A pro-reparative bioelectronic device for controlled delivery of ions and biomolecules.

Wound healing is a complex physiological process that requires precise control and modulation of many parameters. Therapeutic ion and biomolecule delivery has the capability to regulate the wound healing process beneficially. However, achieving controlled delivery through a compact device with the ability to deliver multiple therapeutic species can be a challenge. Bioelectronic devices have emerged as a promising approach for therapeutic delivery. Here, we present a pro-reparative bioelectronic device designed to deliver ions and biomolecules for wound healing applications. The device incorporates ion pumps for the targeted delivery of H+ and zolmitriptan to the wound site. In vivo studies using a mouse model further validated the device's potential for modulating the wound environment via H+ delivery that decreased M1/M2 macrophage ratios. Overall, this bioelectronic ion pump demonstrates potential for accelerating wound healing via targeted and controlled delivery of therapeutic agents to wounds. Continued optimization and development of this device could not only lead to significant advancements in tissue repair and wound healing strategies but also reveal new physiological information about the dynamic wound environment.

Adjusting Some Properties of Poly(methacrylic acid) (Nano)Composite Hydrogels by Means of Silicon-Containing Inorganic Fillers.

The present work aims to show how the main properties of poly(methacrylic acid) (PMAA) hydrogels can be engineered by means of several silicon-based fillers (Laponite XLS/XLG, montmorillonite (Mt), pyrogenic silica (PS)) employed at 10 wt% concentration based on MAA. Various techniques (FT-IR, XRD, TGA, SEM, TEM, DLS, rheological measurements, UV-VIS) were used to comparatively study the effect of these fillers, in correlation with their characteristics, upon the structure and swelling, viscoelastic, and water decontamination properties of (nano)composite hydrogels. The experiments demonstrated that the nanocomposite hydrogel morphology was dictated by the way the filler particles dispersed in water. The equilibrium swelling degree (SDe) depended on both the pH of the environment and the filler nature. At pH 1.2, a slight crosslinking effect of the fillers was evidenced, increasing in the order Mt < Laponite < PS. At pH > pKaMAA (pH 5.4; 7.4; 9.5), the Laponite/Mt-containing hydrogels displayed a higher SDe as compared to the neat one, while at pH 7.4/9.5 the PS-filled hydrogels surprisingly displayed the highest SDe. Rheological measurements on as-prepared hydrogels showed that the filler addition improved the mechanical properties. After equilibrium swelling at pH 5.4, G' and G" depended on the filler, the Laponite-reinforced hydrogels proving to be the strongest. The (nano)composite hydrogels synthesized displayed filler-dependent absorption properties of two cationic dyes used as model water pollutants, Laponite XLS-reinforced hydrogel demonstrating both the highest absorption rate and absorption capacity. Besides wastewater purification, the (nano)composite hydrogels described here may also find applications in the pharmaceutical field as devices for the controlled release of drugs.

A Microfluidic Ion Sensor Array.

A balanced concentration of ions is essential for biological processes to occur. For example, [H+ ] gradients power adenosine triphosphate synthesis, dynamic changes in [K+ ] and [Na+ ] create action potentials in neuronal communication, and [Cl- ] contributes to maintaining appropriate cell membrane voltage. Sensing ionic concentration is thus important for monitoring and regulating many biological processes. This work demonstrates an ion-selective microelectrode array that simultaneously and independently senses [K+ ], [Na+ ], and [Cl- ] in electrolyte solutions. To obtain ion specificity, the required ion-selective membranes are patterned using microfluidics. As a proof of concept, the change in ionic concentration is monitored during cell proliferation in a cell culture medium. This microelectrode array can easily be integrated in lab-on-a-chip approaches to physiology and biological research and applications.

Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function.

Due to the complexity of neuronal networks and the nonlinear dynamics of individual neurons, it is challenging to develop a systems-level model which is accurate enough to be useful yet tractable enough to apply. Mean-field models which extrapolate from single-neuron descriptions to large-scale models can be derived from the neuron's transfer function, which gives its firing rate as a function of its synaptic input. However, analytically derived transfer functions are applicable only to the neurons and noise models from which they were originally derived. In recent work, approximate transfer functions have been empirically derived by fitting a sigmoidal curve, which imposes a maximum firing rate and applies only in the diffusion limit, restricting applications. In this paper, we propose an approximate transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. Refractory SoftPlus activation functions allow the derivation of simple empirically approximated mean-field models using simulation results, which enables prediction of the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. These models also support an accurate approximate bifurcation analysis as a function of the level of recurrent input. Finally, the model works without assuming large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.

Model-agnostic neural mean field with a data-driven transfer function.

As one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable way to bridge the gap between single-neuron and population-level descriptions of neuronal activity, by modeling the behavior of a single representative neuron and extending this to the population. However, existing neural mean-field methods typically either take the limit of small interaction sizes, or are applicable only to the specific neuron models for which they were derived. This paper derives a mean-field model by fitting a transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. The transfer function is fitted numerically to simulated spike time data, and is entirely agnostic to the underlying neuronal dynamics. The resulting mean-field model predicts the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. Furthermore, it enables an accurate approximate bifurcation analysis as a function of the level of recurrent input. This model does not assume large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.

Gamifying cell culture training: The 'Seru-Otchi' experience for undergraduates.

Working in a stem cell laboratory necessitates a thorough understanding of complex cell culture protocols, the operation of sensitive scientific equipment, adherence to safety standards, and general laboratory etiquette. For novice student researchers, acquiring the necessary specialized knowledge before their initial laboratory experience can be a formidable task. Similarly, for experienced laboratory personnel, efficiently and uniformly training new trainees to a rigorous standard presents a significant challenge. In response to these issues, we have developed an educational and interactive virtual cell culture environment. This interactive virtual lab aims to equip students with foundational knowledge in maintaining cortical brain organoids and to instill an understanding of pertinent safety procedures and laboratory etiquette. The gamification of this training process seeks to provide laboratory supervisors in highly specialized fields with an effective tool to integrate students into their work environments more rapidly and safely.

Reducing education inequalities through cloud-enabled live-cell biotechnology.

Biotechnology holds the potential to drive innovations across various fields from agriculture to medicine. However, despite numerous interventions, biotechnology education remains highly unequal worldwide. Historically, the high costs and potential exposure to hazardous materials have hindered biotechnology education. Integration of cloud technologies into classrooms has emerged as an alternative solution that is already enabling biotechnology experiments to reach thousands of students globally. We describe several innovations that collectively facilitate real-time experimentation in biotechnology education in remote locations. These advances enable remote access to scientific data and live experiments, promote collaborative research, and ensure educational inclusivity. We propose cloud-enabled live-cell biotechnology as a mechanism for reducing inequalities in biotechnology education and promoting sustainable development.

Experimental Investigations on Shear Thickening Fluids as "Liquid Body Armors": Non-Conventional Formulations for Ballistic Protection.

Shear thickening fluids (STFs) have garnered attention as potential enhancers of protective capabilities and for the optimization of Kevlar® armor design. To assess the possible shear thickening properties and potential application in ballistic protection, ten formulations were developed by employing polyethylene glycol (PEG) or polypropylene glycol (PPG), along with fumed silica or Aerosil HDK®. Rheological characterization facilitated the identification of formulations displaying shear thickening behavior. The potential integration of the selected shear thickening fluids (STFs) into Kevlar®-based composites was investigated by studying the impact resistance of Kevlar® soft armor structures. Also, high-velocity impact testing revealed that the distance between aramid layers plays a crucial role in the impact resistance effectiveness of Kevlar®-STF composite structures and that there is a very narrow domain between optimal and undesired scenarios in which STF could facilitate the penetration of Kevlar. The introduction of STF between the Kevlar sheets disrupted this packing and the energy absorption capacity of the material was not improved. Only one formulation (PEG400, Aerosil 27 wt.%) led to a less profound traumatic imprint and stopped the bullet when it was placed between layers no.1 and no.2 from a total of 11 layers of Kevlar XP. These experimental findings align with the modeling and simulation of Kevlar®-STF composites using Ansys simulation software (Ansys® AutoDyn 2022 R2).

SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Delivering biochemicals with precision using bioelectronic devices enhanced with feedback control.

Precision medicine endeavors to personalize treatments, considering individual variations in patient responses based on factors like genetic mutations, age, and diet. Integrating this approach dynamically, bioelectronics equipped with real-time sensing and intelligent actuation present a promising avenue. Devices such as ion pumps hold potential for precise therapeutic drug delivery, a pivotal aspect of effective precision medicine. However, implementing bioelectronic devices in precision medicine encounters formidable challenges. Variability in device performance due to fabrication inconsistencies and operational limitations, including voltage saturation, presents significant hurdles. To address this, closed-loop control with adaptive capabilities and explicit handling of saturation becomes imperative. Our research introduces an enhanced sliding mode controller capable of managing saturation, adept at satisfactory control actions amidst model uncertainties. To evaluate the controller's effectiveness, we conducted in silico experiments using an extended mathematical model of the proton pump. Subsequently, we compared the performance of our developed controller with classical Proportional Integral Derivative (PID) and machine learning (ML)-based controllers. Furthermore, in vitro experiments assessed the controller's efficacy using various reference signals for controlled Fluoxetine delivery. These experiments showcased consistent performance across diverse input signals, maintaining the current value near the reference with a relative error of less than 7% in all trials. Our findings underscore the potential of the developed controller to address challenges in bioelectronic device implementation, offering reliable precision in drug delivery strategies within the realm of precision medicine.

Internet-enabled lab-on-a-chip technology for education.

Despite many interventions, science education remains highly inequitable throughout the world. Internet-enabled experimental learning has the potential to reach underserved communities and increase the diversity of the scientific workforce. Here, we demonstrate the use of lab-on-a-chip (LoC) technologies to expose Latinx life science undergraduate students to introductory concepts of computer programming by taking advantage of open-loop cloud-integrated LoCs. We developed a context-aware curriculum to train students at over 8000 km from the experimental site. Through this curriculum, the students completed an assignment testing bacteria contamination in water using LoCs. We showed that this approach was sufficient to reduce the students' fear of programming and increase their interest in continuing careers with a computer science component. Altogether, we conclude that LoC-based internet-enabled learning can become a powerful tool to train Latinx students and increase the diversity in STEM.

Preparation and functionalization of cellulose nanofibers using a naturally occurring acid and their application in stabilizing linseed oil/water Pickering emulsions.

Finding efficient and environmental-friendly methods to produce and chemically modify cellulose nanofibers (CNFs) remains a challenge. In this study, lactic acid (LA) treatment followed by microfluidization was employed for the isolation and functionalization of CNFs. Small amounts of HCl (0.01, 0.1, and 0.2 M) were used alongside LA to intensify cellulose hydrolysis. FTIR spectroscopy and solid-state 13C NMR confirmed the successful functionalization of CNFs with lactyl groups during isolation, while SEM, AFM, and rheological tests revealed that the addition of HCl governed the fibers' sizes and morphology. Notably, the treatment with LA and 0.2 M HCl resulted in a more efficient defibrillation, yielding smaller nanofibers sizes (62 nm) as compared to the treatment with LA or HCl alone (90 and 108 nm, respectively). The aqueous suspension of CNFs treated with LA and 0.2 M HCl showed the highest viscosity and storage modulus. LA-modified CNFs were tested as stabilizers for linseed oil/water (50/50 v/v) emulsions. Owing to the lactyl groups grafted on their surface and higher aspect ratio, CNFs produced with 0.1 and 0.2 M HCl led to emulsions with increased stability (a creaming index increase of only 3 % and 1 %, respectively, in 30 days) and smaller droplets sizes of 23.4 ± 1.2 and 35.5 ± 0.5 µm, respectively. The results showed that LA-modified CNFs are promising stabilizers for Pickering emulsions.

A feedback-driven IoT microfluidic, electrophysiology, and imaging platform for brain organoid studies.

The analysis of tissue cultures, particularly brain organoids, takes a high degree of coordination, measurement, and monitoring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Unified by an Internet of Things (IoT) architecture, our approach enables continuous, communicative interactions among various sensing and actuation devices, achieving precisely timed control of in vitro biological experiments. The framework integrates microfluidics, electrophysiology, and imaging devices to maintain cerebral cortex organoids and monitor their neuronal activity. The organoids are cultured in custom, 3D-printed chambers attached to commercial microelectrode arrays for electrophysiology monitoring. Periodic feeding is achieved using programmable microfluidic pumps. We developed computer vision fluid volume estimations of aspirated media, achieving high accuracy, and used feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a 7-day study of mouse cerebral cortex organoids, comparing manual and automated protocols. The automated experimental samples maintained robust neural activity throughout the experiment, comparable with the control samples. The automated system enabled hourly electrophysiology recordings that revealed dramatic temporal changes in neuron firing rates not observed in once-a-day recordings.

Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters.

Precise modulation of brain activity is fundamental for the proper establishment and maturation of the cerebral cortex. To this end, cortical organoids are promising tools to study circuit formation and the underpinnings of neurodevelopmental disease. However, the ability to manipulate neuronal activity with high temporal resolution in brain organoids remains limited. To overcome this challenge, we introduce a bioelectronic approach to control cortical organoid activity with the selective delivery of ions and neurotransmitters. Using this approach, we sequentially increased and decreased neuronal activity in brain organoids with the bioelectronic delivery of potassium ions (K+) and γ-aminobutyric acid (GABA), respectively, while simultaneously monitoring network activity. This works highlights bioelectronic ion pumps as tools for high-resolution temporal control of brain organoid activity toward precise pharmacological studies that can improve our understanding of neuronal function.

A bioelectronic device for electric field treatment of wounds reduces inflammation in an in vivo mouse model.

Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

A computer vision based optical method for measuring fluid level in cell culture plates.

For a transparent well with a known volume capacity, changes in fluid level result in predictable changes in magnification of an overhead light source. For a given well size and fluid, the relationship between volume and magnification can be calculated if the fluid's index of refraction is known or in a naive fashion with a calibration procedure. Light source magnification can be measured through a camera and processed using computer vision contour analysis with OpenCV. This principle was applied in the design of a 3D printable sensing device using a raspberry pi zero and a camera.

Three-Dimensionally Printed Self-Lock Origami: Design, Fabrication, and Simulation to Improve Performance of Rotational Joint.

Origami structures have made significant contributions to the field of robotics, offering various advantages. One such advantage is their ability to conserve space by transforming the structure into a compact form. Additionally, many origami structures can be fabricated in a flat state to simplify manufacturing, giving them the potential for large-scale and cost-effective production. Rotational joints play a crucial role in the construction of robotic systems, yet origami rotational joints can suffer from a limited range of motion. We previously theoretically proposed the Self-Lock Joint to address this issue, but it is only partially flat-foldable. This paper presents a novel approach to the 3D printing of modular origami joints, such as the Self-Lock Joint, using 3D-printed plates joined with a fabric layer. The compliance of the fabric can improve the joint's semi flat-foldability or even enable it to achieve complete flat-foldability. Furthermore, the rotational motion of the joint is enhanced, allowing for close to 360 degrees of rotational movement. We assess the physical properties of the joint under both loaded and unloaded conditions in order to identify design trade-offs in the physical properties of the joints. Moreover, as a proof of concept, we construct and demonstrate manipulators utilizing these joints. The increase in rotational movement enabled by this fabrication method, coupled with the compliant joint's flat-foldability and modular nature, make it a promising candidate for use in a wide range of applications.

A Geometric Kinematic Model for Flexible Voxel-Based Robots.

Voxel-based structures provide a modular, mechanically flexible periodic lattice, which can be used as a soft robot through internal deformations. To engage these structures for robotic tasks, we use a finite element method to characterize the motion caused by deforming single degrees of freedom and develop a reduced kinematic model. We find that nodes of the periodic lattice move in patterns along geometric planes, primarily along translational degrees of freedom. The resulting kinematic model frames the structural deformations in terms of user-defined control and end-effector nodes, which further reduces the model size. The derived Planes of Motion model can be equivalently used for forward and inverse kinematics, as demonstrated by the design of a voxel-based robotic gripper, and an in-depth design of a voxel-based robotic locomotor. The locomotive robot follows a tripod stable gait and the quasi-static model is validated with physical experiments.

Synthesis and evaluation of poly(propylene fumarate)-grafted graphene oxide as nanofiller for porous scaffolds.

In an effort to obtain porous scaffolds with improved mechanical properties and biocompatibility, the current study discusses nanocomposite materials based on poly(propylene fumarate)/N-vinyl pyrrolidone(PPF/NVP) networks reinforced with polymer-modified graphene oxide (GO@PPF). The GO@PPF nanofiller was synthesized through a facile and convenient surface esterification reaction, and the successful functionalization was demonstrated by complementary techniques such as FT-IR, XPS, TGA and TEM. The PPF/NVP/GO@PPF porous scaffolds obtained using NaCl as a porogen were further characterized in terms of morphology, mechanical properties, sol fraction, and in vitro degradability. SEM and nanoCT examinations of NaCl-leached samples revealed networks of interconnected pores, fairly uniform in size and shape. We show that the incorporation of GO@PPF in the polymer matrix leads to a significant enhancement in the mechanical properties, which we attribute to the formation of denser and more homogenous networks, as suggested by a decreased sol fraction for the scaffolds containing a higher amount of GO@PPF. Moreover, the surface of mineralized PPF/NVP/GO@PPG scaffolds is uniformly covered in hydroxyapatite-like crystals having a morphology and Ca/P ratio similar to bone tissue. Furthermore, the preliminary biocompatibility assessment revealed a good interaction between PPF/PVP/GO@PPF scaffolds and murine pre-osteoblasts in terms of cell viability and proliferation.

Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis.

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Machine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.