improving biosensor performance, such as measurement confidence and speed, it remains a challenge to ensure that predictions are explainable and consistent with domain knowledge. Here, we show that consistency of deep learning classification model predictions with domain knowledge in biosensing can be achieved by cost function supervision and enables rapid and accurate biosensing using the biosensor dynamic response. The impact and utility of the methodology were validated by rapid and accurate quantification of microRNA (let-7a) across the nanomolar (nM) to femtomolar (fM) concentration range using the dynamic response of cantilever biosensors. Data augmentation and cost function supervision based on the consistency of model predictions and experimental observations with the theory of surface-based biosensors improved the F1 score, precision, and recall of a recurrent neural network (RNN) classifier by an average of 13.8%. The theory-guided RNN (TGRNN) classifier enabled quantification of target analyte concentration and false results with an average prediction accuracy, precision, and recall of 98.5% using the initial transient or entire dynamic response, which is indicative of high prediction accuracy and low probability of false-negative and false-positive results. Classification scores were used to establish new relationships among biosensor performance characteristics (e.g., measurement confidence) and design parameters (e.g., inputs and hyperparameters of classification models and data acquisition parameters) that may be used for characterizing biosensor performance.

Synthesis and real-time characterization of self-healing, injectable, fast-gelling hydrogels based on alginate multi-reducing end polysaccharides (MREPs).

Polysaccharide-based hydrogels are promising for many biomedical applications including drug delivery, wound healing, and tissue engineering. We illustrate herein self-healing, injectable, fast-gelling hydrogels prepared from multi-reducing end polysaccharides, recently introduced by the Edgar group. Simple condensation of reducing ends from multi-reducing end alginate (M-Alg) with amines from polyethylene imine (PEI) in water affords a dynamic, hydrophilic polysaccharide network. Trace amounts of acetic acid can accelerate the gelation time from hours to seconds. The fast-gelation behavior is driven by rapid Schiff base formation and strong ionic interactions induced by acetic acid. A cantilever rheometer enables real-time monitoring of changes in viscoelastic properties during hydrogel formation. The reversible nature of these crosslinks (imine bonds, ionic interactions) provides a hydrogel with low toxicity in cell studies as well as self-healing and injectable properties. Therefore, the self-healing, injectable, and fast-gelling M-Alg/PEI hydrogel holds substantial promise for biomedical, agricultural, controlled release, and other applications.

Torsional and lateral resonant modes of cantilevers as biosensors: alternatives to bending modes.

We show here novel cantilever designs that express torsional and lateral modes exhibit excellent mass-change sensitivity to molecular self-assembly on gold (75-135 fg/Hz) which is superior to that of widely investigated bending modes. Lead zirconate titanate (PZT) millimeter-sized cantilevers were designed with two types of anchor asymmetry that induced expression of either torsional or lateral modes in the 0-80 kHz frequency range. Experiments and supporting calculations show that anchor asymmetry enables resonant mode impedance-coupling. The sensitive torsional and lateral modes enabled measurement of self-assembled monolayer formation rate at picomolar levels. The anchor design principle was extended to microcantilevers via finite element simulations, which caused both 97% sensitivity improvement relative to conventional designs, as well as new nonclassical resonant mode shapes.

Electrochemical piezoelectric-excited millimeter-sized cantilever (ePEMC) for simultaneous dual transduction biosensing.

A dual mode electrochemical piezoelectric-excited millimeter cantilever (ePEMC) sensor is reported for simultaneous in-liquid biochemical sensing. The ePEMC incorporates mass-sensing measurement of dynamic-mode cantilevers with electrochemical impedance spectroscopy (EIS) commonly employed for transduction in sensitive electrochemical biosensors. Such an integrated design allows for simultaneous and continuous measurement of resonant frequency shift (Δf) and charge transfer resistance (RCT) as a target analyte binds to the sensor gold surface (0.5 mm(2)) via electromechanical and electrochemical impedance spectroscopy, respectively. The properties of ePEMC are demonstrated in three experiments: (1) resonant frequency response to electrochemically-deposited metal thin-films, (2) resonant frequency response to adsorption of thiolated ssDNA and model proteins with subsequent EIS sensing, and (3) simultaneous resonant frequency and charge transfer resistance response to model chemisorption of a short-chain thiol molecule, mercaptohexanol. Adsorption of all model binding analytes caused decrease in sensor resonant frequency and increase in charge transfer resistance. Comparison of sensor response to binding of protein and thiol molecules showed the two simultaneously transduced signals were proportional and showed the same kinetics.

A cantilever biosensor-based assay for toxin-producing cyanobacteria Microcystis aeruginosa using 16S rRNA.

Monitoring of cyanotoxins in source waters is currently done through toxin-targeting assays which suffer from low sensitivity due to poor antibody avidity. We present a biosensor-based method as an alternative for detecting toxin-producing cyanobacteria M. aeruginosa via species-selective region of 16S rRNA at concentrations as low as 50 cells/mL, and over a five-log dynamic range. The cantilever biosensor was immobilized with a 27-base DNA strand that is complementary to the target variable region of 16S rRNA of M. aeruginosa. The cantilever sensor detects mass-changes through shifts in its resonant frequency. Increase in the biosensor's effective mass, caused by hybridization of target strand with the biosensor-immobilized complementary strand, showed consistent and proportional frequency shift to M. aeruginosa concentrations. The sensor hybridization response was verified in situ by two techniques: (a) presence of duplex DNA structure postdetection via fluorescence measurements, and (b) secondary hybridization of nanogold-labeled DNA strands to the captured 16S rRNA strands. The biosensor-based assay, conducted in a flow format (∼ 0.5 mL/min), is relatively short, and requires a postextraction analysis time of less than two hours. The two-step detection protocol (primary and secondary hybridization) is less prone to false negatives, and the technique as a whole can potentially provide an early warning for toxin presence in source waters.

Accelerated Engineering of Optimized Functional Composite Hydrogels via High-Throughput Experimentation.

The Materials Genome Initiative (MGI) seeks to accelerate the discovery and engineering of advanced materials via high-throughput experimentation (HTE), which is a challenging task, given the common trade-off between design for optimal processability vs performance. Here, we report a HTE method based on automated formulation, synthesis, and multiproperty characterization of bulk soft materials in well plate formats that enables accelerated engineering of functional composite hydrogels with optimized properties for processability and performance. The method facilitates rapid high-throughput screening of hydrogel composition-property relations for multiple properties in well plate formats. The feasibility and utility of the method were demonstrated by application to several functional composite hydrogel systems, including alginate/poly(N-isopropylacrylamide) (PNIPAM) and poly(ethylene glycol) dimethacrylate (PEGDMA)/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) hydrogels. The HTE method was leveraged to identify formulations of conductive PEGDMA/PEDOT:PSS composite hydrogels for optimized performance and processability in three-dimensional (3D) printing. This work provides an advance in experimental methods based on automated dispensing, mixing, and sensing for the accelerated engineering of soft functional materials.

Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning.

Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensing can complement and improve the biosensor accuracy and speed relative to the performance achieved by traditional regression analysis of a standard curve based on the biosensor steady-state response. The methodology was validated by rapid and accurate quantification of microRNA across the nanomolar to femtomolar range using the dynamic response of cantilever biosensors. Theory-guided feature engineering improved the performance and efficiency of several classification models relative to the performance achieved using traditional feature engineering methods (TSFRESH). In addition to the entire dynamic response, the technique enabled rapid and accurate quantification of the target analyte concentration and false-positive and false-negative results using the initial transient response, thereby reducing the required data acquisition time (i.e., time delay). We show that model explainability can be achieved by combining theory-guided feature engineering and feature importance analysis. The performance of multiple classifiers using both TSFRESH- and theory-based features from the biosensor's initial transient response was similar to that achieved using the entire dynamic response with data augmentation. We also show that the methodology can guide design of experiments for high-performance biosensing applications, specifically, the selection of data acquisition parameters (e.g., time) based on potential application-dependent performance thresholds. This work provides an example of the opportunities for improving biosensor performance, such as reducing biosensor false results and time delay, using explainable machine learning models supervised by domain knowledge in biosensing.

Sample preparation-free, real-time detection of microRNA in human serum using piezoelectric cantilever biosensors at attomole level.

A sensitive, selective, sample preparation-free method for near real-time detection of microRNA in buffer and human serum is given using gold (Au)-coated dynamic piezoelectric cantilever sensors. Sensor response to thiolated DNA probe chemisorption, hsa-let-7a hybridization, labeled-DNA hybridization, and Au nanoparticle-functionalized DNA hybridization was monitored continuously in flowing liquid samples using custom flow-cells. The assay showed successful detection of target let-7a with a dynamic range spanning 6 orders of magnitude (10 fM-1 nM) with a limit of detection of less than 10 attomoles (∼4 fM). The serum background had negligible effect on sensitivity relative to the results obtained in the buffer due to reduction in nonspecific binding caused by continuous sensor vibration. Both hybridization and nonspecific binding reduction were confirmed using fluorescence-based assays to support sensor-based results. The sensor-based method demonstrated excellent selectivity for the microRNA target in comparison with similar microRNA differing by only a single nucleotide (hsa-let-7c) and random microRNA sequences. Au nanoparticle-based amplification of sensor response was investigated and led to an order of magnitude improvement in the detection limit and a 128% amplification of sensor response over the entire dynamic range. Au nanoparticle amplification was verified by scanning electron microscopy. The cantilever sensor-based microRNA assay provides competitive sensitivity with current microRNA detection methods and has the advantage of requiring no sample preparation, even when working with biological samples that contain a complex background.

pH effect on protein G orientation on gold surfaces and characterization of adsorption thermodynamics.

The pH effect on adsorbed antibody-binding protein (protein G) orientation on gold (Au) and its adsorption thermodynamic characteristics were investigated using quartz crystal microbalance (QCM) and X-ray photoelectron spectroscopy (XPS). The adsorbed protein G orientation was measured by binding response of two antibody-antigen systems: the model bovine serum albumin (BSA) and the foodborne pathogen E. coli O157:H7. Surface coverage was not significantly affected by pH, but its orientation was. The most properly oriented protein G for antibody binding was achieved at near-neutral pH. Adsorption was verified by XPS measurements using nitrogen (N) 1s, oxygen (O) 1s, and Au 4p peak heights. Adsorption energetics were determined by van't Hoff and Langmuir kinetic analyses of adsorption data obtained at 296, 303, and 308 K. Large characteristic entropy change of protein adsorption was observed (ΔS° = 0.52 ± 0.01 kcal/mol·K). The adsorption process was not classical physisorption but exhibited chemisorption characteristics based on significant enthalpy change (ΔH° = -25 ± 6 kcal/mol).

A sweet solution to complex microprinting.

A simple sugar mixture transfers functional components to surfaces with intricate geometry.

Closed-Loop Controlled Photopolymerization of Hydrogels.

Here, we present a closed-loop controlled photopolymerization process for fabrication of hydrogels with controlled storage moduli. Hydrogel crosslinking was associated with a significant change in the phase angle of a piezoelectric cantilever sensor and established the timescale of the photopolymerization process. The composition, structure, and mechanical properties of the fabricated hydrogels were characterized using Raman spectroscopy, scanning electron microscopy (SEM), and dynamic mechanical analysis (DMA). We found that the storage moduli of photocured poly(ethylene glycol) dimethacrylate (PEGDMA) and poly(N-isopropylacrylamide) (PNIPAm) hydrogels could be controlled using bang-bang and fuzzy logic controllers. Bang-bang controlled photopolymerization resulted in constant overshoot of the storage modulus setpoint for PEGDMA hydrogels, which was mitigated by setpoint correction and fuzzy logic control. SEM and DMA studies showed that the network structure and storage modulus of PEGDMA hydrogels were dependent on the cure time and temporal profile of UV exposure during photopolymerization. This work provides an advance in pulsed and continuous photopolymerization processes for hydrogel engineering based on closed-loop control that enables reproducible fabrication of hydrogels with controlled mechanical properties.