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

Revolutionizing biosensing with wearable microneedle patches: innovations and applications

Wearable microneedle (MN) patches have emerged as a transformative platform for biosensing, offering a minimally invasive and user-friendly approach to real-time health monitoring and disease diagnosis. Primarily designed to access interstitial fluid (ISF) through shallow skin penetration, MNs enable precise and continuous sampling of biomarkers such as glucose, lactate, and electrolytes. Additionally, recent innovations have integrated MN arrays with microfluidic and porous structures to support sweat-based analysis, where MNs act as structural or functional components in hybrid wearable systems. This review explores the design, fabrication, and functional integration of MNs into wearable devices, highlighting advances in multi-analyte detection, wireless data transmission, and self-powered sensing. Challenges related to material biocompatibility, sensor stability, scalability, and user variability are addressed, alongside emerging opportunities in microfluidics, artificial intelligence, and soft materials. Overall, MN-based biosensing platforms are poised to redefine personalized healthcare by enabling dynamic, decentralized, and accessible health monitoring.

Advanced transdermal drug delivery system: A comprehensive review of microneedle technologies, novel designs, diverse applications, and critical challenges

Transdermal drug delivery presents numerous advantages over conventional administration routes, including non-invasiveness, enhanced patient adherence, circumvention of hepatic first-pass metabolism, self-administration capabilities, controlled release, and increased bioavailability. Nevertheless, the barrier function of stratum corneum limits this strategy to molecules possessing requisite physicochemical attributes. To expand the field of transdermal delivery, researchers have pioneered physical enhancement techniques, with micron-sized needles emerging as a particularly promising platform for the transdermal and intradermal delivery of therapeutic agents across a spectrum of molecular sizes. Microneedles function by disrupting the skin's integrity, generating microchannels that facilitate efficient drug permeation. This innovative technology boasts a captivating profile characterized by non-invasive drug delivery, enhanced efficacy and onset time, improved patient acceptability, self-administration possibilities, and precise dosing capabilities. Consequently, both academic institutions and industry have invested substantial resources in the development of microneedle systems for pharmaceutical delivery. This comprehensive review elucidates the multifaceted aspects of microneedle technology, encompassing its historical evolution, diverse materials, innovative designs, fabrication methodologies, and characterization techniques. The review extends to various microneedle types, including solid, hollow, coated, dissolving, swelling, and porous microneedles, as well as cutting-edge designs such as stimulus-responsive, iontophoresis-assisted, and bionic microneedles. Furthermore, we explore microneedle applications in vaccination, targeted delivery, and the administration of biologics, long-acting therapeutic agents, and cosmetics. Critical challenges in microneedle development, including dimensional considerations, safety concerns, acceptability factors, production scalability, regulatory hurdles, and sustainability issues, are thoroughly addressed, alongside a presentation of future prospects in this rapidly evolving field.

Optimizing Solid Microneedle Design: A Comprehensive ML-Augmented DOE Approach

Microneedles (MNs), that is, a matrix of micrometer-scale needles, have diverse applications in drug delivery, skincare therapy, and health monitoring. MNs offer a minimally invasive alternative to hypodermic needles, characterized by rapid and painless procedures, cost-effective fabrication methods, and reduced tissue damage. This study explores four MN designs, cone-shaped, tapered cone-shaped, pyramidal with a square base, and pyramidal with a triangular-shaped base, and their optimization based on predefined criteria. The workflow encompasses three loading conditions: compressive load during insertion, critical buckling load, and bending loading resulting from incorrect insertion. Geometric parameters such as base radius/width, tip radius/width, height, and tapered angle tip influence the output criteria, namely, total deformation, critical buckling loads, factor of safety (FOS), and bending stress. The comprehensive framework employing a design of experiment approach within the ANSYS workbench toolbox establishes a mathematical model and a response surface fitting model. The resulting regression model, sensitivity chart, and response curve are used to create a multiobjective optimization problem that helps achieve an optimized MN geometrical design across the introduced four shapes, integrating machine learning (ML) techniques. This study contributes valuable insights into a potential ML-augmented optimization framework for MNs via needle designs to stay durable for various physiologically relevant conditions.

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.

Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology

Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.