Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Self-Sustaining Triboelectric Nanosensors for Real-Time Urine Analysis in Smart Toilets

Healthcare has undergone a revolutionary shift with the advent of smart technologies, and smart toilets (STs) are among the innovative inventions offering non-invasive continuous health monitoring. The present technical challenges toward this development include limited sensitivity of integrated sensors, poor stability, slow response and the requirement external energy supply alongside manual sample collection. In this article, triboelectric nanosensor array (TENSA) is introduced featuring electrodes crafted from laser-induced 3D graphene with functional polymers like polystyrene, polyimide, and polycaprolactone for real-time urine analysis while generating 50 volts output via urine droplet-based triboelectrification. Though modulating interfacial double-layer capacitance, these sensors exhibit exceptional sensitivity and selectivity in detecting a broad spectrum of urinary biomarkers, including ions, glucose, and urea with a classification precision of 95% and concentration identification accuracy of up to 0.97 (R2), supported by artificial neural networks. Upon exposure to urine samples containing elevated levels of Na+, K+, and NH4 +, a notable decrease (ranging from 32% to 68%) is observed in output voltages. Conversely, urea induces an increase up to 13%. Experimental validation confirms the stability, robustness, reliability, and reproducibility of TENSA, representing a significant advancement in healthcare technology, offering the potential for improved disease management and prevention strategies.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Accurate Cancer Screening and Prediction of PD-L1-Guided Immunotherapy Efficacy Using Quantum Dot Nanosphere Self-Assembly and Machine Learning

Accurate quantification of exosomal PD-L1 protein in tumors is closely linked to the response to immunotherapy, but robust methods to achieve high-precision quantitative detection of PD-L1 expression on the surface of circulating exosomes are still lacking. In this work, we developed a signal amplification approach based on aptamer recognition and DNA scaffold hybridization-triggered assembly of quantum dot nanospheres, which enables bicolor phenotyping of exosomes to accurately screen for cancers and predict PD-L1-guided immunotherapeutic effects through machine learning. Through DNA-mediated assembly, we utilized two aptamers for simultaneous ultrasensitive detection of exosomal antigens, which have synergistic roles in tumor diagnosis and treatment prediction, and thus, we achieved better sample classification and prediction through machine-learning algorithms. With a drop of blood, we can distinguish between different cancer patients and healthy individuals and predict the outcome of immunotherapy. This approach provides valuable insights into the development of personalized diagnostics and precision medicine.

Methods gold standard in clinic millifluidics multiplexed extended gate field-effect transistor biosensor with gold nanoantennae as signal amplifiers

We present a portable multiplexed biosensor platform based on the extended gate field-effect transistor and demonstrate its amplified response thanks to gold nanoparticle-based bioconjugates introduced as a part of the immunoassay. The platform comprises a disposable chip hosting an array of 32 extended gate electrodes, a readout module based on a single transistor operating in constant charge mode, and a multiplexer to scan sensing electrodes one-by-one. Although employing only off-the-shelf electronic components, our platform achieves sensitivities comparable to fully customized nanofabricated potentiometric sensors. In particular, it reaches a detection limit of 0.2 fM for the pure molecular assay when sensing horseradish peroxidase-linked secondary antibody (∼0.4 nM reached by standard microplate methods). Furthermore, with the gold nanoparticle bioconjugation format, we demonstrate ca. 5-fold amplification of the potentiometric response compared to a pure molecular assay, at the detection limit of 13.3 fM. Finally, we elaborate on the mechanism of this amplification and propose that nanoparticle-mediated disruption of the diffusion barrier layer is the main contributor to the potentiometric signal enhancement. These results show the great potential of our portable, sensitive, and cost-efficient biosensor for multidimensional diagnostics in the clinical and laboratory settings, including e.g., serological tests or pathogen screening.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

Extracellular Vesicles: Techniques and Biomedical Applications Related to Single Vesicle Analysis

Extracellular vesicles (EVs) are extensively dispersed lipid bilayer membrane vesicles involved in the delivery and transportation of molecular payloads to certain cell types to facilitate intercellular interactions. Their significant roles in physiological and pathological processes make EVs outstanding biomarkers for disease diagnosis and treatment monitoring as well as ideal candidates for drug delivery. Nevertheless, differences in the biogenesis processes among EV subpopulations have led to a diversity of biophysical characteristics and molecular cargos. Additionally, the prevalent heterogeneity of EVs has been found to substantially hamper the sensitivity and accuracy of disease diagnosis and therapeutic monitoring, thus impeding the advancement of clinical applications. In recent years, the evolution of single EV (SEV) analysis has enabled an in-depth comprehension of the physical properties, molecular composition, and biological roles of EVs at the individual vesicle level. This review examines the sample acquisition tactics prior to SEV analysis, i.e., EV isolation techniques, and outlines the current state-of-the-art label-free and label-based technologies for SEV identification. Furthermore, the challenges and prospects of biomedical applications based on SEV analysis are systematically discussed.

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

Renal Clearable Magnetic Nanoreporter for Colorimetric Urinalysis of Tumor

The convenience and availability are of great significance for the early screening of cancer. Herein, a magnetic nanoreporter with renal clearable capability and activatable catalytic activity was developed for colorimetric urinalysis of tumors. The magnetic nanoreporters were prepared by loading 3.2 nm Fe3O4 nanoparticles (NPs) and glucose oxidase (GOD) into macrophage cell-derived microvesicles (MVs) through electroporation, and these compositions serve as renal clearable catalytic reporters, synergistic catalysts, and targeted delivery carriers, respectively. The magnetic nanoreporters can convert the H2O2 in the mildly acidic tumor microenvironment into hydroxyl radicals through the synergistic catalysis of Fe3O4 NPs and GOD. Then the MVs can be disintegrated by the radicals, and ultrasmall Fe3O4 NPs will be released from the MVs at the tumor site, enabling rapid clearance of the Fe3O4 NPs into urine and a direct colorimetric urinalysis of the tumor within 4 h. The magnetic nanoreporters had good biocompatibility, and the released Fe3O4 NPs were rapidly excreted from the body, avoiding the potential toxicity. We envision that the magnetic nanoreporters can be used for convenient and rapid cancer screening.

Ultrasensitive Electrochemiluminescence Immunosensor for Bladder Marker Human Complement Factor H-Related Protein Detection

The development of noninvasive and sensitive detection methods for the early diagnosis and monitoring of bladder cancer is critical but challenging. Herein, an ultrasensitive electrochemiluminescence (ECL) immunosensor that uses Ru(bpy)₃²⁺-metal-organic framework (Ru-MOF) nanospheres and a DNA tetrahedral (TDN) probe was established for bladder cancer marker complement factor H-related protein (CFHR1) detection. The synthesized Ru(bpy)₃²⁺-metal-organic frameworks (Ru-MOFs) served as a linked substrate for immobilization of AuNPs and antibody (Ab₂) to prepare the ECL signal probe (Ru-MOF@AuNPs-Ab₂), exhibiting a stable and strengthened ECL emission. At the same time, the inherent advantages of TDN probes on the electrode as the capture probe (TDN-Ab₁) improve the accessibility of targets to probes. In the presence of CFHR1, the signal probe Ru-MOF@AuNPs-Ab₂ was modified on the electrode through immune binding, thereby obtaining an outstanding ECL signal. As expected, the developed ECL immunosensor exhibited splendid performance for CFHR1 detection in the range of 0.1 fg/mL to 10 pg/mL with a quite low detection limit of 0.069 fg/mL. By using the proposed strategy to detect CFHR1 from urine, it showed acceptable accuracy, which can effectively distinguish between bladder cancer patients and healthy samples. This work contributes to a novel, noninvasive, and accurate method for early clinical diagnosis of bladder cancer.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

Nanopore Single-molecule Analysis of Biomarkers: Providing Possible Clues to Disease Diagnosis

Biomarker detection has attracted increasing interest in recent years due to the minimally or non-invasive sampling process. Single entity analysis of biomarkers is expected to provide real-time and accurate biological information for early disease diagnosis and prognosis, which is critical to the effective disease treatment and is also important in personalized medicine. As an innovative single entity analysis method, nanopore sensing is a pioneering single-molecule detection technique that is widely used in analytical bioanalytical fields. In this review, we overview the recent progress of nanopore biomarker detection as new approaches to disease diagnosis. In highlighted studies, nanopore was focusing on detecting biomarkers of different categories of communicable and noncommunicable diseases, such as pandemic Covid-19, AIDS, cancers, neurologic diseases, etc. Various sensitive and selective nanopore detecting strategies for different types of biomarkers are summarized. In addition, the challenges, opportunities, and direction for future development of nanopore-based biomarker sensors are also discussed.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring

The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both people's needs in real life and in clinical research, the use of computer-based techniques should be considered. This paper describes the latest progress in heart rate sensors empowered by machine learning methods. The paper is based on a review of the literature and patents from recent years, and is reported according to the PRISMA 2020 statement. The most important challenges and prospects in this field are presented. Key applications of machine learning are discussed in medical sensors used for medical diagnostics in the area of data collection, processing, and interpretation of results. Although current solutions are not yet able to operate independently, especially in the diagnostic context, it is likely that medical sensors will be further developed using advanced artificial intelligence methods.

Toward a new generation of permeable skin electronics

Skin-mountable electronics are considered to be the future of the next generation of portable electronics, due to their softness and seamless integration with human skin. However, impermeable materials limit device comfort and reliability for long-term, continuous usage. The recent emergence of permeable skin-mountable electronics has attracted tremendous attention in the soft electronics field. Herein, we provide a comprehensive and systematic review of permeable skin-mountable electronics. Typical porous materials and structures are first highlighted, followed by discussion of important device properties. Then, we review the latest representative applications of breathable skin-mountable electronics, such as bioelectrical sensors, temperature sensors, humidity and hydration sensors, strain and pressure sensors, and energy harvesting and storage devices. Finally, a conclusion and future directions for permeable skin electronics are provided.

Machine learning toward high-performance electrochemical sensors

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.

Wearable Hydrogel-Based Epidermal Sensor with Thermal Compatibility and Long Term Stability for Smart Colorimetric Multi-Signals Monitoring

Hydrogel-based wearable epidermal sensors (HWESs) have attracted widespread attention in health monitoring, especially considering their colorimetric readout capability. However, it remains challenging for HWESs to work at extreme temperatures with long term stability due to the existence of water. Herein, a wearable transparent epidermal sensor with thermal compatibility and long term stability for smart colorimetric multi-signals monitoring is developed, based on an anti-freezing and anti-drying hydrogel with high transparency (over 90% transmittance), high stretchability (up to 1500%) and desirable adhesiveness to various kinds of substrates. The hydrogel consists of polyacrylic acid, polyacrylamide, and tannic acid-coated cellulose nanocrystals in glycerin/water binary solvents. When glycerin readily forms strong hydrogen bonds with water, the hydrogel exhibits outstanding thermal compatibility. Furthermore, the hydrogel maintains excellent adhesion, stretchability, and transparency after long term storage (45 days) or at subzero temperatures (-20 °C). For smart colorimetric multi-signals monitoring, the freestanding smart colorimetric HWESs are utilized for simultaneously monitoring the pH, T and light, where colorimetric signals can be read and stored by artificial intelligence strategies in a real time manner. In summary, the developed wearable transparent epidermal sensor holds great potential for monitoring multi-signals with visible readouts in long term health monitoring.

Future of Digital Assays to Resolve Clinical Heterogeneity of Single Extracellular Vesicles

Extracellular vesicles (EVs) are complex lipid membrane vehicles with variable expressions of molecular cargo, composed of diverse subpopulations that participate in the intercellular signaling of biological responses in disease. EV-based liquid biopsies demonstrate invaluable clinical potential for overhauling current practices of disease management. Yet, EV heterogeneity is a major needle-in-a-haystack challenge to translate their use into clinical practice. In this review, existing digital assays will be discussed to analyze EVs at a single vesicle resolution, and future opportunities to optimize the throughput, multiplexing, and sensitivity of current digital EV assays will be highlighted. Furthermore, this review will outline the challenges and opportunities that impact the clinical translation of single EV technologies for disease diagnostics and treatment monitoring.

Prostate cancer detection: a systematic review of urinary biosensors

BACKGROUND

Current diagnostic methods for prostate cancer are invasive and lack specificity towards aggressive forms of the disease, which can lead to overtreatment. A new class of non-invasive alternatives is under development, in which urinary biomarkers are detected using biosensing devices to offer rapid and accurate prostate cancer diagnosis. These different approaches are systematically reviewed and their potential for translation to clinical practice is evaluated.

METHODS

A systematic review of the literature was performed in May 2021 using PubMed Medline database, Embase, and Web of Science. The objective was to review the structural designs and performance of biosensors tested on urine samples from patients with prostate cancer.

RESULTS

A total of 76 records were identified. After screening and eligibility, 14 articles were included and are discussed in this paper. The biosensors were discussed based on the target biomarkers and detection technologies used, as well as the results of the clinical studies. Most of the works reported good discrimination between patients with prostate cancer and controls.

CONCLUSIONS

This review highlights the potential of urinary biosensors for non-invasive prostate cancer detection. However, clinical studies have so far only been conducted on small cohorts of patient, with large scale trials still needed to validate the proposed approaches. Overall, the consensus arising from the proof of concepts studies reviewed here, is that an adequate combination of biomarkers into multiplex biosensor platforms is required to achieve accurate diagnostic tests. Furthermore, whether such devices can discriminate between aggressive and indolent cancer has not yet been addressed, because it entails optimized biomarkers panels and long-term clinical trials.

A New Strategy for Microbial Taxonomic Identification through Micro-Biosynthetic Gold Nanoparticles and Machine Learning

Microorganisms can serve as biological factories for the synthesis of inorganic nanomaterials that can become useful as nanocatalysts, energy-harvesting-storage components, antibacterial agents, and biomedical materials. Herein, the development of biosynthesis of inorganic nanomaterials into a simple, stable, and accurate strategy for distinguishing microorganisms from multiple classification levels (i.e., kingdom, order, genus, and species) without gene amplification, biochemical testing, or target recognition is reported. Gold nanoparticles (AuNPs) biosynthesized by different microorganisms differ in color of the solution, and their features can be characterized, including the particle size, the surface plasmon resonance (SPR) spectrum, and the surface potential. The inter-relation between the features of micro-biosynthetic AuNPs and the classification of microorganisms are exploited at different levels through machine learning to establish a taxonomic model. This model agrees well with traditional classification methods that offers a new strategy for microbial taxonomic identification. The underlying mechanism of this strategy is related to the biomolecules produced by different microorganisms including glucose, glutathione, and nicotinamide adenine dinucleotide phosphate-dependent reductase that regulate the features of micro-biosynthetic AuNPs. This work broadens the application of biosynthesis of inorganic materials through micro-biosynthetic AuNPs and machine learning, which holds great promise as a tool for biomedical research.

Powering smart contact lenses for continuous health monitoring: Recent advancements and future challenges

As the tear is noninvasively and continuously available, it has been turned into a convenient biological interface as a wearable medical device for out-of-hospital and self-monitoring applications. Recent progress in integrated circuits (ICs) and biosensors coupled with wireless data communication techniques have led to the implementation of smart contact lenses that can continuously sample tear fluid, analyze physiological conditions, and wirelessly transmit data to an electronic device such as smartphone, which can send data to relevant healthcare units. Continuous analyte monitoring is one of the significant characteristics of wearable biosensors. However, despite several advantages over other on-skin wearable medical devices, batteries cannot be incorporated on smart contact lenses for continuous electrical power supply due to the limited area. Herein, we review the progress of power delivery techniques of smart contact lenses for the first time. Different approaches, including wireless power transmission (WPT), biofuel cells, supercapacitors, flexible batteries, wired connections, and hybrid methods, are thoroughly discussed to understand the principles of self-sustainable contact lens biosensors comprehensively. Additionally, recent progress in contact lens biosensors is reviewed in detail, thereby providing the prospects for further developments of smart contact lenses as a common biosensing platform for various disease monitoring and diagnostic applications.

SARS-CoV-2 Variant Screening Using a Virus-Receptor-Based Electrical Biosensor

SARS-CoV-2 variants are of particular interest because they can potentially increase the transmissibility and virulence of COVID-19 or reduce the effectiveness of available vaccines. However, screening SARS-CoV-2 variants is a challenge because biosensors target viral components that can mutate. One promising strategy is to screen variants via angiotensin-converting enzyme 2 (ACE2), a virus receptor shared by all known SARS-CoV-2 variants. Here we designed a highly sensitive and portable COVID-19 screening biosensor based on the virus receptor. We chose a dual-gate field-effect transistor to overcome the low sensitivity of virus-receptor-based biosensors. To optimize the biosensor, we introduced a synthetic virus that mimics the important features of SARS-CoV-2 (size, bilayer structure, and composition). The developed biosensor successfully detected SARS-CoV-2 in 20 min and showed sensitivity comparable to that of molecular diagnostic tests (∼165 copies/mL). Our results indicate that a virus-receptor-based biosensor can be an effective strategy for screening infectious diseases to prevent pandemics.

Ratiometric 3D DNA Machine Combined with Machine Learning Algorithm for Ultrasensitive and High-Precision Screening of Early Urinary Diseases

Urinary extracellular vesicles (uEVs) have received considerable attention as a potential biomarker source for the diagnosis of urinary diseases. Present studies mainly focus on the discovery of biomarkers based on high-throughput proteomics, while limited efforts have been paid to applying the uEVs' biomarkers for the diagnosis of early urinary disease. Herein, we demonstrate a diagnosis protocol to realize ultrasensitive detection of uEVs and accurate classification of early urinary diseases, by combing a ratiometric three-dimensional (3D) DNA machine with machine learning (ML). The ratiometric 3D DNA machine platform is constructed by conjugating a padlock probe (PLP) containing cytosine-rich (C-rich) sequences, anchor strands, and nucleic-acid-stabilized silver nanoclusters (DNA_AgNCs) onto the magnetic nanoparticles (MNPs). The competitive binding of uEVs with the aptamer releases the walker strand, thus the ratiometric 3D DNA machine was activated to undergo an accurate amplification reaction and produce a ratiometric fluorescence signal. The present biosensor offers a detection limit of 9.9 × 10³ particles mL^-1 with a linear range of 10⁴-10⁸ particles mL^-1 for uEVs. Two ML algorithms, K-nearest neighbor (KNN) and support vector machine (SVM), were subsequently applied for analyzing the correlation between the sensing signals of uEV multibiomarkers and the clinical state. The disease diagnostic accuracy of optimal biomarker combination reaches 95% and 100% by analyzing with KNN and SVM, and the disease type classification exhibits an accuracy of 94.7% and 89.5%, respectively. Moreover, the protocol results in 100% accurate visual identification of clinical uEV samples from individuals with disease or as healthy control by a t-distributed stochastic neighbor embedding (tSNE) algorithm.

Multiplexed analysis of small extracellular vesicle-derived mRNAs by droplet digital PCR and machine learning improves breast cancer diagnosis

Breast cancer has become the leading cause of global cancer incidence and a serious threat to women's health. Accurate diagnosis and early treatment are of great importance to prognosis. Although clinically used diagnostic approaches can be used for cancer screening, accurate diagnosis of breast cancer is still a critical unmet need. Here, we report a 4-plex droplet digital PCR technology for simultaneous detection of four small extracellular vesicle (sEV)-derived mRNAs (PGR, ESR1, ERBB2 and GAPDH) in combination with machine learning (ML) algorithms to improve breast cancer diagnosis. We evaluate the diagnsotic results with and without the assistance of the ML models. The results indicate that ML-assisted analysis exhibits higher diagnostic performance even using a single marker for breast cancer diagnosis, and demonstrate improved diagnostic performance under the best combination of biomarkers and suitable ML diagnostic model. Therefore, multiple sEV-derived mRNAs analysis coupled with ML not only provides the best combination of markers for breast cancer diagnosis, but also significantly improves the diagnostic efficiency of breast cancer.

A Rapid and Facile Separation-Detection Integrated Strategy for Exosome Profiling Based on Boronic Acid-Directed Coupling Immunoaffinity

Exosomes are a promising noninvasive tumor biomarker for cancer diagnosis and classification. However, efficient capture and precise analysis of exosomes in complex biological samples remain challenging. Here, sensitive profiling of exosomes with an integrated separation-detection strategy of 37 min is performed based on boronic acid-directed coupling immunoaffinity. The modification of g-C₃N₄ nanosheets with boronic acid (BCNNS) contributes to antibody binding under physiological conditions, which is accompanied by fluorescence enhancement. When exosomes are captured by an antibody equipped with BCNNS, a decrease in fluorescence can be induced; moreover, using the dispersion property of BCNNS, the exosomes can be separated by a simple centrifugation step. The protocol shows a favorable sensitivity with a detection limit of 2484 particles/mL. By changing only the fused antibody, exosome phenotype information profiling can be achieved, and exosomes derived from four different cell lines (HeLa, HepG2, MCF-7, and MCF-10A) can be successfully distinguished. More significantly, the positive prediction accuracy results reach 100% for serum samples from different individuals and have the advantage of multiple parameters; thus, the method has great potential in noninvasive diagnosis and point-of-care testing.

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

Contemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real‐time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye‐view of their characteristics, functions, and applications. The review also presents the state‐of‐the‐art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI‐assisted support systems relating to clinical decisions are presented and discussed.

Cancer nanotechnology: current status and perspectives

Modern medicine has been waging a war on cancer for nearly a century with no tangible end in sight. Cancer treatments have significantly progressed, but the need to increase specificity and decrease systemic toxicities remains. Early diagnosis holds a key to improving prognostic outlook and patient quality of life, and diagnostic tools are on the cusp of a technological revolution. Nanotechnology has steadily expanded into the reaches of cancer chemotherapy, radiotherapy, diagnostics, and imaging, demonstrating the capacity to augment each and advance patient care. Nanomaterials provide an abundance of versatility, functionality, and applications to engineer specifically targeted cancer medicine, accurate early-detection devices, robust imaging modalities, and enhanced radiotherapy adjuvants. This review provides insights into the current clinical and pre-clinical nanotechnological applications for cancer drug therapy, diagnostics, imaging, and radiation therapy.

Urinary MicroRNA-Based Diagnostic Model for Central Nervous System Tumors Using Nanowire Scaffolds

There are no accurate mass screening methods for early detection of central nervous system (CNS) tumors. Recently, liquid biopsy has received a lot of attention for less-invasive cancer screening. Unlike other cancers, CNS tumors require efforts to find biomarkers due to the blood-brain barrier, which restricts molecular exchange between the parenchyma and blood. Additionally, because a satisfactory way to collect urinary biomarkers is lacking, urine-based liquid biopsy has not been fully investigated despite the fact that it has some advantages compared to blood or cerebrospinal fluid-based biopsy. Here, we have developed a mass-producible and sterilizable nanowire-based device that can extract urinary microRNAs efficiently. Urinary microRNAs from patients with CNS tumors (n = 119) and noncancer individuals (n = 100) were analyzed using a microarray to yield comprehensive microRNA expression profiles. To clarify the origin of urinary microRNAs of patients with CNS tumors, glioblastoma organoids were generated. Glioblastoma organoid-derived differentially expressed microRNAs (DEMs) included 73.4% of the DEMs in urine of patients with parental tumors but included only 3.9% of those in urine of noncancer individuals, which suggested that many CNS tumor-derived microRNAs could be identified in urine directly. We constructed the diagnostic model based on the expression of the selected microRNAs and found that it was able to differentiate patients and noncancer individuals at a sensitivity and specificity of 100 and 97%, respectively, in an independent dataset. Our findings demonstrate that urinary microRNAs extracted with the nanowire device offer a well-fitted strategy for mass screening of CNS tumors.

Artificial Intelligence in Medical Sensors for Clinical Decisions

Due to the limited ability of conventional methods and the limited perspective of human diagnostics, patients are often diagnosed incorrectly or at a late stage as their disease condition progresses. They may then undergo unnecessary treatments due to inaccurate diagnoses. In this Perspective, we offer a brief overview on the integration of nanotechnology-based medical sensors and artificial intelligence (AI) for advanced clinical decision support systems to help decision-makers and healthcare systems improve how they approach information, insights, and the surrounding contexts, as well as to promote the uptake of personalized medicine on an individualized basis. Relying on these milestones, wearable sensing devices could enable interactive and evolving clinical decisions that could be used for evidence-based analysis and recommendations as well as for personalized monitoring of disease progress and treatment. We present and discuss the ongoing challenges and future opportunities associated with AI-enabled medical sensors in clinical decisions.