Wearable Sensors for the Detection of Biomarkers for Wound Infection

Sensing patches for biomarker identification in skin-derived biofluids

In conventional clinical disease diagnosis and screening based on biomarker detection, most analysis samples are collected from serum, blood. However, these invasive collection methods require specific instruments, professionals, and may lead to infection risks. Additionally, the diagnosis process suffers from untimely results. The identification of skin-related biomarkers plays an unprecedented role in early disease diagnosis. More importantly, these skin-mediated approaches for collecting biomarker-containing biofluid samples are noninvasive or minimally invasive, which is more preferable for point-of-care testing (POCT). Therefore, skin-based biomarker detection patches have been promoted, owing to their unique advantages, such as simple fabrication, desirable transdermal properties and no requirements for professional medical staff. Currently, the skin biomarkers extracted from sweat, interstitial fluid (ISF) and wound exudate, are achieved with wearable sweat patches, transdermal MN patches, and wound patches, respectively. In this review, we detail these three types of skin patches in biofluids collection and diseases-related biomarkers identification. Patch classification and the corresponding manufacturing as well as detection strategies are also summarized. The remaining challenges in clinical applications and current issues in accurate detection are discussed for further advancement of this technology (Scheme 1).

Portable Photoacoustic Analytical System Combined with Wearable Hydrogel Patch for pH Monitoring in Chronic Wounds

Timely diagnosis, monitoring, and management of chronic wounds play crucial roles in improving patients' quality of life, but clinical evaluation of chronic wounds is still ambiguous and relies heavily on the experience of clinician, resulting in increased social and financial burden and delay of optimal treatment. During the different stages of the healing process, specific and dynamic changes of pH values in the wound exudate can be used as biomarkers to reflect the wound status. Herein, a pH-responsive agent with well-behaved photoacoustic (PA) properties, nitrazine yellow (NY), was incorporated in poly(vinyl alcohol)/sucrose (PVA/Suc) hydrogel to construct a wearable pH-sensing patch (PVA/Suc/NY hydrogel) for monitoring of pH values during chronic wound healing. According to Rosencwaig-Gersho theory and the combination of 3D printing technology, the PA chamber volume and chopping frequency were systematically optimized to improve the sensitivity of the PA analytical system. The prepared PVA/Suc/NY hydrogel patch had excellent mechanical properties and flexibility and could maintain conformal contact with skin. Moreover, combined with the miniaturized PA analytical device, it had the potential to detect pH values (5.0-9.0) free from the color interference of blood and therapeutic drugs, which provides a valuable strategy for wound pH value monitoring by PA quantitation. This strategy of combining the wearable hydrogel patch with portable PA analysis offers broad new prospects for the treatment and management of chronic wounds due to its features of simple operation, time savings, and anti-interference.

Comparative analysis of electrochemical and optical sensors for detection of chronic wounds biomarkers: A review

Chronic wounds (CW) present a significant healthcare challenge due to their prolonged healing time and associated complications. To effectively treat these wounds and prevent further deterioration, monitoring their healing progress is crucial. Traditional wound assessment methods relying on visual inspection and subjective evaluation are prone to inter-observer variability. Biomarkers play a critical role in objectively evaluating wound status and predicting healing outcomes, providing quantitative measures of wound healing progress, inflammation, infection, and tissue regeneration. Recent attention has been devoted to identifying and validating CW biomarkers. Various studies have investigated potential biomarkers, including growth factors, cytokines, proteases, and extracellular matrix components, shedding light on the complex molecular and cellular processes within CW. This knowledge enables a more targeted and personalized approach to wound management. Accurate and sensitive techniques are necessary for detecting CW biomarkers. Thus, this review compares and discusses the use of electrochemical and optical sensors for biomarker determination. The advantages and disadvantages of these sensors are highlighted. Differences in detection capabilities and characteristics such as non-invasiveness, portability, high sensitivity, specificity, simplicity, cost-effectiveness, compatibility with point-of-care applications, and real-time monitoring of wound biomarkers will be pointed out and compared. In summary, this work provides an overview of CW, explores the emerging field of CW biomarkers, and discusses methods for detecting these biomarkers, with a specific focus on optical and electrochemical sensors. The potential of further research and development in this field for advancing wound care and improving patient outcomes will also be noted.

Lab-on-a-Fiber Wearable Multi-Sensor for Monitoring Wound Healing

Chronic wounds are regarded as a silent epidemic, affecting 1-2% of the population and representing 2-4% of healthcare expenses. The current methods used to assess the wound healing process are based on the visual evaluation of physical parameters. This work aims to design a wearable non-invasive device capable of evaluating three parameters simultaneously: the pH and the levels of glucose and matrix metalloproteinase (MMP) present in the wound exudate. The device is composed of three independent polymer optical fibers functionalized with fluorescent-based sensing chemistries specific to the targeted analytes. Each fiber is characterized in terms of detection sensitivity and selectivity confirming their suitability for monitoring the targeted parameters in ranges relevant to the wound environment. The selectivity and robustness of the multi-sensing device are confirmed with analyses using complex solutions with different pH levels (5, 6, and 7), different concentrations of glucose (1.25, 2.5, and 5 mm), and MMP (1.25, 2.5, and 5 µg mL-1 ). Given the simple set-up, the affordability of the materials used and the possibility of detecting additional parameters relevant to wound healing, such multi-sensing fiber-based devices could pave the way for novel non-invasive wearable tools enabling the assessment of wound healing from the molecular perspective.

A light-sensitive protein-based wearable pH biometer

Bacteriorhodopsin is a biological material with excellent photosensitivity properties. It can directly convert optical signals into electrical signals and is widely used in various biosensors. Here, we present a bR-based wearable pH biometer that can be used to monitor wound infection. The mechanism of the pH-sensitive effect of the bR electrode is explained, which generates a transient photovoltage under light irradiation and a negative photovoltage when the lamp is turned off. Since the photoelectric signal of bR is affected by different pH values, the photovoltage is changed by adjusting the pH value. The ratio (Vn/Vp) of negative photovoltage (Vn) to positive photovoltage (Vp) has a good linear relationship (R2 = 0.9911) in the pH range of 4.0-10.0. In vitro experiments using rats as a model confirmed that this wearable pH biometer can monitor pH changes that occur in wound infection.

Point-of-care detection devices for wound care and monitoring

Healthcare resources are heavily burdened by infections that impede the wound-healing process. A wide range of advanced technologies have been developed for detecting and quantifying infection biomarkers. Finding a timely, accurate, non-invasive diagnostic alternative that does not require a high level of training is a critical step toward arresting common clinical patterns of wound health decline. There is growing interest in the development of innovative diagnostics utilizing a variety of emerging technologies, and new biomarkers have been investigated as potential indicators of wound infection. In this review, we summarize diagnostics available for wound infection, including those used in clinics and still under development.

A 3D-printed millifluidic device for triboelectricity-driven pH sensing based on ZnO nanosheets with super-Nernstian response

This paper suggests a straightforward and rapid fabrication method applying the integration of 3D printing and triboelectric nanogenerator (TENG) technologies to realize milli/microfluidic multipurpose devices. The proposed liquid-solid TENG device is served as an energy harvester and sensor at the same time with flexibility in operation modes. Accordingly, an innovative ethylene vinyl acetate (EVA)-made millifluidic pH sensor is fabricated based on zinc oxide nanosheets as a showcase of the functional adaptability of the ubiquitous device, and its performance is analyzed and compared with contemporary electrochemical pH sensors. High crystallinity of the nanosheets with an incline to (103) orientation in parallel with high levels of oxygen vacancies provides capacity for surface charge accumulation at the nanosheet-aqueous solution interface and the ensuing ultrahigh sensitivity of the triboelectric sensor. The millichannel is optimized in terms of sensing surface area, flow rate, and hydrophobicity properties by opting for appropriate geometry, TENG operation modes, and materials. Despite the finding that quasi-single-electrode mode TENG experiences a higher response (8.12 × Nernst limit) in comparison with quasi-contact-separation configuration (4.14 × Nernst limit), the latter enjoys superior linearity, stability, repeatability, reproducibility, and reliability characteristics corresponding to R² of 98.93%, drift rate of 13 mV/h, relative standard deviation (RSD) of 1.23% in third hysteresis loop, 2.24%, and maximum standard error of ±0.2 pH units across multiple trials, respectively, in a wide pH range of 2-13. Time- and cost-effectiveness, user-friendliness, self-powering, portability, and biocompatibility of the device could be asserted as considerable advantages to open the door for feasibly realizing the new generation of real-life and point-of-care devices.

Deep learning framework for sensor array precision and accuracy enhancement

In the upcoming years, artificial intelligence is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between [Formula: see text]. 800 vectors are extracted, covering a range from to 30 to [Formula: see text]. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10[Formula: see text] on the training set and 1.22x10[Formula: see text] on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.

Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System

Smart wearable patch systems that combine biosensing and therapeutic components have emerged as promising approaches for personalized healthcare and therapeutic platforms that enable self-administered, noninvasive, user-friendly, and long-acting smart drug delivery. Sensing components can continuously monitor physiological and biochemical parameters, and the monitoring signals can be transferred to various stimuli using actuators. In therapeutic components, stimuli-responsive carrier-based drug delivery systems (DDSs) provide on-demand drug delivery in a closed-loop manner. This review provides an overview of the recent advances in smart wearable patch systems, focusing on sensing components, stimuli, and therapeutic components. Additionally, this review highlights the potential of fully integrated smart wearable patch systems for personalized medicine. Furthermore, challenges associated with the clinical applications of this system and future perspectives are discussed, including issues related to drug loading and reloading, biocompatibility, accuracy of sensing and drug delivery, and largescale fabrication.

A Systematic Review on the Advanced Techniques of Wearable Point-of-Care Devices and Their Futuristic Applications

Personalized point-of-care testing (POCT) devices, such as wearable sensors, enable quick access to health monitoring without the use of complex instruments. Wearable sensors are gaining popularity owing to their ability to offer regular and continuous monitoring of physiological data by dynamic, non-invasive assessments of biomarkers in biofluids such as tear, sweat, interstitial fluid and saliva. Current advancements have concentrated on the development of optical and electrochemical wearable sensors as well as advances in non-invasive measurements of biomarkers such as metabolites, hormones and microbes. For enhanced wearability and ease of operation, microfluidic sampling, multiple sensing, and portable systems have been incorporated with materials that are flexible. Although wearable sensors show promise and improved dependability, they still require more knowledge about interaction between the target sample concentrations in blood and non-invasive biofluids. In this review, we have described the importance of wearable sensors for POCT, their design and types of these devices. Following which, we emphasize on the current breakthroughs in the application of wearable sensors in the realm of wearable integrated POCT devices. Lastly, we discuss the present obstacles and forthcoming potentials including the use of Internet of Things (IoT) for offering self-healthcare using wearable POCT.

The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model

Introduction

Surgical Site Infection (SSI) is a common healthcare-associated infection that imposes a considerable clinical and economic burden on healthcare systems. Advances in wearable sensors and digital technologies have unlocked the potential for the early detection and diagnosis of SSI, which can help reduce this healthcare burden and lower SSI-associated mortality rates.

Methods

In this study, we evaluated the ability of a multi-modal bio-signal system to predict current and developing superficial incisional infection in a porcine model infected with Methicillin Susceptible Staphylococcus Aureus (MSSA) using a bagged, stacked, and balanced ensemble logistic regression machine learning model.

Results

Results demonstrated that the expression levels of individual biomarkers (i.e., peri-wound tissue oxygen saturation, temperature, and bioimpedance) differed between non-infected and infected wounds across the study period, with cross-correlation analysis indicating that a change in bio-signal expression occurred 24 to 31 hours before this change was reflected by clinical wound scoring methods employed by trained veterinarians. Moreover, the multi-modal ensemble model indicated acceptable discriminability to detect the presence of a current superficial incisional SSI (AUC = 0.77), to predict an SSI 24 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.80), and to predict an SSI 48 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.74).

Discussion

In sum, the results of the current study indicate that non-invasive multi-modal sensor and signal analysis systems have the potential to detect and predict superficial incisional SSIs in porcine subjects under experimental conditions.

Non-invasive biomedical sensors for early detection and monitoring of bacterial biofilm growth at the point of care

Bacterial infections have long been a serious global health issue. Biofilm formation complicates matters even more. The biofilm's extracellular polymeric substances (EPSs) matrix protects bacteria from the host's immune responses, yielding strong adhesion and drug resistance as the biofilm matures. Early bacterial biofilm detection and bacterial biofilm growth monitoring are crucial to treating biofilm-associated infections. Current detection methods are highly sensitive but not portable, are time-consuming, and require expensive equipment and complex operating procedures, limiting their use at the point of care. Therefore, there is an urgent need to develop affordable, on-body, and non-invasive biomedical sensors to continuously monitor and detect early biofilm growth at the point of care through personalized telemedicine. Herein, recent advances in developing non-invasive biomedical sensors for early detection and monitoring bacterial biofilm growth are comprehensively reviewed. First, biofilm's life cycle and its impact on the human body, such as biofilm-associated disease and infected medical devices, are introduced together with the challenges of biofilm treatment. Then, the current methods used in clinical and laboratory settings for biofilm detection and their challenges are discussed. Next, the current state of non-invasive sensors for direct and indirect detection of bacterial biofilms are summarized and highlighted with the detection parameters and their design details. Finally, commercially available products, challenges of current devices, and the further trend in biofilm detection sensors are discussed.

Recent Advances, Opportunities, and Challenges in Developing Nucleic Acid Integrated Wearable Biosensors for Expanding the Capabilities of Wearable Technologies in Health Monitoring

Wearable biosensors are becoming increasingly popular due to the rise in demand for non-invasive, real-time monitoring of health and personalized medicine. Traditionally, wearable biosensors have explored protein-based enzymatic and affinity-based detection strategies. However, in the past decade, with the success of nucleic acid-based point-of-care diagnostics, a paradigm shift has been observed in integrating nucleic acid-based assays into wearable sensors, offering better stability, enhanced analytical performance, and better clinical applicability. This narrative review builds upon the current state and advances in utilizing nucleic acid-based assays, including oligonucleotides, nucleic acid, aptamers, and CRISPR-Cas, in wearable biosensing. The review also discusses the three fundamental blocks, i.e., fabrication requirements, biomolecule integration, and transduction mechanism, for creating nucleic acid integrated wearable biosensors.