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.

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 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.

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.

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.

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.

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.

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.

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.

Internet-Connected Cortical Organoids for Project-Based Stem Cell and Neuroscience Education.

The introduction of Internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell (PSC)-derived cortical organoids in two different settings: using microscopy to monitor organoid growth in an introductory tissue culture course and using high-density (HD) multielectrode arrays (MEAs) to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.

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.

A system for bioelectronic delivery of treatment directed toward wound healing.

The development of wearable bioelectronic systems is a promising approach for optimal delivery of therapeutic treatments. These systems can provide continuous delivery of ions, charged biomolecules, and an electric field for various medical applications. However, rapid prototyping of wearable bioelectronic systems for controlled delivery of specific treatments with a scalable fabrication process is challenging. We present a wearable bioelectronic system comprised of a polydimethylsiloxane (PDMS) device cast in customizable 3D printed molds and a printed circuit board (PCB), which employs commercially available engineering components and tools throughout design and fabrication. The system, featuring solution-filled reservoirs, embedded electrodes, and hydrogel-filled capillary tubing, is assembled modularly. The PDMS and PCB both contain matching through-holes designed to hold metallic contact posts coated with silver epoxy, allowing for mechanical and electrical integration. This assembly scheme allows us to interchange subsystem components, such as various PCB designs and reservoir solutions. We present three PCB designs: a wired version and two battery-powered versions with and without onboard memory. The wired design uses an external voltage controller for device actuation. The battery-powered PCB design uses a microcontroller unit to enable pre-programmed applied voltages and deep sleep mode to prolong battery run time. Finally, the battery-powered PCB with onboard memory is developed to record delivered currents, which enables us to verify treatment dose delivered. To demonstrate the functionality of the platform, the devices are used to deliver H[Formula: see text] in vivo using mouse models and fluoxetine ex vivo using a simulated wound environment. Immunohistochemistry staining shows an improvement of 35.86% in the M1/M2 ratio of H[Formula: see text]-treated wounds compared with control wounds, indicating the potential of the platform to improve wound healing.

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.

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.

Internet-connected cortical organoids for project-based stem cell and neuroscience education.

The introduction of internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell-derived cortical organoids in two different settings: Using microscopy to monitor organoid growth in an introductory tissue culture course, and using high density multielectrode arrays to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.

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.

Preparation and In Vitro Characterization of Alkyl Polyglucoside-Based Microemulsion for Topical Administration of Curcumin.

The skin is a complex and selective system from the perspective of permeability to substances from the external environment. Microemulsion systems have demonstrated a high performance in encapsulating, protecting and transporting active substances through the skin. Due to the low viscosity of microemulsion systems and the importance of a texture that is easy to apply in the cosmetic and pharmaceutical fields, gel microemulsions are increasingly gaining more interest. The aim of this study was to develop new microemulsion systems for topical use; to identify a suitable water-soluble polymer in order to obtain gel microemulsions; and to study the efficacy of the developed microemulsion and gel microemulsion systems in the delivery of a model active ingredient, namely curcumin, into the skin. A pseudo-ternary phase diagram was developed using AKYPO® SOFT 100 BVC, PLANTACARE® 2000 UP Solution and ethanol as a surfactant mix; caprylic/capric triglycerides, obtained from coconut oil, as the oily phase; and distilled water. To obtain gel microemulsions, sodium hyaluronate salt was used. All these ingredients are safe for the skin and are biodegradable. The selected microemulsions and gel microemulsions were physicochemically characterized by means of dynamic light scattering, electrical conductivity, polarized microscopy and rheometric measurements. To evaluate the efficiency of the selected microemulsion and gel microemulsion to deliver the encapsulated curcumin, an in vitro permeation study was performed.

Open-loop lab-on-a-chip technology enables remote computer science training in Latinx life sciences students.

Despite many interventions, science education remains highly inequitable throughout the world. Among all life sciences fields, Bioinformatics and Computational Biology suffer from the strongest underrepresentation of racial and gender minorities. Internet-enabled project-based learning (PBL) 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 train Latinx life science undergraduate students in concepts of computer programming by taking advantage of open-loop cloud-integrated LoCs. We developed a context-aware curriculum to train students at over 8,000 km from the experimental site. We showed that this approach was sufficient to develop programming skills and increase the interest of students in continuing careers in Bioinformatics. Altogether, we conclude that LoC-based Internet-enabled PBL can become a powerful tool to train Latinx students and increase the diversity in STEM.

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.

Introducing Semi-Interpenetrating Networks of Chitosan and Ammonium-Quaternary Polymers for the Effective Removal of Waterborne Pathogens from Wastewaters.

The present work aims to study the influence of ammonium-quaternary monomers and chitosan, obtained from different sources, upon the effect of semi-interpenetrating polymer network (semi-IPN) hydrogels upon the removal of waterborne pathogens and bacteria from wastewater. To this end, the study was focused on using vinyl benzyl trimethylammonium chloride (VBTAC), a water-soluble monomer with known antibacterial properties, and mineral-enriched chitosan extracted from shrimp shells, to prepare the semi-IPNs. By using chitosan, which still contains the native minerals (mainly calcium carbonate), the study intends to justify that the stability and efficiency of the semi-IPN bactericidal devices can be modified and better improved. The new semi-IPNs were characterized for composition, thermal stability and morphology using well-known methods. Swelling degree (SD%) and the bactericidal effect assessed using molecular methods revealed that hydrogels made of chitosan derived from shrimp shell demonstrated the most competitive and promising potential for wastewater (WW) treatment.