Repurposing Sewage and Toilet Systems: Environmental, Public Health, and Person-Centered Healthcare Applications.

Global terrestrial water supplies are rapidly depleting due to the consequences of climate change. Water scarcity results in an inevitable compromise of safe hygiene and sanitation practices, leading to the transmission of water-borne infectious diseases, and the preventable deaths of over 800.000 people each year. Moreover, almost 500 million people lack access to toilets and sanitation systems. Ecosystems are estimated to be contaminated by 6.2 million tons of nitrogenous products from human wastewater management practices. It is therefore imperative to transform toilet and sewage systems to promote equitable access to water and sanitation, improve public health, conserve water, and protect ecosystems. Here, the integration of emerging technologies in toilet and sewage networks to repurpose toilet and wastewater systems is reviewed. Potential applications of these systems to develop sustainable solutions to environmental challenges, promote public health, and advance person-centered healthcare are discussed.

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.

AI-Based Metamaterial Design.

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

Exosomes encapsulated in hydrogels for effective central nervous system drug delivery.

The targeted delivery of pharmacologically active molecules, metabolites, and growth factors to the brain parenchyma has become one of the major challenges following the onset of neurodegeneration and pathological conditions. The therapeutic effect of active biomolecules is significantly impaired after systemic administration in the central nervous system (CNS) because of the blood-brain barrier (BBB). Therefore, the development of novel therapeutic approaches capable of overcoming these limitations is under discussion. Exosomes (Exo) are nano-sized vesicles of endosomal origin that have a high distribution rate in biofluids. Recent advances have introduced Exo as naturally suitable bio-shuttles for the delivery of neurotrophic factors to the brain parenchyma. In recent years, many researchers have attempted to regulate the delivery of Exo to target sites while reducing their removal from circulation. The encapsulation of Exo in natural and synthetic hydrogels offers a valuable strategy to address the limitations of Exo, maintaining their integrity and controlling their release at a desired site. Herein, we highlight the current and novel approaches related to the application of hydrogels for the encapsulation of Exo in the field of CNS tissue engineering.

Deep learning-augmented T-junction droplet generation.

Droplet generation technology has become increasingly important in a wide range of applications, including biotechnology and chemical synthesis. T-junction channels are commonly used for droplet generation due to their integration capability of a larger number of droplet generators in a compact space. In this study, a finite element analysis (FEA) approach is employed to simulate droplet production and its dynamic regimes in a T-junction configuration and collect data for post-processing analysis. Next, image analysis was performed to calculate the droplet length and determine the droplet generation regime. Furthermore, machine learning (ML) and deep learning (DL) algorithms were applied to estimate outputs through examination of input parameters within the simulation range. At the end, a graphical user interface (GUI) was developed for estimation of the droplet characteristics based on inputs, enabling the users to preselect their designs with comparable microfluidic configurations within the studied range.

Impedimetric antimicrobial peptide biosensor for the detection of human immunodeficiency virus envelope protein gp120.

This study presents the design and implementation of an antimicrobial peptide-based electrochemical impedance spectroscopy (EIS) based biosensor system. The biosensor consists of a gold coated carbon electrode with MXene and silver nanoparticles (AgNPs) for the label-free detection of the human immunodeficiency virus (HIV) envelope protein gp120. Scanning electron microscopy was used to confirm the presence and distribution of MXene and AgNPs on the biosensor surface. The employment of the antimicrobial peptide on the electrode surface minimized the denaturation of the biorecognition receptor to ensure reliable and stable performance. The biosensor exhibited a linear range of 10-4000 pg mL-1 for gp120 detection, demonstrating good repeatability in real samples. The limit of detection (LOD) and limit of quantification (LOQ) were also calculated as 0.05 pg mL-1 and 0.14 pg mL-1, respectively. This biosensing platform has promising applications in the detection of HIV in clinical and point-of-care settings.

Loop-Mediated Isothermal Amplification-Integrated CRISPR Methods for Infectious Disease Diagnosis at Point of Care.

Infectious diseases continue to pose an imminent threat to global public health, leading to high numbers of deaths every year and disproportionately impacting developing countries where access to healthcare is limited. Biological, environmental, and social phenomena, including climate change, globalization, increased population density, and social inequity, contribute to the emergence of novel communicable diseases. Rapid and accurate diagnoses of infectious diseases are essential to preventing the transmission of infectious diseases. Although some commonly used diagnostic technologies provide highly sensitive and specific measurements, limitations including the requirement for complex equipment/infrastructure and refrigeration, the need for trained personnel, long sample processing times, and high cost remain unresolved. To ensure global access to affordable diagnostic methods, loop-mediated isothermal amplification (LAMP) integrated clustered regularly interspaced short palindromic repeat (CRISPR) based pathogen detection has emerged as a promising technology. Here, LAMP-integrated CRISPR-based nucleic acid detection methods are discussed in point-of-care (PoC) pathogen detection platforms, and current limitations and future directions are also identified.

Machine learning-augmented fluid dynamics simulations for micromixer educational module.

Micromixers play an imperative role in chemical and biomedical systems. Designing compact micromixers for laminar flows owning a low Reynolds number is more challenging than flows with higher turbulence. Machine learning models can enable the optimization of the designs and capabilities of microfluidic systems by receiving input from a training library and producing algorithms that can predict the outcomes prior to the fabrication process to minimize development cost and time. Here, an educational interactive microfluidic module is developed to enable the design of compact and efficient micromixers at low Reynolds regimes for Newtonian and non-Newtonian fluids. The optimization of Newtonian fluids designs was based on a machine learning model, which was trained by simulating and calculating the mixing index of 1890 different micromixer designs. This approach utilized a combination of six design parameters and the results as an input data set to a two-layer deep neural network with 100 nodes in each hidden layer. A trained model was achieved with R2 = 0.9543 that can be used to predict the mixing index and find the optimal parameters needed to design micromixers. Non-Newtonian fluid cases were also optimized using 56700 simulated designs with eight varying input parameters, reduced to 1890 designs, and then trained using the same deep neural network used for Newtonian fluids to obtain R2 = 0.9063. The framework was subsequently used as an interactive educational module, demonstrating a well-structured integration of technology-based modules such as using artificial intelligence in the engineering curriculum, which can highly contribute to engineering education.

Wound healing strategies based on nanoparticles incorporated in hydrogel wound patches.

The intricate, tightly controlled mechanism of wound healing that is a vital physiological mechanism is essential to maintaining the skin's natural barrier function. Numerous studies have focused on wound healing as it is a massive burden on the healthcare system. Wound repair is a complicated process with various cell types and microenvironment conditions. In wound healing studies, novel therapeutic approaches have been proposed to deliver an effective treatment. Nanoparticle-based materials are preferred due to their antibacterial activity, biocompatibility, and increased mechanical strength in wound healing. They can be divided into six main groups: metal NPs, ceramic NPs, polymer NPs, self-assembled NPs, composite NPs, and nanoparticle-loaded hydrogels. Each group shows several advantages and disadvantages, and which material will be used depends on the type, depth, and area of the wound. Better wound care/healing techniques are now possible, thanks to the development of wound healing strategies based on these materials, which mimic the extracellular matrix (ECM) microenvironment of the wound. Bearing this in mind, here we reviewed current studies on which NPs have been used in wound healing and how this strategy has become a key biotechnological procedure to treat skin infections and wounds.

Machine Learning-Enabled Optimization of Interstitial Fluid Collection via a Sweeping Microneedle Design.

Microneedles (MNs) allow for biological fluid sampling and drug delivery toward the development of minimally invasive diagnostics and treatment in medicine. MNs have been fabricated based on empirical data such as mechanical testing, and their physical parameters have been optimized through the trial-and-error method. While these methods showed adequate results, the performance of MNs can be enhanced by analyzing a large data set of parameters and their respective performance using artificial intelligence. In this study, finite element methods (FEMs) and machine learning (ML) models were integrated to determine the optimal physical parameters for a MN design in order to maximize the amount of collected fluid. The fluid behavior in a MN patch is simulated with several different physical and geometrical parameters using FEM, and the resulting data set is used as the input for ML algorithms including multiple linear regression, random forest regression, support vector regression, and neural networks. Decision tree regression (DTR) yielded the best prediction of optimal parameters. ML modeling methods can be utilized to optimize the geometrical design parameters of MNs in wearable devices for application in point-of-care diagnostics and targeted drug delivery.

3D-Printed Microrobots: Translational Challenges.

The science of microrobots is accelerating towards the creation of new functionalities for biomedical applications such as targeted delivery of agents, surgical procedures, tracking and imaging, and sensing. Using magnetic properties to control the motion of microrobots for these applications is emerging. Here, 3D printing methods are introduced for the fabrication of microrobots and their future perspectives are discussed to elucidate the path for enabling their clinical translation.

Microneedle arrays integrated with microfluidic systems: Emerging applications and fluid flow modeling.

Microneedle arrays are patches of needles at micro- and nano-scale, which are competent and versatile technologies that have been merged with microfluidic systems to construct more capable devices for biomedical applications, such as drug delivery, wound healing, biosensing, and sampling body fluids. In this paper, several designs and applications are reviewed. In addition, modeling approaches used in microneedle designs for fluid flow and mass transfer are discussed, and the challenges are highlighted.

Bioprinting in Microgravity.

Bioprinting as an extension of 3D printing offers capabilities for printing tissues and organs for application in biomedical engineering. Conducting bioprinting in space, where the gravity is zero, can enable new frontiers in tissue engineering. Fabrication of soft tissues, which usually collapse under their own weight, can be accelerated in microgravity conditions as the external forces are eliminated. Furthermore, human colonization in space can be supported by providing critical needs of life and ecosystems by 3D bioprinting without relying on cargos from Earth, e.g., by development and long-term employment of living engineered filters (such as sea sponges-known as critical for initiating and maintaining an ecosystem). This review covers bioprinting methods in microgravity along with providing an analysis on the process of shipping bioprinters to space and presenting a perspective on the prospects of zero-gravity bioprinting.

Biosensors for prostate cancer detection.

Prostate cancer (PC) is one of the most common tumors and a leading cause of mortality among men, resulting in ~375 000 deaths annually worldwide. Various analytical methods have been designed for quantitative and rapid detection of PC biomarkers. Electrochemical (EC), optical, and magnetic biosensors have been developed to detect tumor biomarkers in clinical and point-of-care (POC) settings. Although POC biosensors have shown potential for detection of PC biomarkers, some limitations, such as the sample preparation, should be considered. To tackle such shortcomings, new technologies have been utilized for development of more practical biosensors. Here, biosensing platforms for the detection of PC biomarkers such as immunosensors, aptasensors, genosensors, paper-based devices, microfluidic systems, and multiplex high-throughput platforms, are discussed.

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.

Electrochemiluminescent immunosensor for detection of carcinoembryonic antigen using luminol-coated silver nanoparticles.

Recently, electrochemiluminescent (ECL) immunosensors have received much attention in the field of biomarker detection. Here, a highly enhanced ECL immunosensing platform was designed for ultrasensitive detection of carcinoembryonic antigen (CEA). The surface of the glassy carbon electrode was enhanced by applying functional nanostructures such as thiolated graphene oxide (S-GO) and streptavidin-coated gold nanoparticles (SA-AuNPs). The selectivity and sensitivity of the designed immunosensor were improved by entrapping CEA biomolecules using a sandwich approach. Luminol/silver nanoparticles (Lu-SNPs) were applied as the main core of the signaling probe, which were then coated with streptavidin to provide overloading of the secondary antibody. The highly ECL signal enhancement was obtained due to the presence of horseradish peroxidase (HRP) in the signaling probe, in which the presence of H2O2 further amplified the intensity of the signals. The engineered immunosensor presented excellent sensitivity for CEA detection, with limit of detection (LOD) and linear detection range (LDR) values of 58 fg mL-1 and 0.1 pg mL-1 to 5 pg mL-1 (R2 = 0.9944), respectively. Besides its sensitivity, the fabricated ECL immunosensor presented outstanding selectivity for the detection of CEA in the presence of various similar agents. Additionally, the developed immunosensor showed an appropriate repeatability (RSD 3.8%) and proper stability (2 weeks). Having indicated a robust performance in the real human serum with stated LOD and LDR, the engineered immunosensor can be considered for the detection and monitoring of CEA in the clinic.

Nanotechnology-based electrochemical biosensors for monitoring breast cancer biomarkers.

Breast cancer is categorized as the most widespread cancer type among women globally. On-time diagnosis can decrease the mortality rate by making the right decision in the therapy procedure. These features lead to a reduction in medication time and socioeconomic burden. The current review article provides a comprehensive assessment for breast cancer diagnosis using nanomaterials and related technologies. Growing use of the nano/biotechnology domain in terms of electrochemical nanobiosensor designing was discussed in detail. In this regard, recent advances in nanomaterial applied for amplified biosensing methodologies were assessed for breast cancer diagnosis by focusing on the advantages and disadvantages of these approaches. We also monitored designing methods, advantages, and the necessity of suitable (nano) materials from a statistical standpoint. The main objective of this review is to classify the applicable biosensors based on breast cancer biomarkers. With numerous nano-sized platforms published for breast cancer diagnosis, this review tried to collect the most suitable methodologies for detecting biomarkers and certain breast cancer cell types.

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.

CRISPR-Cas-Integrated LAMP.

Pathogen-specific point-of-care (PoC) diagnostic tests have become an important need in the fight against infectious diseases and epidemics in recent years. PoC diagnostic tests are designed with the following parameters in mind: rapidity, accuracy, sensitivity, specificity, and ease of use. Molecular techniques are the gold standard for pathogen detection due to their accuracy and specificity. There are various limitations in adapting molecular diagnostic methods to PoC diagnostic tests. Efforts to overcome limitations are focused on the development of integrated molecular diagnostics by utilizing the latest technologies available to create the most successful PoC diagnostic platforms. With this point of view, a new generation technology was developed by combining loop-mediated isothermal amplification (LAMP) technology with clustered regularly interspaced short palindromic repeat (CRISPR)-associated (CRISPR-Cas) technology. This integrated approach benefits from the properties of LAMP technology, namely its high efficiency, short turnaround time, and the lack of need for a complex device. It also makes use of the programmable function of CRISPR-Cas technology and the collateral cleavage activity of certain Cas proteins that allow for convenient reporter detection. Thus, this combined technology enables the development of PoC diagnostic tests with high sensitivity, specificity, and ease of use without the need for complicated devices. In this review, we discuss the advantages and limitations of the CRISPR/Cas combined LAMP technology. We review current limitations to convert CRISPR combined LAMP into pathogen-specific PoC platforms. Furthermore, we point out the need to design more useful PoC platforms using microfabrication technologies by developing strategies that overcome the limitations of this new technology, reduce its complexity, and reduce the risk of contamination.