Skin-on-a-chip technologies towards clinical translation and commercialization.

Skin is the largest organ of the human body which plays a critical role in thermoregulation, metabolism (e.g. synthesis of vitamin D), and protection of other organs from environmental threats, such as infections, microorganisms, ultraviolet radiation, and physical damage. Even though skin diseases are considered to be less fatal, the ubiquity of skin diseases and irritation caused by them highlights the importance of skin studies. Furthermore, skin is a promising means for transdermal drug delivery, which requires a thorough understanding of human skin structure. Current animal andin vitrotwo/three-dimensional skin models provide a platform for disease studies and drug testing, whereas they face challenges in the complete recapitulation of the dynamic and complex structure of actual skin tissue. One of the most effective methods for testing pharmaceuticals and modeling skin diseases are skin-on-a-chip (SoC) platforms. SoC technologies provide a non-invasive approach for examining 3D skin layers and artificially creating disease models in order to develop diagnostic or therapeutic methods. In addition, SoC models enable dynamic perfusion of culture medium with nutrients and facilitate the continuous removal of cellular waste to further mimic thein vivocondition. Here, the article reviews the most recent advances in the design and applications of SoC platforms for disease modeling as well as the analysis of drugs and cosmetics. By examining the contributions of different patents to the physiological relevance of skin models, the review underscores the significant shift towards more ethical and efficient alternatives to animal testing. Furthermore, it explores the market dynamics ofin vitroskin models and organ-on-a-chip platforms, discussing the impact of legislative changes and market demand on the development and adoption of these advanced research tools. This article also identifies the existing obstacles that hinder the advancement of SoC platforms, proposing directions for future improvements, particularly focusing on the journey towards clinical adoption.

Additive manufacturing and three-dimensional printing in obstetrics and gynecology: a comprehensive review.

Three-dimensional (3D) printing, also known as additive manufacturing, is a technology used to create complex 3D structures out of a digital model that can be almost any shape. Additive manufacturing allows the creation of customized, finely detailed constructs. Improvements in 3D printing, increased 3D printer availability, decreasing costs, development of biomaterials, and improved cell culture techniques have enabled complex, novel, and customized medical applications to develop. There have been rapid development and utilization of 3D printing technologies in orthopedics, dentistry, urology, reconstructive surgery, and other health care areas. Obstetrics and Gynecology (OBGYN) is an emerging application field for 3D printing. This technology can be utilized in OBGYN for preventive medicine, early diagnosis, and timely treatment of women-and-fetus-specific health issues. Moreover, 3D printed simulations of surgical procedures enable the training of physicians according to the needs of any given procedure. Herein, we summarize the technology and materials behind additive manufacturing and review the most recent advancements in the application of 3D printing in OBGYN studies, such as diagnosis, surgical planning, training, simulation, and customized prosthesis. Furthermore, we aim to give a future perspective on the integration of 3D printing and OBGYN applications and to provide insight into the potential applications.

Shape Fidelity Evaluation of Alginate-Based Hydrogels through Extrusion-Based Bioprinting.

Extrusion-based 3D bioprinting is a promising technique for fabricating multi-layered, complex biostructures, as it enables multi-material dispersion of bioinks with a straightforward procedure (particularly for users with limited additive manufacturing skills). Nonetheless, this method faces challenges in retaining the shape fidelity of the 3D-bioprinted structure, i.e., the collapse of filament (bioink) due to gravity and/or spreading of the bioink owing to the low viscosity, ultimately complicating the fabrication of multi-layered designs that can maintain the desired pore structure. While low viscosity is required to ensure a continuous flow of material (without clogging), a bioink should be viscous enough to retain its shape post-printing, highlighting the importance of bioink properties optimization. Here, two quantitative analyses are performed to evaluate shape fidelity. First, the filament collapse deformation is evaluated by printing different concentrations of alginate and its crosslinker (calcium chloride) by a co-axial nozzle over a platform to observe the overhanging deformation over time at two different ambient temperatures. In addition, a mathematical model is developed to estimate Young's modulus and filament collapse over time. Second, the printability of alginate is improved by optimizing gelatin concentrations and analyzing the pore size area. In addition, the biocompatibility of proposed bioinks is evaluated with a cell viability test. The proposed bioink (3% w/v gelatin in 4% alginate) yielded a 98% normalized pore number (high shape fidelity) while maintaining >90% cell viability five days after being bioprinted. Integration of quantitative analysis/simulations and 3D printing facilitate the determination of the optimum composition and concentration of different elements of a bioink to prevent filament collapse or bioink spreading (post-printing), ultimately resulting in high shape fidelity (i.e., retaining the shape) and printing quality.

3D-printed microrobots from design to translation.

Microrobots have attracted the attention of scientists owing to their unique features to accomplish tasks in hard-to-reach sites in the human body. Microrobots can be precisely actuated and maneuvered individually or in a swarm for cargo delivery, sampling, surgery, and imaging applications. In addition, microrobots have found applications in the environmental sector (e.g., water treatment). Besides, recent advancements of three-dimensional (3D) printers have enabled the high-resolution fabrication of microrobots with a faster design-production turnaround time for users with limited micromanufacturing skills. Here, the latest end applications of 3D printed microrobots are reviewed (ranging from environmental to biomedical applications) along with a brief discussion over the feasible actuation methods (e.g., on- and off-board), and practical 3D printing technologies for microrobot fabrication. In addition, as a future perspective, we discussed the potential advantages of integration of microrobots with smart materials, and conceivable benefits of implementation of artificial intelligence (AI), as well as physical intelligence (PI). Moreover, in order to facilitate bench-to-bedside translation of microrobots, current challenges impeding clinical translation of microrobots are elaborated, including entry obstacles (e.g., immune system attacks) and cumbersome standard test procedures to ensure biocompatibility.

Deep Learning-Enabled Technologies for Bioimage Analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

Hemp-Based Microfluidics.

Hemp is a sustainable, recyclable, and high-yield annual crop that can be used to produce textiles, plastics, composites, concrete, fibers, biofuels, bionutrients, and paper. The integration of microfluidic paper-based analytical devices (µPADs) with hemp paper can improve the environmental friendliness and high-throughputness of µPADs. However, there is a lack of sufficient scientific studies exploring the functionality, pros, and cons of hemp as a substrate for µPADs. Herein, we used a desktop pen plotter and commercial markers to pattern hydrophobic barriers on hemp paper, in a single step, in order to characterize the ability of markers to form water-resistant patterns on hemp. In addition, since a higher resolution results in densely packed, cost-effective devices with a minimized need for costly reagents, we examined the smallest and thinnest water-resistant patterns plottable on hemp-based papers. Furthermore, the wicking speed and distance of fluids with different viscosities on Whatman No. 1 and hemp papers were compared. Additionally, the wettability of hemp and Whatman grade 1 paper was compared by measuring their contact angles. Besides, the effects of various channel sizes, as well as the number of branches, on the wicking distance of the channeled hemp paper was studied. The governing equations for the wicking distance on channels with laser-cut and hydrophobic side boundaries are presented and were evaluated with our experimental data, elucidating the applicability of the modified Washburn equation for modeling the wicking distance of fluids on hemp paper-based microfluidic devices. Finally, we validated hemp paper as a substrate for the detection and analysis of the potassium concentration in artificial urine.

Increasing the packing density of assays in paper-based microfluidic devices.

Paper-based devices have a wide range of applications in point-of-care diagnostics, environmental analysis, and food monitoring. Paper-based devices can be deployed to resource-limited countries and remote settings in developed countries. Paper-based point-of-care devices can provide access to diagnostic assays without significant user training to perform the tests accurately and timely. The market penetration of paper-based assays requires decreased device fabrication costs, including larger packing density of assays (i.e., closely packed features) and minimization of assay reagents. In this review, we discuss fabrication methods that allow for increasing packing density and generating closely packed features in paper-based devices. To ensure that the paper-based device is low-cost, advanced fabrication methods have been developed for the mass production of closely packed assays. These emerging methods will enable minimizing the volume of required samples (e.g., liquid biopsies) and reagents in paper-based microfluidic devices.

3D-printed microneedles in biomedical applications.

Conventional needle technologies can be advanced with emerging nano- and micro-fabrication methods to fabricate microneedles. Nano-/micro-fabricated microneedles seek to mitigate penetration pain and tissue damage, as well as providing accurately controlled robust channels for administrating bioagents and collecting body fluids. Here, design and 3D printing strategies of microneedles are discussed with emerging applications in biomedical devices and healthcare technologies. 3D printing offers customization, cost-efficiency, a rapid turnaround time between design iterations, and enhanced accessibility. Increasing the printing resolution, the accuracy of the features, and the accessibility of low-cost raw printing materials have empowered 3D printing to be utilized for the fabrication of microneedle platforms. The development of 3D-printed microneedles has enabled the evolution of pain-free controlled release drug delivery systems, devices for extracting fluids from the cutaneous tissue, biosignal acquisition, and point-of-care diagnostic devices in personalized medicine.

Microfluidics for microalgal biotechnology.

Microalgae have expanded their roles as renewable and sustainable feedstocks for biofuel, smart nutrition, biopharmaceutical, cosmeceutical, biosensing, and space technologies. They accumulate valuable biochemical compounds from protein, carbohydrate, and lipid groups, including pigments and carotenoids. Microalgal biomass, which can be adopted for multivalorization under biorefinery settings, allows not only the production of various biofuels but also other value-added biotechnological products. However, state-of-the-art technologies are required to optimize yield, quality, and the economical aspects of both upstream and downstream processes. As such, the need to use microfluidic-based devices for both fundamental research and industrial applications of microalgae, arises due to their microscale sizes and dilute cultures. Microfluidics-based devices are superior to their competitors through their ability to perform multiple functions such as sorting and analyzing small amounts of samples (nanoliter to picoliter) with higher sensitivities. Here, we review emerging applications of microfluidic technologies on microalgal processes in cell sorting, cultivation, harvesting, and applications in biofuels, biosensing, drug delivery, and nutrition.

Machine learning-enabled multiplexed microfluidic sensors.

High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.