A wearable microneedle patch incorporating reversible FRET-based hydrogel sensors for continuous glucose monitoring.

Continuous glucose monitors are crucial for diabetes management, but invasive sampling, signal drift and frequent calibrations restrict their widespread usage. Microneedle sensors are emerging as a minimally-invasive platform for real-time monitoring of clinical parameters in interstitial fluid. Herein, a painless and flexible microneedle sensing patch is constructed by a mechanically-strong microneedle base and a thin layer of fluorescent hydrogel sensor for on-site, accurate, and continuous glucose monitoring. The Förster resonance energy transfer (FRET)-based hydrogel sensors are fabricated by facile photopolymerizations of acryloylated FRET pairs and glucose-specific phenylboronic acid. The optimized hydrogel sensor enables quantification of glucose with reversibility, high selectivity, and signal stability against photobleaching. Poly (ethylene glycol diacrylate)-co-polyacrylamide hydrogel is utilized as the microneedle base, facilitating effective skin piercing and biofluid extraction. The integrated microneedle sensor patch displays a sensitivity of 0.029 mM-1 in the (patho)physiological range, a low detection limit of 0.193 mM, and a response time of 7.7 min in human serum. Hypoglycemia, euglycemia and hyperglycemia are continuously monitored over 6 h simulated meal and rest activities in a porcine skin model. This microneedle sensor with high transdermal analytical performance offers a powerful tool for continuous diabetes monitoring at point-of-care settings.

Photonic Solutions for Challenges in Sensing.

Sensing technologies support timely and critical decisions to save precious resources in healthcare, veterinary care, food safety, and environmental protection. However, the design of sensors demands strict technical characteristics for real-world applications. In this Viewpoint, we discuss the main challenges to tackle in the sensing field and how photonics represents a valuable tool in this sphere.

Engineered Bacteria as Living Biosensors in Dermal Tattoos.

Dermal tattoo biosensors are promising platforms for real-time monitoring of biomarkers, with skin used as a diagnostic interface. Traditional tattoo sensors have utilized small molecules as biosensing elements. However, the rise of synthetic biology has enabled the potential employment of engineered bacteria as living analytical tools. Exploiting engineered bacterial sensors will allow for potentially more sensitive detection across a broad biomarker range, with advanced processing and sense/response functionalities using genetic circuits. Here, the interfacing of bacterial biosensors as living analytics in tattoos is shown. Engineered bacteria are encapsulated into micron-scale hydrogel beads prepared through scalable microfluidics. These biosensors can sense both biochemical cues (model biomarkers) and biophysical cues (temperature changes, using RNA thermometers), with fluorescent readouts. By tattooing beads into skin models and confirming sensor activity post-tattooing, our study establishes a foundation for integrating bacteria as living biosensing entities in tattoos.

Biosensors for psychiatric biomarkers in mental health monitoring.

Psychiatric disorders are associated with serve disturbances in cognition, emotional control, and/or behavior regulation, yet few routine clinical tools are available for the real-time evaluation and early-stage diagnosis of mental health. Abnormal levels of relevant biomarkers may imply biological, neurological, and developmental dysfunctions of psychiatric patients. Exploring biosensors that can provide rapid, in-situ, and real-time monitoring of psychiatric biomarkers is therefore vital for prevention, diagnosis, treatment, and prognosis of mental disorders. Recently, psychiatric biosensors with high sensitivity, selectivity, and reproducibility have been widely developed, which are mainly based on electrochemical and optical sensing technologies. This review presented psychiatric disorders with high morbidity, disability, and mortality, followed by describing pathophysiology in a biomarker-implying manner. The latest biosensors developed for the detection of representative psychiatric biomarkers (e.g., cortisol, dopamine, and serotonin) were comprehensively summarized and compared in their sensitivities, sensing technologies, applicable biological platforms, and integrative readouts. These well-developed biosensors are promising for facilitating the clinical utility and commercialization of point-of-care diagnostics. It is anticipated that mental healthcare could be gradually improved in multiple perspectives, ranging from innovations in psychiatric biosensors in terms of biometric elements, transducing principles, and flexible readouts, to the construction of 'Big-Data' networks utilized for sharing intractable psychiatric indicators and cases.

A paper-based dual functional biosensor for safe and user-friendly point-of-care urine analysis.

Safe, accurate, and reliable analysis of urinary biomarkers is clinically important for early detection and monitoring of the progression of chronic kidney disease (CKD), as it has become one of the world's most prevalent non-communicable diseases. However, current technologies for measuring urinary biomarkers are either time-consuming and limited to well-equipped hospitals or lack the necessary sensitivity for quantitative analysis and post a health risk to frontline practitioners. Here we report a robust paper-based dual functional biosensor, which is integrated with the clinical urine sampling vial, for the simultaneous and quantitative analysis of pH and glucose in urine. The pH sensor was fabricated by electrochemically depositing IrOx onto a paper substrate using optimised parameters, which enabled an ultrahigh sensitivity of 71.58 mV pH-1. Glucose oxidase (GOx) was used in combination with an electrochemically deposited Prussian blue layer for the detection of glucose, and its performance was enhanced by gold nanoparticles (AuNPs), chitosan, and graphite composites, achieving a sensitivity of 1.5 µA mM-1. This dual function biosensor was validated using clinical urine samples, where a correlation coefficient of 0.96 for pH and 0.98 for glucose detection was achieved with commercial methods as references. More importantly, the urine sampling vial was kept sealed throughout the sample-to-result process, which minimised the health risk to frontline practitioners and simplified the diagnostic procedures. This diagnostic platform, therefore, holds high promise as a rapid, accurate, safe, and user-friendly point-of-care (POC) technology for the analysis of urinary biomarkers in frontline clinical settings.

Plasmonic Contact Lenses Based on Silver Nanoparticles for Blue Light Protection.

Constant exposure to blue light emanating from screens, lamps, digital devices, or other artificial sources at night can suppress melatonin secretion, potentially compromising both sleep quality and overall health. Daytime exposure to elevated levels of blue light can also lead to permanent damage to the eyes. Here, we have developed blue light protective plasmonic contact lenses (PCLs) to mitigate blue light exposure. Crafted from poly(hydroxyethyl methacrylate) (pHEMA) and infused with silver nanoparticles, these contact lenses serve as a protective barrier to filter blue light. Leveraging the plasmonic properties of silver nanoparticles, the lenses effectively filtered out the undesirable blue light (400-510 nm), demonstrating substantial protection (22-71%) while maintaining high transparency (80-96%) for the desirable light (511-780 nm). The maximum protection level reaches a peak of 79% at 455 nm, aligned with the emission peak for the blue light sourced from LEDs in consumer displays. The presence of silver nanoparticles was found to have an insignificant impact on the water content of the developed contact lenses. The lenses maintained high water retention levels within the range of 50-70 wt %, comparable to commercial contact lenses. The optical performance of the developed lenses remains unaffected in both artificial tears and contact lens storage solution over a month with no detected leakage of the nanoparticles. Additionally, the MTT assay confirmed that the lenses were biocompatible and noncytotoxic, maintaining cell viability at over 85% after 24 h of incubation. These lenses could be a potential solution to protect against the most intense wavelengths emitted by consumer displays and offer a remedy to counteract the deleterious effects of prolonged blue light exposure.

Biosensors for melanoma skin cancer diagnostics.

Skin cancer is a critical global public health concern, with melanoma being the deadliest variant, correlated to 80% of skin cancer-related deaths and a remarkable propensity to metastasize. Despite notable progress in skin cancer prevention and diagnosis, the limitations of existing methods accentuate the demand for precise diagnostic tools. Biosensors have emerged as valuable clinical tools, enabling rapid and reliable point-of-care (POC) testing of skin cancer. This review offers insights into skin cancer development, highlights essential cutaneous melanoma biomarkers, and assesses the current landscape of biosensing technologies for diagnosis. The comprehensive analysis in this review underscores the transformative potential of biosensors in revolutionizing melanoma skin cancer diagnosis, emphasizing their critical role in advancing patient outcomes and healthcare efficiency. The increasing availability of these approaches supports direct diagnosis and aims to reduce the reliance on biopsies, enhancing POC diagnosis. Recent advancements in biosensors for skin cancer diagnosis hold great promise, with their integration into healthcare expected to enhance early detection accuracy and reliability, thereby mitigating socioeconomic disparities.

Microneedle Sensors for Point-of-Care Diagnostics.

Point-of-care (POC) has the capacity to support low-cost, accurate and real-time actionable diagnostic data. Microneedle sensors have received considerable attention as an emerging technique to evolve blood-based diagnostics owing to their direct and painless access to a rich source of biomarkers from interstitial fluid. This review systematically summarizes the recent innovations in microneedle sensors with a particular focus on their utility in POC diagnostics and personalized medicine. The integration of various sensing techniques, mostly electrochemical and optical sensing, has been established in diverse architectures of "lab-on-a-microneedle" platforms. Microneedle sensors with tailored geometries, mechanical flexibility, and biocompatibility are constructed with a variety of materials and fabrication methods. Microneedles categorized into four types: metals, inorganics, polymers, and hydrogels, have been elaborated with state-of-the-art bioengineering strategies for minimally invasive, continuous, and multiplexed sensing. Microneedle sensors have been employed to detect a wide range of biomarkers from electrolytes, metabolites, polysaccharides, nucleic acids, proteins to drugs. Insightful perspectives are outlined from biofluid, microneedles, biosensors, POC devices, and theragnostic instruments, which depict a bright future of the upcoming personalized and intelligent health management.

Non-contact monitoring of glucose concentration and pH by integration of wearable and implantable hydrogel sensors with optical coherence tomography.

Optical coherence tomography (OCT) is a noninvasive imaging technique with large penetration depth into the tissue, but limited chemical specificity. By incorporating functional co-monomers, hydrogels can be designed to respond to specific molecules and undergo reversible volume changes. In this study, we present implantable and wearable biocompatible hydrogel sensors combined with OCT to monitor their thickness change as a tool for continuous and real-time monitoring of glucose concentration and pH. The results demonstrate the potential of combining hydrogel biosensors with OCT for non-contact continuous in-vivo monitoring of physiological parameters.

Contact lens sensor for ocular inflammation monitoring.

Contact lens sensors have been emerging as point-of-care devices in recent healthcare developments for ocular physiological condition monitoring and diagnosis. Fluorescence sensing technologies have been widely applied in contact lens sensors due to their accuracy, high sensitivity, and specificity. As ascorbic acid (AA) level in tears is closely related to ocular inflammation, a fluorescent contact lens sensor incorporating a BSA-Au nanocluster (NC) probe is developed for in situ tear AA detection. The NCs are firstly synthesized to obtain a fluorescent probe, which exhibits high reusability through the quench/recover (KMnO4/AA) process. The probe is then encapsulated with 15 wt% of poly(vinyl alcohol) (PVA) and 1.5 wt% of citric acid (CA) film, and implemented on a closed microfluidic contact lens sensing region. The laser-ablated microfluidic channel in contact lens sensors allows for tear fluid to flow through the sensing region, enabling an in-situ detection of AA. Meanwhile, a smartphone application accompanied by a customized 3D printed readout box is developed for image caption and algorism to quantitative analysis of AA levels. The contact lens sensor is tested within the readout box and the emission signal is collected through the smartphone camera at room temperature with an achieved LOD of 0.178 mmol L-1 (0.0-1.2 mmol L-1). The operational and storage lifetime is also evaluated to characterize the sensor properties and resulted in 20 h and 10 days, respectively. The reusable AA contact lens sensor is promising to lead to an alternative accessible diagnostic method for ocular inflammation in 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.

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.

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.

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.

Wearable artificial intelligence biosensor networks.

The demand for high-quality healthcare and well-being services is remarkably increasing due to the ageing global population and modern lifestyles. Recently, the integration of wearables and artificial intelligence (AI) has attracted extensive academic and technological attention for its powerful high-dimensional data processing of wearable biosensing networks. This work reviews the recent developments in AI-assisted wearable biosensing devices in disease diagnostics and fatigue monitoring demonstrating the trend towards personalised medicine with highly efficient, cost-effective, and accurate point-of-care diagnosis by finding hidden patterns in biosensing data and detecting abnormalities. The reliability of adaptive learning and synthetic data and data privacy still need further investigation to realise personalised medicine in the next decade. Due to the worldwide popularity of smartphones, they have been utilised for sensor readout, wireless data transfer, data processing and storage, result display, and cloud server communication leading to the development of smartphone-based biosensing systems. The recent advances have demonstrated a promising future for the healthcare system because of the increasing data processing power, transfer efficiency and storage capacity and diversifying functionalities.

Quantitative brain-derived neurotrophic factor lateral flow assay for point-of-care detection of glaucoma.

Glaucoma, a ruinous group of eye diseases with progressive degeneration of the optic nerve and vision loss, is the leading cause of irreversible blindness. Accurate and timely diagnosis of glaucoma is critical to promote secondary prevention and early disease-modifying therapies. Reliable, cheap, and rapid tests for measuring disease activities are highly required. Brain-derived neurotrophic factor (BDNF) plays an important role in maintaining the function and survival of the central nervous system. Decreased BDNF levels in tear fluid can be seen in glaucoma patients, which indicates that BDNF can be regarded as a novel biomarker for glaucoma. Conventional ELISA is the standard method to measure the BDNF level, but the multi-step operation and strict storage conditions limit its usage in point-of-care settings. Herein, a one-step and a portable glaucoma detection method was developed based on the lateral flow assay (LFA) to quantify the BDNF concentration in artificial tear fluids. The results of the LFA were analyzed by using a portable and low-cost system consisting of a smartphone camera and a dark readout box fabricated by 3D printing. The concentration of BDNF was quantified by analyzing the colorimetric intensity of the test line and the control line. This assay yields reliable quantitative results from 25 to 300 pg mL-1 with an experimental detection limit of 14.12 pg mL-1. The LFA shows a high selectivity for BDNF and high stability in different pH environments. It can be readily adapted for sensitive and quantitative testing of BDNF in a point-of-care setting. The BDNF LFA strip shows it has great potential to be used in early glaucoma detection.