Malignancies and Biosensors: A Focus on Oral Cancer Detection through Salivary Biomarkers

Oral cancer is among the deadliest types of malignancy due to the late stage at which it is usually diagnosed, leaving the patient with an average five-year survival rate of less than 50%. The booming field of biosensing and point of care diagnostics can, in this regard, play a major role in the early detection of oral cancer. Saliva is gaining interest as an alternative biofluid for non-invasive diagnostics, and many salivary biomarkers of oral cancer have been proposed. While these findings are promising for the application of salivaomics tools in routine practice, studies on larger cohorts are still needed for clinical validation. This review aims to summarize the most recent development in the field of biosensing related to the detection of salivary biomarkers commonly associated with oral cancer. An introduction to oral cancer diagnosis, prognosis and treatment is given to define the clinical problem clearly, then saliva as an alternative biofluid is presented, along with its advantages, disadvantages, and collection procedures. Finally, a brief paragraph on the most promising salivary biomarkers introduces the sensing technologies commonly exploited to detect oral cancer markers in saliva. Hence this review provides a comprehensive overview of both the clinical and technological advantages and challenges associated with oral cancer detection through salivary biomarkers

Fully Integrated Biochip Platforms for Advanced Healthcare

Recent advances in microelectronics and biosensors are enabling developments of innovative biochips for advanced healthcare by providing fully integrated platforms for continuous monitoring of a large set of human disease biomarkers. Continuous monitoring of several human metabolites can be addressed by using fully integrated and minimally invasive devices located in the sub-cutis, typically in the peritoneal region. This extends the techniques of continuous monitoring of glucose currently being pursued with diabetic patients. However, several issues have to be considered in order to succeed in developing fully integrated and minimally invasive implantable devices. These innovative devices require a high-degree of integration, minimal invasive surgery, long-term biocompatibility, security and privacy in data transmission, high reliability, high reproducibility, high specificity, low detection limit and high sensitivity. Recent advances in the field have already proposed possible solutions for several of these issues. The aim of the present paper is to present a broad spectrum of recent results and to propose future directions of development in order to obtain fully implantable systems for the continuous monitoring of the human metabolism in advanced healthcare applications

Enhancing Proprioception and Regulating Cognitive Load in Neurodiverse Populations through Biometric Monitoring with Wearable Technologies

This paper considers the realm of wearable technologies and their prospective applications for individuals with neurodivergent conditions, specifically Autism Spectrum Disorders (ASDs). The study undertakes a multifaceted analysis that encompasses biomarker sensing technologies, AI-driven biofeedback mechanisms, and haptic devices, focusing on their implications for enhancing proprioception and social interaction among neurodivergent populations. While wearables offer a range of opportunities for societal advancement, a discernable gap remains: a scarcity of consumer-oriented applications tailored to the unique physiological and psychological needs of these individuals. Key takeaways underscore the emergent promise of tailored auditory stimuli in workplace dynamics and the efficacy of haptic feedback in sensory substitution. The investigation concludes with an urgent call for multidisciplinary research aimed at the development of specific consumer applications, rigorous empirical validation, and an ethical framework encompassing data privacy and user consent. As the pervasiveness of technology in daily life continues to expand, the article posits that there is an imperative for future research to shift from generalized solutions to individualized applications, thereby ensuring that the spectrum of wearable technology truly accommodates the full scope of human neurodiversity

Microfluidics for Biosensing

There are 12 papers published with 8 research articles, 3 review articles and 1 perspective. The topics cover: Biomedical microfluidics Lab-on-a-chip Miniaturized systems for chemistry and life science (MicroTAS) Biosensor development and characteristics Imaging and other detection technologies Imaging and signal processing Point-of-care testing microdevices Food and water quality testing and control We hope this collection could promote the development of microfluidics and point-of-care testing (POCT) devices for biosensing

Machine Learning for Biosensors

Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and data analysis models. The potential benefits of machine learning in biosensors are discussed, including the ability to analyze large and complex data sets, to detect subtle changes in biomolecular interactions, and to provide real-time monitoring of biological processes. The challenges associated with the integration of machine learning and biosensors are also addressed, including data availability, sensor performance, and computational requirements. We further highlight the challenges and opportunities for the integration of machine learning and biosensors, including the development of portable and low-cost biosensors, and the use of machine learning algorithms for efficient data analysis. Finally, we provide an outlook on future trends and emerging technologies in the field, including the use of artificial intelligence and deep learning algorithms for biosensors, and the potential for creating a fully autonomous biosensing system

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field

Emerging and Disruptive Next-Generation Technologies for POC: Sensors, Chemistry and Microfluidics for Diagnostics

Recently, the attention paid to self-care tests and the easy and large screening of a high number of people has dramatically increased. Indeed, easy and affordable tools for the safe management of biological fluids together with self-diagnosis have emerged as compulsory requirements in this time of the COVID-19 pandemic, to lighten the pressure on public healthcare institutions and thus limiting the diffusion of infections. Obviously, other kinds of pathologies (cancer or other degenerative diseases) also continue to require attention, with progressively earlier and more widespread diagnoses. The contribution to the development of this research field comes from the areas of innovative plastic and 3D microfluidics, smart chemistry and the integration of miniaturized sensors, going in the direction of improving the performances of in vitro diagnostic (IVD) devices. In our Special Issue, we include papers describing easy strategies to identify diseases at the point-of-care and near-the-bed levels, but also dealing with innovative biomarkers, sample treatments, and chemistry processes which, in perspective, represent promising tools to be applied in the field

Correlating the Effect of Dynamic Variability in the Sensor Environment on Sensor Design

This dissertation studies the effect of biofluid dynamics on the electrochemical response of a wearable sensor for monitoring of chronic wounds. The research investigates various dynamic in vivo parameters and correlates them with experimentally measured behavior with wound monitoring as a use case. Wearable electrochemical biosensors suffer from several unaddressed challenges, like stability and sensitivity, that need to be resolved for obtaining accurate data. One of the major challenges in the use of these sensors is continuous variation in biofluid composition. Wound healing is a dynamic process with wound composition changing continuously. This dissertation investigates the effects of several in vivo biochemical and environmental parameters on the sensor response to establish actionable correlations. Real-time assessment of wound healing was carried out through longitudinal monitoring of uric acid and other wound fluid characteristics. A textile sensor was designed using a simple fabrication approach combining conductive inks with a polymeric substrate, for conformal contact with the wound bed. A −1 cm−2, establishing the applicability of the sensor for measurements in the physiologically relevant range. The sensor was also found to be stable for a period of 3 days when subjected to physiological and elevated temperatures (37oC and 40oC) confirming its relevance for long-term monitoring. A direct correlation between sensor response and the dynamic parameters was seen, with the results showing a ~20% deviation from the accurate UA reading. The results confirmed that as a consequence of these parameters temporally changing in the wound environment, the sensor response will be altered. The work develops mathematical models correlating this effect on sensor response to allow for real-time sensor calibration. The clinical validation studies established the feasibility of UA measurement by the developed electrochemical sensor and derive correlations between the wound chronicity and UA levels. The protocols developed in this work for the design, fabrication, and calibration of the sensor to correct for the dynamic in vivo behavior can be extended to any wearable sensor for improved accuracy