Sensing devices have shown tremendous potential for monitoring state-of-the-art organ chip devices. However, challenges like miniaturization while maintaining higher performance, longer operating times for continuous monitoring, and fabrication complexities limit their use. Herein simple, low-cost, and solution-processible inkjet dispenser printing of embedded electrochemical sensors for dissolved oxygen (DO) and reactive oxygen species (ROS) is proposed for monitoring developmental (initially normoxia) and induced hypoxia in a custom-developed gut bilayer microfluidic chip platform for 6 days. The DO sensors showed a high sensitivity of 31.1 nA L mg⁻¹ with a limit of detection (LOD) of 0.67 mg L⁻¹ within the 0–9 mg L⁻¹ range, whereas the ROS sensor had a higher sensitivity of 1.44 nA μm⁻¹ with a limit of detection of 1.7 μm within the 0–300 μm range. The dynamics of the barrier tight junctions are quantified with the help of an in-house developed trans-epithelial–endothelial electrical impedance (TEEI) sensor. Immunofluorescence staining was used to evaluate the expressions of HIF-1α and tight junction protein (TJP) ZO-1. This platform can also be used to enhance bioavailability assays, drug transport studies under an oxygen-controlled environment, and even other barrier organ models, as well as for various applications like toxicity testing, disease modeling and drug screening.

The cellular microenvironment is influenced explicitly by the extracellular matrix (ECM), the main tissue support biomaterial, as a decisive factor for tissue growth patterns. The recent emergence of hepatic microphysiological systems (MPS) provide the basic physiological emulation of the human liver for drug screening. However, engineering microfluidic devices with standardized surface coatings of ECM may improve MPS-based organ-specific emulation for improved drug screening. The influence of surface coatings of different ECM types on tissue development needs to be optimized. Additionally, an intensity-based image processing tool and transepithelial electrical resistance (TEER) sensor may assist in the analysis of tissue formation capacity under the influence of different ECM types. The current study highlights the role of ECM coatings for improved tissue formation, implying the additional role of image processing and TEER sensors. We studied hepatic tissue formation under the influence of multiple concentrations of Matrigel, collagen, fibronectin, and poly-L-lysine. Based on experimental data, a mathematical model was developed, and ECM concentrations were validated for better tissue development. TEER sensor and image processing data were used to evaluate the development of a hepatic MPS for human liver physiology modeling. Image analysis data for tissue formation was further strengthened by metabolic quantification of albumin, urea, and cytochrome P450. Standardized ECM type for MPS may improve clinical relevance for modeling hepatic tissue microenvironment, and image processing possibly enhance the tissue analysis of the MPS.

Organoids produce through traditional manual pipetting methods face challenges such as labor‐intensive procedures and batch‐to‐batch variability in quality. To ensure consistent organoid production, 3D bioprinting platforms offer a more efficient alternative. However, optimizing multiple printing parameters to achieve the desired organoid size remains a time‐consuming and costly endeavor. To address these obstacles, machine learning is employed to optimize five critical printing parameters (i.e., bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration), and develop algorithms capable of immediate cellular droplet size prediction. In this study, a high‐throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. Finally, these top‐performing machine learning models are integrated into a user‐friendly interface to streamline usability. The bioprinting parameter optimization platform develops in this study is expected to create significant synergy when combined with various bioprinting technologies, advancing the scalable production of organoids for a range of applications.

The growing demand for rapid, low-cost biomarker detection has accelerated development of point-of-care electrochemical biosensors, yet their broader adoption is still constrained by limited reproducibility, stability, and scalable fabrication, especially for nanomaterial-modified electrodes. Here, we introduce an electrochemical biosensor for uric acid detection based on a bentonite nanoclay (Bent) and multi-walled carbon nanotubes (MWCNTs) nanocomposite film deposited via a custom spray-coating method. The fabrication process was systematically characterized to evaluate the influence of Bent incorporation on film morphology, electrode surface properties, and electrochemical behavior. Experimental results confirmed that Bent improved homogeneity and film-forming behavior within the MWCNTs network, contributing to enhanced sensing performance. The resulting biosensor exhibited high sensitivity (13.91 mA mM^-1 cm^-2) with a linear range of 10-2000 μM and a limit of detection of 5.31 μM. Notably, the developed sensor maintained acceptable recovery (92.2-110.8 %) even in contrived samples. Overall, this study demonstrates a reproducible lab-scale fabrication approach and highlights the potential of Bent for enabling highly sensitive and stable biosensors, offering a promising route toward real-world point-of-care applications.

Controlled volume microliter cell-laden droplet bioprinting is important for precise biologics deposition, reliably replicating 3D microtissue environments for building cell aggregates or organoids. To achieve this, we propose an innovative machine-learning approach to predict cell-laden droplet volumes according to input parameters. We developed a novel bioprinting platform capable of collecting high-throughput droplet images and generating an extensive dataset for training machine learning and deep learning algorithms. Our research compared the performance of three machine learning and two deep learning algorithms that predict droplet volume based on numerous bioprinting parameters. By adjusting bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration as input parameters, we precisely could control droplet sizes, ranging from 0.1 µL to 50 µL in volume. We utilized a hydrogel precursor composed of 5% gelatin methacrylate and a mixture of 0.5% and 1% alginate, respectively. Additionally, we optimized the cell bioprinting process using green fluorescent protein-tagged 3T3 fibroblast cells. These models demonstrated superior predictive accuracy and revealed the interrelationships among parameters while taking minimal time for training and testing. This method promises to advance the mass production of organoids and microtissues with precise volume control for various biomedical applications.

Electrical conductivity is a crucial requirement of matrices for developing engineered neural tissues. A conductive matrix not only supports cell growth but also provides potential to stimulate the cells. However, electrically conductive matrices often require inclusion of synthetic polymers, nanomaterials and large number of ionic species. While enhancing electrical conductivity, often properties like transparency, mechanical stiffness and biocompatibility are compromised which can render the resulting matrices partially suitable for neural tissue engineering. Further, the byproducts of matrix degradation can have unforeseen influences. Therefore, electrically active matrices are required which provide a suitable combination of electrical conductivity, mechanical properties and biocompatibility. In this work, a novel biomaterial is described which results in optically transparent, electrically conductive and highly biocompatible matrices along with ability to match the native neural tissue stiffness. Using gelatin methacryloyl (GelMA) as base hydrogel, we covalently incorporated zwitterionic functional groups to obtain a composite matrix. The zwitterion moieties were derived from Ornithine by synthesizing ornithine methacryloyl (OrnMA) and blending with GelMA inks. Through systematic characterization we demonstrated the suitability of GelMA-OrnMA hydrogels in providing mechanical stiffness matching the native neural tissues, supporting proliferation of human astrocytes in 3D culture and electrical conductivity in the range required for electrically active cell types like astrocytes. Owing to their electrical conductivity, these matrices also influenced the growth of astrocytes which manifested as significant changes in their organization and morphology. These findings suggest that GelMA-OrnMA has immense potential as a bioink for developing engineered neural tissues.