Optical biosensors - Illuminating the path to personalized drug dosing

Optical biosensors are low-cost, sensitive and portable devices that are poised to revolutionize the medical industry. Healthcare monitoring has already been transformed by such devices, with notable recent applications including heart rate monitoring in smartwatches and COVID-19 lateral flow diagnostic test kits. The commercial success and impact of existing optical sensors has galvanized research in expanding its application in numerous disciplines. Drug detection and monitoring seeks to benefit from the fast-approaching wave of optical biosensors, with diverse applications ranging from illicit drug testing, clinical trials, monitoring in advanced drug delivery systems and personalized drug dosing. The latter has the potential to significantly improve patients' lives by minimizing toxicity and maximizing efficacy. To achieve this, the patient's serum drug levels must be frequently measured. Yet, the current method of obtaining such information, namely therapeutic drug monitoring (TDM), is not routinely practiced as it is invasive, expensive, time-consuming and skilled labor-intensive. Certainly, optical sensors possess the capabilities to challenge this convention. This review explores the current state of optical biosensors in personalized dosing with special emphasis on TDM, and provides an appraisal on recent strategies. The strengths and challenges of optical biosensors are critically evaluated, before concluding with perspectives on the future direction of these sensors

Ten years of lateral flow immunoassay technique applications: Trends, challenges and future perspectives

The Lateral Flow Immunoassay (LFIA) is by far one of the most successful analytical platforms to perform the on-site detection of target substances. LFIA can be considered as a sort of lab-in-a-hand and, together with other point-of-need tests, has represented a paradigm shift from sample-to-lab to lab-to-sample aiming to improve decision making and turnaround time. The features of LFIAs made them a very attractive tool in clinical diagnostic where they can improve patient care by enabling more prompt diagnosis and treatment decisions. The rapidity, simplicity, relative cost-effectiveness, and the possibility to be used by nonskilled personnel contributed to the wide acceptance of LFIAs. As a consequence, from the detection of molecules, organisms, and (bio)markers for clinical purposes, the LFIA application has been rapidly extended to other fields, including food and feed safety, veterinary medicine, environmental control, and many others. This review aims to provide readers with a 10-years overview of applications, outlining the trends for the main application fields and the relative compounded annual growth rates. Moreover, future perspectives and challenges are discussed

Hybrid point-of-care devices for visual detection of biomarkers and drugs

Early diagnostics is a crucial part of clinical practice offering a rapid and convenient way to investigate and quantify the presence of key biomarkers related to specific pathologies and increasing the chance of successful treatments. In this regard, point-of-care testing (POCT) shows several advantages enabling simple and rapid analyses, allowing for real-time results, and permitting home testing. Metallic nanoparticles (NPs), like gold NPs (AuNPs), can be beneficially integrated into POC devices thanks to their tunable plasmonic properties which provide a naked-eye read-out. Moreover, the high sensitivity of NPs enables the detection of biomarkers in non-invasive fluids where the concentrations are typically low. These biofluids, like saliva and urine, are functionally equivalent to serum in reflecting the physiological state of the body, whilst they are easier to handle, collect, and store. In this thesis, I first reported the design and development of a colorimetric strategy based on the morphological change of multibranched plasmonic AuNPs, aimed at detecting glucose in saliva. The sensing approach relied on a target-induced reshaping process which involves the oxidation of the NP tips and the transformation into a spherical shape, characterized by a naked-eye detectable blue-to-pink color change. The platform proved to be beneficial in the early and non-invasive diagnosis of hyperglycemia. The successful technological transfer on a solid substrate paved the way for the realization of a dipstick prototype for home testing. Then, the strategy was adapted to other biomarkers, leading to the development of a multiplexing test for the simultaneous detection of three salivary analytes (cholesterol, glucose, and lactate). This multiplexing assay enabled to save reagents, costs, and time, whilst increasing the overall clinical value of the test. Exploiting the microfluidics applied on a paper sheet, I realized a monolithic and fully integrated POC device, through a low-cost and fast CO2 laser cutter. The platform showed excellent selectivity and multiplexing ability, with negligible interferences. The second part of my thesis was focused on the development of POC devices for the detection of anticancer drug contaminations in water solutions and urine samples. Antiblastic agents have revealed high toxicity for the exposed healthcare workers who prepare and administer these drugs in occupational environments. Hence, continuous monitoring is highly required, and POCT shows tremendous potential in this context. With this aim, I realized a lateral-flow (LF) device for the assessment of doxorubicin contamination, using the fluorescent properties of the drug for naked-eye detection. The pharmacological recognition of the DNA probe was exploited to overcome the lack of anti-doxorubicin antibodies. The highly sensitive strategy was successfully adapted to a real urine sample, without resorting to complex pretreatment procedures. Then, I developed a competitive LF device for the detection of methotrexate (MTX). AuNPs were employed as the label molecules and the pharmacological competition of folic acid and MTX for the capture enzyme was exploited as the recognition mechanism, instead of costly antibodies. Despite the sensitivity requires further improvements, the strategy showed fast and reliable results, demonstrating a high potential for workers’ safety control

Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing

We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted

Review—Lab-in-a-Mouth and Advanced Point-of-Care Sensing Systems: Detecting Bioinformation from the Oral Cavity and Saliva

Cavitas sensors and point-of-need sensors capable of providing physical and biochemical information from the oral cavity and saliva have attracted great attention because they offer remarkable advantages for noninvasive sensing systems. Herein, we introduce the basic anatomy and physiology of important body cavities to understand their characteristics as it is a pivotal foundation for the successful development of in-mouth devices. Next, the advanced development in lab-in-a-mouth sensors and point-of-need sensors for analyzing saliva are explained. In addition, we discuss the integrations of artificial intelligence and electronic technologies in smart sensing networks for healthcare systems. This review ends with a discussion of the challenges, future research trends, and opportunities in relevant disciplines. Mouthguard-based sensors and conventional salivary sensing devices will continue to be significant for the progress in the next-generation sensing technologies and smart healthcare systems.ope

Deep learning of HIV field-based rapid tests

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans-experienced nurses and newly trained community health worker staff-and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections