Artificial Intelligence in Medical Sensors for Clinical Decisions

Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

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.

Challenges and opportunities in commercializing whole-cell bioreporters in environmental application

Since the initial introduction of whole-cell bioreporters (WCBs) nearly 30 years ago, their high sensitivity, selectivity, and suitability for on-site detection have rendered them highly promising for environmental monitoring, medical diagnosis, food safety, biomanufacturing, and other fields. Especially in the environmental field, the technology provides a fast and efficient way to assess the bioavailability of pollutants in the environment. Despite these advantages, the technology has not been commercialized. This lack of commercialization is confusing, given the broad application prospects of WCBs. Over the years, numerous research papers have focused primarily on enhancing the sensitivity and selectivity of WCBs, with little attention paid to their wider commercial applications. So far, there is no a critical review has been published yet on this topic. Therefore, in this article we critically reviewed the research progress of WCBs over the past three decades, assessing the performance and limitations of current systems to understand the barriers to commercial deployment. By identifying these obstacles, this article provided researchers and industry stakeholders with deeper insights into the challenges hindering market entry and inspire further research toward overcoming these barriers, thereby facilitating the commercialization of WCBs as a promising technology for environmental monitoring.

Advances in AI-assisted biochip technology for biomedicine

The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.

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.

Glyphosate: Hepatotoxicity, Nephrotoxicity, Hemotoxicity, Carcinogenicity, and Clinical Cases of Endocrine, Reproductive, Cardiovascular, and Pulmonary System Intoxication

Glyphosate (GLP) is an active agent of GLP-based herbicides (GBHs), i.e., broad-spectrum and postemergent weedkillers, commercialized by Monsanto as, e.g., Roundup and RangerPro formulants. The GBH crop spraying, dedicated to genetically engineered GLP-resistant crops, has revolutionized modern agriculture by increasing the production yield. However, abusively administered GBHs' ingredients, e.g., GLP, polyoxyethyleneamine, and heavy metals, have polluted environmental and industrial areas far beyond farmlands, causing global contamination and life-threatening risk, which has led to the recent local bans of GBH use. Moreover, preclinical and clinical reports have demonstrated harmful impacts of GLP and other GBH ingredients on the gut microbiome, gastrointestinal tract, liver, kidney, and endocrine, as well as reproductive, and cardiopulmonary systems, whereas carcinogenicity of these herbicides remains controversial. Occupational exposure to GBH dysregulates the hypothalamic-pituitary-adrenal axis, responsible for steroidogenesis and endocrinal secretion, thus affecting hormonal homeostasis, functions of reproductive organs, and fertility. On the other hand, acute intoxication with GBH, characterized by dehydration, oliguria, paralytic ileus, as well as hypovolemic and cardiogenic shock, pulmonary edema, hyperkalemia, and metabolic acidosis, may occur fatally. As no antidote has been developed for GBH poisoning so far, the detoxification is mainly symptomatic and supportive and requires intensive care based on gastric lavage, extracorporeal blood filtering, and intravenous lipid emulsion infusion. The current review comprehensively discusses the molecular and physiological basics of the GLP- and/or GBH-induced diseases of the endocrine and reproductive systems, and cardiopulmonary-, nephro-, and hepatotoxicities, presented in recent preclinical studies and case reports on the accidental or intentional ingestions with the most popular GBHs. Finally, they briefly describe modern and future healthcare methods and tools for GLP detection, determination, and detoxification. Future electronically powered, decision-making, and user-friendly devices targeting major GLP/GBH's modes of actions, i.e., dysbiosis and the inhibition of AChE, shall enable self-handled or point-of-care professional-assisted evaluation of the harm followed with rapid capturing GBH xenobiotics in the body and precise determining the GBH pathology-associated biomarkers levels.

Design of a Humidity Sensor for a PPE Kit Using a Flexible Paper Substrate

The present work reports the rapid sweat detection inside a PPE kit using a flexible humidity sensor based on hydrothermally synthesized ZnO (zinc oxide) nanoflowers (ZNFs). Physical characterization of ZNFs was done using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transmission infrared spectroscopy (FTIR), UV-visible, particle size analysis, Raman analysis, and X-ray photoelectron spectroscopy (XPS) analysis, and the hydrophilicity was investigated by using contact angle measurement. Fabrication of a flexible sensor was done by deposition on the paper substrate using the spin coating technique. It exhibited high sensitivity and low response and recovery times in the humidity range 10-95%RH. The sensor demonstrated the highest sensitivity of 296.70 nF/%RH within the humidity range 55-95%RH, and the rapid response and recovery times were also calculated and found as 5.10/1.70 s, respectively. The selectivity of the proposed sensor was also analyzed, and it is highly sensitive to humidity. The humidity sensing characteristics were theoretically witnessed in terms of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and electronic properties of sensing materials in ambient and humid conditions. These theoretical results are evidence of the interaction of ZnO with humidity. Overall, the present study provides a scope of architecture-enabled paper-based humidity sensors for the detection of sweat levels inside PPE kits for health workers.

Single-Response Duplexing of Electrochemical Label-Free Biosensor from the Same Tag

Multiplexing is a valuable strategy to boost throughput and improve clinical accuracy. Exploiting the vertical, meshed design of reproducible and low-cost ultra-dense electrochemical chips, the unprecedented single-response multiplexing of typical label-free biosensors is reported. Using a cheap, handheld one-channel workstation and a single redox probe, that is, ferro/ferricyanide, the recognition events taking place on two spatially resolved locations of the same working electrode can be tracked along a single voltammetry scan by collecting the electrochemical signatures of the probe in relation to different quasi-reference electrodes, Au (0 V) and Ag/AgCl ink (+0.2 V). This spatial isolation prevents crosstalk between the redox tags and interferences over functionalization and binding steps, representing an advantage over the existing non-spatially resolved single-response multiplex strategies. As proof of concept, peptide-tethered immunosensors are demonstrated to provide the duplex detection of COVID-19 antibodies, thereby doubling the throughput while achieving 100% accuracy in serum samples. The approach is envisioned to enable broad applications in high-throughput and multi-analyte platforms, as it can be tailored to other biosensing devices and formats.

An insight into the state of nanotechnology-based electrochemical biosensors for PCOS detection

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders affecting many women of reproductive age all over the world. PCOS is associated with the onset of enduring health complications, notably diabetes and cardiovascular diseases. Furthermore, PCOS escalates the propensity for conditions such as obesity, insulin resistance, and dyslipidemia, which can potentially culminate in life-threatening scenarios. A pervasive predicament surrounding PCOS pertains to its underdiagnosis due to discrepancies in diagnostic criteria and the intricacy of available testing methodologies. Consequently, many women encounter substantial delays in diagnosis with traditional diagnostic approaches. Prompt identification is imperative, as any delay can precipitate severe consequences. The conventional techniques employed for PCOS detection typically suffer from suboptimal accuracy, protracted assay times, and inherent limitations, thereby constraining their widespread applicability and accessibility. In response to these challenges, various electrochemical methods leveraging nanotechnology have been documented. In this concise review, we endeavor to delineate the deficiencies associated with established conventional methodologies while accentuating the distinctive attributes and benefits inherent to contemporary biosensors. We place particular emphasis on elucidating the pivotal advancements and recent breakthroughs in the realm of nanotechnology-facilitated biosensors for the detection of PCOS.

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.

Automation and Computerization of (Bio)sensing Systems

Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.

Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.

Glyphosate: Impact on the microbiota-gut-brain axis and the immune-nervous system, and clinical cases of multiorgan toxicity

Glyphosate (GLP) and GLP-based herbicides (GBHs), such as polyethoxylated tallow amine-based GLP surfactants (GLP-SH), developed in the late 70', have become the most popular and controversial agrochemicals ever produced. Nowadays, GBHs have reached 350 million hectares of crops in over 140 countries, with an annual turnover of 5 billion and 11 billion USD in the U.S.A. and worldwide, respectively. Because of the highly efficient inhibitory activity of GLP targeted to the 5-enolpyruvylshikimate-3-phosphate synthase pathway, present in plants and several bacterial strains, the GLP-resistant crop-based genetic agricultural revolution has decreased famine and improved the costs and quality of living in developing countries. However, this progress has come at the cost of the 50-year GBH overuse, leading to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide farm and companies' workers. According to preclinical and clinical studies covered in the present review, poisoning with GLP, GLP-SH, and GBHs devastatingly affects gut microbiota and the microbiota-gut-brain (MGB) axis, leading to dysbiosis and gastrointestinal (GI) ailments, as well as immunosuppression and inappropriate immunostimulation, cholinergic neurotransmission dysregulation, neuroendocrinal system disarray, and neurodevelopmental and neurobehavioral alterations. Herein, we mainly focus on the contribution of gut microbiota (GM) to neurological impairments, e.g., stroke and neurodegenerative and neuropsychiatric disorders. The current review provides a comprehensive introduction to GLP's microbiological and neurochemical activities, including deviation of the intestinal Firmicutes-to-Bacteroidetes ratio, acetylcholinesterase inhibition, excitotoxicity, and mind-altering processes. Besides, it summarizes and critically discusses recent preclinical studies and clinical case reports concerning the harmful impacts of GBHs on the GI tract, MGB axis, and nervous system. Finally, an insightful comparison of toxic effects caused by GLP, GBH-SH, and GBHs is presented. To this end, we propose a first-to-date survey of clinical case reports on intoxications with these herbicides.

A stretchable wearable sensor with dual working electrodes for reliable detection of uric acid in sweat

Wearable sweat sensors with stretch capabilities and robust performances are desired for continuous monitoring of human health, and it remains a challenge for sweat sensors to detect targets reliably in both static and dynamic states. Herein, a flexible sweat sensor was created using a cost-effective approach involving the utilization of three-dimensional graphene foam and polydimethylsiloxane (PDMS). The flexible electrochemical sensor was fabricated based on PDMS and Pt/Pd nanoparticles modified 3D graphene foam for the detection of uric acid in sweat. Pt/Pd nanoparticles were electrodeposited on the graphene foam to markedly enhance the electrocatalytic activity for uric acid detection. The graphene foam with excellent electrical property and high porosity, and PDMS with an ideal mechanical property endow the sensing device with high stretchability (tolerable strain up to 110 %), high sensitivity (0.87 μA μM-1 cm-2), and stability (remaining unchanged for more than 5000 cycles) for daily wear. To eliminate possible interferences, the wearable sensor was designed with dual working electrodes, and their response difference ensured reliable and accurate detection of targets. This strategy of constructing sweat sensors with dual working electrodes based on the flexible composite material represents a promising way for the development of robust wearable sensing devices.

Self-powered freestanding multifunctional microneedle-based extended gate device for personalized health monitoring

Online monitoring of prognostic biomarkers is critically important when diagnosing disorders and assessing individuals' health, especially for chronic and infectious diseases. Despite this, current diagnosis techniques are time-consuming, labor-intensive, and performed offline. In this context, developing wearable devices for continuous measurements of multiple biomarkers from body fluids has considerable advantages including availability, rapidity, convenience, and minimal invasiveness over the conventional painful and time-consuming tools. However, there is still a significant challenge in powering these devices over an extended period, especially for applications that require continuous and long-term health monitoring. Herein, a new freestanding, wearable, multifunctional microneedle-based extended gate field effect transistor biosensor is fabricated for online detection of multiple biomarkers from the interstitial fluid including sodium, calcium, potassium, and pH along with excellent electrical response, reversibility, and precision. In addition, a hybrid powering system of triboelectric nanogenerator and solar cell was developed for creating a freestanding, closed-loop platform for continuous charging of the device's battery and integrated with an Internet of Things technology to broadcast the measurements online, suggesting a stand-alone, stable multifunctional tool which paves the way for advanced practical personalized health monitoring and diagnosis.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

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.

Humans and robots: Friends of the future? A bird's eye view of biomanufacturing industry 5.0

The evolution of industries have introduced versatile technologies, motivating limitless possibilities of tackling pivotal global predicaments in the arenas of medicine, environment, defence, and national security. In this direction, ardently emerges the new era of Industry 5.0 through the eyes of biomanufacturing, which integrates the most advanced systems 21st century has to offer by means of integrating artificial systems to mimic and nativize the natural milieu to substitute the deficits of nature, thence leading to a new meta world. Albeit, it questions the natural order of the living world, which necessitates certain paramount stipulations to be addressed for a successful expansion of biomanufacturing Industry 5.0. Can humans live in synergism with artificial beings? How can humans establish dominance of hierarchy with artificial counterparts? This perspective provides a bird's eye view on the plausible direction of a new meta world inquisitively. For this purpose, we propose the influence of internet of things (IoT) via new generation interfacial systems, such as, human-machine interface (HMI) and brain-computer interface (BCI) in the domain of tissue engineering and regenerative medicine, which can be extended to target modern warfare and smart healthcare.

Enzyme/pH dual stimuli-responsive nanoplatform co-deliver disulfiram and doxorubicin for effective treatment of breast cancer lung metastasis

OBJECTIVES

Metastasis is still one of the main obstacles in the treatment of breast cancer. This study aimed to develop disulfiram (DSF) and doxorubicin (DOX) co-loaded nanoparticles (DSF-DOX NPs) with enzyme/pH dual stimuli-responsive characteristics to inhibit breast cancer metastasis.

METHODS

DSF-DOX NPs were prepared using the amphiphilic poly(ε-caprolactone)-b-poly(L-glutamic acid)-g-methoxy poly(ethylene glycol) (PCL-b-PGlu-g-mPEG) copolymer by a classical dialysis method. In vitro release tests, in vitro cytotoxicity assay, and anti-metastasis studies were conducted to evaluate pH/enzyme sensitivity and therapeutic effect of DSF-DOX NPs.

RESULTS

The specific pH and enzyme stimuli-responsiveness of DSF-DO NPs can be attributed to the transformation of secondary structure and the degradation of amide bonds in the PGlu segment, respectively. This accelerated drug release significantly increased the cytotoxicity to 4T1 cells. Compared with the control group, the DSF-DOX NPs showed a strong inhibition of in vitro metastasis with a wound healing rate of 36.50% and a migration rate of 18.39%. Impressively, in vivo anti-metastasis results indicated that the metastasis of 4T1 cells was almost completely suppressed by DSF-DOX NPs.

CONCLUSION

DSF-DOX NPs with controllable tumor site delivery of DOX and DSF were a prospectively potential strategy for metastatic breast cancer treatment.

Nanocellulose-based sensors in medical/clinical applications: The state-of-the-art review

In recent years, the considerable importance of healthcare and the indispensable appeal of curative issues, particularly the diagnosis of diseases, have propelled the invention of sensing platforms. With the development of nanotechnology, the integration of nanomaterials in such platforms has been much focused on, boosting their functionality in many fields. In this direction, there has been rapid growth in the utilisation of nanocellulose in sensors with medical applications. Indeed, this natural nanomaterial benefits from striking features, such as biocompatibility, cytocompatibility and low toxicity, as well as unprecedented physical and chemical properties. In this review, different classifications of nanocellulose-based sensors (biosensors, chemical and physical sensors), alongside some subcategories manufactured for health monitoring, stand out. Moreover, the types of nanocellulose and their roles in such sensors are discussed.

An aptamer array for discriminating tetracycline antibiotics based on binding-enhanced intrinsic fluorescence

Tetracyclines are a class of antibiotics with a similar four-ringed structure. Due to this structural similarity, they are not easily differentiated from each other. We recently selected aptamers using oxytetracycline as a target and focused on an aptamer named OTC5, which has similar affinities for oxytetracycline (OTC), tetracycline (TC), and doxycycline (DOX). Tetracyclines exhibit an intrinsic fluorescence that is enhanced upon aptamer binding, allowing convenient binding assays and label-free detection. In this study, we analyzed the top 100 sequences from the previous selection library. Three other sequences were found to differentiate between different tetracyclines (OTC, DOX, and TC) by the selective enhancement of their intrinsic fluorescence. Among them, the OTC43 aptamer was more selective for OTC with a limit of detection (LOD) of 0.7 nM OTC, OTC22 was more selective for DOX (LOD 0.4 nM), and OTC2 was more selective for TC (0.3 nM). Using these three aptamers to form a sensor array, principal component analysis was able to discriminate between the three tetracyclines from each other and from the other molecules. This group of aptamers could be useful as probes for the detection of tetracycline antibiotics.

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.

Large-scale nano-biosensing technologies

Nanoscale technologies have brought significant advancements to modern diagnostics, enabling unprecedented bio-chemical sensitivities that are key to disease monitoring. At the same time, miniaturized biosensors and their integration across large areas enabled tessellating these into high-density biosensing panels, a key capability for the development of high throughput monitoring: multiple patients as well as multiple analytes per patient. This review provides a critical overview of various nanoscale biosensing technologies and their ability to unlock high testing throughput without compromising detection resilience. We report on the challenges and opportunities each technology presents along this direction and present a detailed analysis on the prospects of both commercially available and emerging biosensing technologies.

Multiplex microdisk biosensor based on simultaneous intensity and phase detection

Future healthcare and precision medicine require multiplex and reliable biosensors. Here we present a compact photonic crystal based microdisk biosensor that is designed for simultaneous intensity and phase measurements of multiple biomarkers in parallel. The combination of two different measurement approaches has a range of advantages. Phase detection has higher signal to noise ratios, while intensity measurement helps to align the sensor to high phase sensitivities and increase the reliability. The performance of the microdisk biosensor system is examined by simulations and measurements. For proof of concept, parallel intensity and phase shifts are measured upon binding of human-alpha-thrombin and streptavidin.

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.

Toward Large-Scale Energy Harvesting by a UV-Curable Organic-Coating-Based Triboelectric Nanogenerator

Triboelectric nanogenerators (TENGs) stand out as an attractive form of technology for the efficient harvest of mechanical energy and the powering of wearable devices due to their light weight, simplicity, high power density, and efficient vibration energy scavenging capabilities. However, the requirement for micro/nanostructures and/or complex and expensive instruments hinders their cheap mass production, thus limiting their practical applications. By using a simple, cost-effective, fast spray-coating process, we develop high-performance UV-curable triboelectric coatings for large-scale energy harvesting. The effect of different formulations and coating compositions on the triboelectric output is investigated to design triboelectric coatings with high output performance. The TENG based on a hybrid coating exhibits high output performance of 54.5 μA current, 1228.9 V voltage, 163.6 nC transferred charge and 3.51 mW output power. Moreover, the hybrid coatings show good long-term output stability. All the results indicate that the designed triboelectric coatings show great potential for large-scale energy harvesting with the advantages of cost-effectiveness, fast fabrication, easy mass production and long-term stability.

Wearable artificial intelligence biosensor networks

The demand for high-quality healthcare and well-being services is remarkably increasing due to the ageing global population and modern lifestyles. Recently, the integration of wearables and artificial intelligence (AI) has attracted extensive academic and technological attention for its powerful high-dimensional data processing of wearable biosensing networks. This work reviews the recent developments in AI-assisted wearable biosensing devices in disease diagnostics and fatigue monitoring demonstrating the trend towards personalised medicine with highly efficient, cost-effective, and accurate point-of-care diagnosis by finding hidden patterns in biosensing data and detecting abnormalities. The reliability of adaptive learning and synthetic data and data privacy still need further investigation to realise personalised medicine in the next decade. Due to the worldwide popularity of smartphones, they have been utilised for sensor readout, wireless data transfer, data processing and storage, result display, and cloud server communication leading to the development of smartphone-based biosensing systems. The recent advances have demonstrated a promising future for the healthcare system because of the increasing data processing power, transfer efficiency and storage capacity and diversifying functionalities.

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.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

End-to-end design of wearable sensors

Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.

Materials with Tunable Optical Properties for Wearable Epidermal Sensing in Health Monitoring

Advances in wearable epidermal sensors have revolutionized the way that physiological signals are captured and measured for health monitoring. One major challenge is to convert physiological signals to easily readable signals in a convenient way. One possibility for wearable epidermal sensors is based on visible readouts. There are a range of materials whose optical properties can be tuned by parameters such as temperature, pH, light, and electric fields. Herein, this review covers and highlights a set of materials with tunable optical properties and their integration into wearable epidermal sensors for health monitoring. Specifically, the recent progress, fabrication, and applications of these materials for wearable epidermal sensors are summarized and discussed. Finally, the challenges and perspectives for the next generation wearable devices are proposed.

Non-Invasive Staging of In Vitro Mice Embryos by Means of Volatolomics

Current methods for embryo selection are limited. This study assessed a novel method for the prediction of embryo developmental potential based on the analysis of volatile organic compounds (VOCs) emitted by embryo samples. The study included mice embryos monitored during the pre-implantation period. Four developmental stages of the embryos were tested, covering the period from 1 to 4 days after fecundation. In each stage, the VOCs released by the embryos were collected and examined by employing two different volatolomic techniques, gas-chromatography coupled to mass-spectrometry (GC-MS) and a nanoarray of chemical gas sensors. The GC-MS study revealed that the VOC patterns emanating from embryo samples had statistically different values at different stages of embryo development. The sensor nanoarray was capable of classifying the developmental stages of the embryos. The proposed volatolomics analysis approach for embryos presents a promising potential for predicting their developmental stage. In combination with conventional morphokinetic parameters, it could be effective as a predictive model for detecting metabolic shifts that affect embryo quality before preimplantation.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Decision support system in radiology for fast diagnostics of thoracic diseases under COVID-19 pandemic conditions

In the present article the relevance of using DSS under the current conditions for image recognition and, as a more specific application, for the purpose of additional assistance rendered to medical experts (radiologists) in their decision-making and preparing findings upon assessment of X-ray images is considered. The paper analyzes the requirements for some expert DSS and their main characteristics that they should have; considered and selected is the necessary software for making rapid diagnoses of diseases of the thorax. All these modern requirements and characteristics are met by the Deep Learning Studio (DLS) software, which allows using deep convolutional neural network Inception V3 to teach this network and further obtain optimal results in the recognition and diagnosis of diseases of the thorax by assessing X-ray images. As a result of this study, a ready-made DSS intended for use by medical institutions for additional assistance to radiologists to prepare findings according to X-ray images has been obtained.

Highly Efficient Self-Healing Multifunctional Dressing with Antibacterial Activity for Sutureless Wound Closure and Infected Wound Monitoring

Wound healing represents a major clinical and public healthcare problem that is frequently challenged by infection risks, detrimental consequences on the surrounding tissues, and difficulties to monitor the healing process. Here we report on a novel self-healing, antibacterial, and multifunctional wound dressing for sutureless wound closure and real-time monitoring of the healing parameters. The self-healing elastomer contains cetyltrimethylammonium bromide (CTAB) and has high mechanical toughness (35 MJ m^-3 ), biocompatibility, and outstanding antibacterial activity (bactericidal rate is ≈90% in 12 h), enabling the wound dressing to effectively inhibit bacterial growth and accelerate infected wound healing. In vivo tests based on full-thickness skin incision model shows that the multifunctional wound dressing can help in contracting wound edges and facilitate wound closure and healing, as could be evidenced by notably dense and well-organized collagen deposition. The test provides an evidence that the integrated sensor array within the multifunctional wound dressing can monitor temperature, pH, and glucose level of the wound area in real-time, providing reliable and timely information of the condition of the wound. Ultimately, the reported multifunctional dressing would be of high value in managing the burden associated with wound healing via personalised monitoring and treatment approaches, digital and other people-centred solutions for health care.

Bioinspired Triboelectric Nanosensors for Self-Powered Wearable Applications

The sustainable operation of wearable sensors plays an important role in continuous and longtime health monitoring. Conventional batteries, which are bulky and rigid, do not satisfy these requirements and, rather, cause additional economic burdens and environmental problems by regular replacement of power sources. This article provides a review on an alternative solution in the form of self-powered devices that can harvest energy from the surrounding environment to support the operation of the wearable sensor. The Review starts with an introduction of the self-powered triboelectric nanosensors (TENSs) and its two independent modules: the energy harvester and the sensing module. The Review continues with the TENS-related bioinspired designs for wearable applications, while providing a bird's-eye view of their characteristics and applications. The ongoing challenges and prospects for providing personal healthcare with self-powered TENS are presented and discussed.

Non-invasive electrochemistry-driven metals tracing in human biofluids

Wearable analytical devices represent the future for fast, de-centralized, and human-centered health monitoring. Electrochemistry-based platforms have been highlighted as the role model for future developments amid diverse strategies and transduction technologies. Among the various relevant analytes to be real-time and non-invasively monitored in bodily fluids, we review the latest wearable achievements towards determining essential and toxic metals. On-skin measurements represent an excellent possibility for humankind: real-time monitoring, digital/fast communication with specialists, quick interventions, removing barriers in developing countries. In this review, we discuss the achievements over the last 5 years in non-invasive electrochemical platforms, providing a comprehensive table for quick visualizing the diverse sensing/technological advances. In the final section, challenges and future perspectives about wearables are deeply discussed.

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.

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients' medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

Use of some cost-effective technologies for a routine clinical pathology laboratory

Classically, the need for highly sophisticated instruments with important economic costs has been a major limiting factor for clinical pathology laboratories, especially in developing countries. With the aim of making clinical pathology more accessible, a wide variety of free or economical technologies have been developed worldwide in the last few years. 3D printing and Arduino approaches can provide up to 94% economical savings in hardware and instrumentation in comparison to commercial alternatives. The vast selection of point-of-care-tests (POCT) currently available also limits the need for specific instruments or personnel, as they can be used almost anywhere and by anyone. Lastly, there are dozens of free and libre digital tools available in health informatics. This review provides an overview of the state-of-the-art on cost-effective alternatives with applications in routine clinical pathology laboratories. In this context, a variety of technologies including 3D printing and Arduino, lateral flow assays, plasmonic biosensors, and microfluidics, as well as laboratory information systems, are discussed. This review aims to serve as an introduction to different technologies that can make clinical pathology more accessible and, therefore, contribute to achieve universal health coverage.

Wearable wristband-based electrochemical sensor for the detection of phenylalanine in biofluids

Wearable electrochemical sensors are driven by the user-friendly capability of on-site detection of key biomarkers for health management. Despite the advances in biomolecule monitoring such as glucose, still, several unmet clinical challenges need to be addressed. For example, patients suffering from phenylketonuria (PKU) should be able to monitor their phenylalanine (PHE) level in a rapid, decentralized, and affordable manner to avoid high levels of PHE in the body which can lead to a profound and irreversible mental disability. Herein, we report a wearable wristband electrochemical sensor for the monitoring of PHE tackling the necessity of controlling PHE levels in PHE hydroxylase deficiency patients. The proposed electrochemical sensor is based on a screen-printed electrode (SPE) modified with a membrane consisting of Nafion, to avoid interferences in biofluids. The membrane also consists of sodium 1,2-naphthoquinone-4-sulphonate for the in situ derivatization of PHE into an electroactive product, allowing its electrochemical oxidation at the surface of the SPE in alkaline conditions. Importantly, the electrochemical sensor is integrated into a wristband configuration to enhance user interaction and engage the patient with PHE self-monitoring. Besides, a paper-based sampling strategy is designed to alkalinize the real sample without the need for sample pretreatment, and thus simplify the analytical process. Finally, the wearable device is tested for the determination of PHE in saliva and blood serum. The proposed wristband-based sensor is expected to impact the PKU self-monitoring, facilitating the daily lives of PKU patients toward optimal therapy and disease management.

Printable Smart Materials and Devices: Strategies and Applications

Smart materials are a kind of functional materials which can sense and response to environmental conditions or stimuli from optical, electrical, magnetic mechanical, thermal, and chemical signals, etc. Patterning of smart materials is the key to achieving large-scale arrays of functional devices. Over the last decades, printing methods including inkjet printing, template-assisted printing, and 3D printing are extensively investigated and utilized in fabricating intelligent micro/nano devices, as printing strategies allow for constructing multidimensional and multimaterial architectures. Great strides in printable smart materials are opening new possibilities for functional devices to better serve human beings, such as wearable sensors, integrated optoelectronics, artificial neurons, and so on. However, there are still many challenges and drawbacks that need to be overcome in order to achieve the controllable modulation between smart materials and device performance. In this review, we give an overview on printable smart materials, printing strategies, and applications of printed functional devices. In addition, the advantages in actual practices of printing smart materials-based devices are discussed, and the current limitations and future opportunities are proposed. This review aims to summarize the recent progress and provide reference for novel smart materials and printing strategies as well as applications of intelligent devices.

Profiles of Volatile Biomarkers Detect Tuberculosis from Skin

Tuberculosis (TB) is an infectious disease that threatens >10 million people annually. Despite advances in TB diagnostics, patients continue to receive an insufficient diagnosis as TB symptoms are not specific. Many existing biodiagnostic tests are slow, have low clinical performance, and can be unsuitable for resource-limited settings. According to the World Health Organization (WHO), a rapid, sputum-free, and cost-effective triage test for real-time detection of TB is urgently needed. This article reports on a new diagnostic pathway enabling a noninvasive, fast, and highly accurate way of detecting TB. The approach relies on TB-specific volatile organic compounds (VOCs) that are detected and quantified from the skin headspace. A specifically designed nanomaterial-based sensors array translates these findings into a point-of-care diagnosis by discriminating between active pulmonary TB patients and controls with sensitivity above 90%. This fulfills the WHO's triage test requirements and poses the potential to become a TB triage test.