Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors

Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of <6.46% and a decent concentration measurement correlation with an r2 value of >0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies have the potential to advance FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy.

High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System

CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput, limited integration, and complex reagent preparation. Here, a system, microfluidic multiplate-based ultrahigh throughput analysis of SARS-CoV-2 variants of concern using CRISPR/Cas12a and nonextraction RT-LAMP (mutaSCAN), is proposed for rapid detection of SARS-CoV-2 and its variants with limited resource requirements. With the aid of the self-developed reagents and deep-learning enabled prototype device, our mutaSCAN system can detect SARS-CoV-2 in mock swab samples below 30 min as low as 250 copies/mL with the throughput up to 96 per round. Clinical specimens were tested with this system, the accuracy for routine and mutation testing (22 wildtype samples, 26 mutational samples) was 98% and 100%, respectively. No false-positive results were found for negative (n = 24) samples.

Rapid single-tier serodiagnosis of Lyme disease

Point-of-care serological and direct antigen testing offers actionable insights for diagnosing challenging illnesses, empowering distributed health systems. Here, we report a POC-compatible serologic test for Lyme disease (LD), leveraging synthetic peptides specific to LD antibodies and a paper-based platform for rapid, and cost-effective diagnosis. Antigenic epitopes conserved across Borrelia burgdorferi genospecies, targeted by IgG and IgM antibodies, are selected to develop a multiplexed panel for detection of LD antibodies from patient sera. Multiple peptide epitopes, when combined synergistically with a machine learning-based diagnostic model achieve high sensitivity without sacrificing specificity. Blinded validation with 15 LD-positive and 15 negative samples shows 95.5% sensitivity and 100% specificity. Blind testing with the CDC's LD repository samples confirms the test accuracy, matching lab-based two-tier results, correctly differentiating between LD and look-alike diseases. This LD diagnostic test could potentially replace the cumbersome two-tier testing, improving diagnosis and enabling earlier treatment while facilitating immune monitoring and surveillance.

An integrated electronic tag-based vertical flow assay (e-VFA) with micro-sieve and AlGaN/GaN HEMT sensors for multi-target detection in actual saliva

Vertical flow assay (VFA) is an effective point-of-care (POC) diagnostic tool for widespread application. Nevertheless, the lack of multi-target detection and multi-signal readout capability still remains a challenge. Herein, a brand new VFA scheme for multi-target saliva detection based on electronic tags was proposed, where AlGaN/GaN HEMT sensors modified with different bio-receptors as electronic tags endowed the VFA with multi-target detection capability. In addition, the use of electronic tags instead of optical tags allowed the VFA to simultaneously carry out direct multi-target readouts, which ensure effective POC diagnostics for saliva analysis. Moreover, by integrating a hydrophilically optimized micro-sieve, impurities like sticky filaments, epidermal cells and other large-scale charged particles in saliva were effectively screened, which enabled the direct detection of saliva using AlGaN/GaN HEMT sensors. Glucose, urea, and cortisol were selected to verify the feasibility of the multi-target e-VFA scheme, and the results showed that the limit of detection (LOD) was as low as 100 aM. The linear response was demonstrated in the dynamic range of 100 aM to 100 μM, and the specificity, long-term stability and validity of the actual saliva test were also verified. These results demonstrated that the as-proposed e-VFA has potential for application in saliva detection for simultaneous multi-target detection, and it is expected to achieve the real-time detection of more biological targets in saliva.

Enhancing genome-wide populus trait prediction through deep convolutional neural networks

As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.

AI-Based Metamaterial Design

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

Label-Free Field Effect Transistors (FETs) for Fabrication of Point-of-Care (POC) Biomedical Detection Probes

Field effect transistors (FETs)-based detection probes are powerful platforms for quantification in biological media due to their sensitivity, ease of miniaturization, and ability to function in biological media. Especially, FET-based platforms have been utilized as promising probes for label-free detections with the potential for use in real-time monitoring. The integration of new materials in the FET-based probe enhances the analytical performance of the developed probes by increasing the active surface area, rejecting interfering agents, and providing the possibility for surface modification. Furthermore, the use of new materials eliminates the need for traditional labeling techniques, providing rapid and cost-effective detection of biological analytes. This review discusses the application of materials in the development of FET-based label-free systems for point-of-care (POC) analysis of different biomedical analytes from 2018 to 2024. The mechanism of action of the reported probes is discussed, as well as their pros and cons were also investigated. Also, the possible challenges and potential for the fabrication of commercial devices or methods for use in clinics were discussed.

Electrochemical Paper-Based Microfluidics: Harnessing Capillary Flow for Advanced Diagnostics

Electrochemical paper-based microfluidics has attracted much attention due to the promise of transforming point-of-care diagnostics by facilitating quantitative analysis with low-cost and portable analyzers. Such devices harness capillary flow to transport samples and reagents, enabling bioassays to be executed passively. Despite exciting demonstrations of capillary-driven electrochemical tests, conventional methods for fabricating electrodes on paper impede capillary flow, limit fluidic pathways, and constrain accessible device architectures. This account reviews recent developments in paper-based electroanalytical devices and offers perspective by revisiting key milestones in lateral flow tests and paper-based microfluidics engineering. The study highlights the benefits associated with electrochemical sensing and discusses how the detection modality can be leveraged to unlock novel functionalities. Particular focus is given to electrofluidic platforms that embed electrodes into paper for enhanced biosensing applications. Together, these innovations pave the way for diagnostic technologies that offer portability, quantitative analysis, and seamless integration with digital healthcare, all without compromising the simplicity of commercially available rapid diagnostic tests.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Ratiometric fluorescence sensing NADH using AIE-dots transducers at the point of care

Reduced nicotinamide adenine dinucleotide (NADH) has a strong impact on physiological metabolism, and its concentration is related to metabolic and neurodegenerative diseases. A more reliable and accurate detection method for NADH quantitation is needed for early disease diagnosis and point-of-care testing. Aggregation-induced emission (AIE) materials are widely used to improve the sensitivity in analytes assays due to their anti-aggregation-caused quenching property. Here we developed TPA-BQD-Py AIE-dots transducers and evaluated its performance in NADH detection. The NADH concentration-dependent ratiometric sensing was based on electron transfer from TPA-BQD-Py AIE-dots to NADH with variable fluorescence intensity at 584 nm and 470 nm, resulting in high sensitivity (limit of detection at 110 nM), photostability, selectivity, and a rapid and reversible response. We further developed the application of TPA-BQD-Py AIE-dots transducers in in vivo NADH imaging using a smartphone and digital camera, respectively, demonstrating the potential for NADH point-of-care testing.

Nanoparticle-Based Bioaffinity Assays: From the Research Laboratory to the Market

Advances in the development of new biorecognition elements, nanoparticle-based labels as well as instrumentation have inspired the design of new bioaffinity assays. This review critically discusses the potential of nanoparticles to replace current enzymatic or molecular labels in immunoassays and other bioaffinity assays. Successful implementations of nanoparticles in commercial assays and the need for rapid tests incorporating nanoparticles in different roles such as capture support, signal generation elements, and signal amplification systems are highlighted. The limited number of nanoparticles applied in current commercial assays can be explained by challenges associated with the analysis of real samples (e.g., blood, urine, or nasal swabs) that are difficult to resolve, particularly if the same performance can be achieved more easily by conventional labels. Lateral flow assays that are based on the visual detection of the red-colored line formed by colloidal gold are a notable exception, exemplified by SARS-CoV-2 rapid antigen tests that have moved from initial laboratory testing to widespread market adaption in less than two years.

Comparison and Verification of Three Algorithms for Accuracy Improvement of Quartz Resonant Pressure Sensors

Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of -10-40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors.

Recent advances in point-of-care testing of COVID-19

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.

Innovative solutions for disease management

The increasing prevalence of chronic diseases is a driver for emerging big data technologies for healthcare including digital platforms for data collection, systems for active patient engagement and education, therapy specific predictive models, optimized patient pathway models. Powerful bioelectronic medicine tools for data collection, analysis and visualization allow for joint processing of large volumes of heterogeneous data, which in turn can produce new insights about patient outcomes and alternative interpretations of clinical patterns that can lead to implementation of optimized clinical decisions and clinical patient pathway by healthcare professionals.With this perspective, we identify innovative solutions for disease management and evaluate their impact on patients, payers and society, by analyzing their impact in terms of clinical outcomes (effectiveness, safety, and quality of life) and economic outcomes (cost-effectiveness, savings, and productivity).As a result, we propose a new approach based on the main pillars of innovation in the disease management area, i.e. progressive patient care models, patient-centric approaches, bioelectronics for precise medicine, and lean management that, combined with an increase in appropriate private-public-citizen-partnership, leads towards Patient-Centric Healthcare.

Deep Learning-Enabled Multiplexed Point-of-Care Sensor using a Paper-Based Fluorescence Vertical Flow Assay

Multiplexed computational sensing with a point-of-care serodiagnosis assay to simultaneously quantify three biomarkers of acute cardiac injury is demonstrated. This point-of-care sensor includes a paper-based fluorescence vertical flow assay (fxVFA) processed by a low-cost mobile reader, which quantifies the target biomarkers through trained neural networks, all within <15 min of test time using 50 µL of serum sample per patient. This fxVFA platform is validated using human serum samples to quantify three cardiac biomarkers, i.e., myoglobin, creatine kinase-MB, and heart-type fatty acid binding protein, achieving less than 0.52 ng mL-1 limit-of-detection for all three biomarkers with minimal cross-reactivity. Biomarker concentration quantification using the fxVFA that is coupled to neural network-based inference is blindly tested using 46 individually activated cartridges, which shows a high correlation with the ground truth concentrations for all three biomarkers achieving >0.9 linearity and <15% coefficient of variation. The competitive performance of this multiplexed computational fxVFA along with its inexpensive paper-based design and handheld footprint makes it a promising point-of-care sensor platform that can expand access to diagnostics in resource-limited settings.

CRISPR-Cas-amplified urinary biomarkers for multiplexed and portable cancer diagnostics

Synthetic biomarkers, bioengineered sensors that generate molecular reporters in diseased microenvironments, represent an emerging paradigm in precision diagnostics. Despite the utility of DNA barcodes as a multiplexing tool, their susceptibility to nucleases in vivo has limited their utility. Here we exploit chemically stabilized nucleic acids to multiplex synthetic biomarkers and produce diagnostic signals in biofluids that can be 'read out' via CRISPR nucleases. The strategy relies on microenvironmental endopeptidase to trigger the release of nucleic acid barcodes and polymerase-amplification-free, CRISPR-Cas-mediated barcode detection in unprocessed urine. Our data suggest that DNA-encoded nanosensors can non-invasively detect and differentiate disease states in transplanted and autochthonous murine cancer models. We also demonstrate that CRISPR-Cas amplification can be harnessed to convert the readout to a point-of-care paper diagnostic tool. Finally, we employ a microfluidic platform for densely multiplexed, CRISPR-mediated DNA barcode readout that can potentially evaluate complex human diseases rapidly and guide therapeutic decisions.

Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics

BACKGROUND

Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format.

METHODS

We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images.

RESULTS

Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images.

CONCLUSIONS

The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.

Engineering Innovative Interfaces for Point-of-Care Diagnostics

The ongoing Coronavirus disease 2019 (COVID-19) pandemic illustrates the need for sensitive and reliable tools to diagnose and monitor diseases. Traditional diagnostic approaches rely on centralized laboratory tests that result in long wait times to results and reduce the number of tests that can be given. Point-of-care tests (POCTs) are a group of technologies that miniaturize clinical assays into portable form factors that can be run both in clinical areas --in place of traditional tests-- and outside of traditional clinical settings --to enable new testing paradigms. Hallmark examples of POCTs are the pregnancy test lateral flow assay and the blood glucose meter. Other uses for POCTs include diagnostic assays for diseases like COVID-19, HIV, and malaria but despite some successes, there are still unsolved challenges for fully translating these lower cost and more versatile solutions. To overcome these challenges, researchers have exploited innovations in colloid and interface science to develop various designs of POCTs for clinical applications. Herein, we provide a review of recent advancements in lateral flow assays, other paper based POCTs, protein microarray assays, microbead flow assays, and nucleic acid amplification assays. Features that are desirable to integrate into future POCTs, including simplified sample collection, end-to-end connectivity, and machine learning, are also discussed in this review.

Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs.

Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

A novel patches-selection method for the classification of point-of-care biosensing lateral flow assays with cardiac biomarkers

Cardiovascular Disease (CVD) is amongst the leading cause of death globally, which calls for rapid detection and treatment. Biosensing devices are used for the diagnosis of cardiovascular disease at the point-of-care (POC), with lateral flow assays (LFAs) being particularly useful. However, due to their low sensitivity, most LFAs have been shown to have difficulties detecting low analytic concentrations. Breakthroughs in artificial intelligence (AI) and image processing reduced this detection constraint and improved disease diagnosis. This paper presents a novel patches-selection approach for generating LFA images from the test line and control line of LFA images, analyzing the image features, and utilizing them to reliably predict and classify LFA images by deploying classification algorithms, specifically Convolutional Neural Networks (CNNs). The generated images were supplied as input data to the CNN model, a strong model for extracting crucial information from images, to classify the target images and provide risk stratification levels to medical professionals. With this approach, the classification model produced about 98% accuracy, and as per the literature review, this approach has not been investigated previously. These promising results show the proposed method may be useful for identifying a wide variety of diseases and conditions, including cardiovascular problems.

DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants. Traditional methods typically use linear regression models with clear assumptions; such methods are unable to capture the complex relationships between genotypes and phenotypes. Non-linear models (e.g., deep neural networks) have been proposed as a superior alternative to linear models because they can capture complex non-additive effects. Here we introduce a deep learning (DL) method, deep neural network genomic prediction (DNNGP), for integration of multi-omics data in plants. We trained DNNGP on four datasets and compared its performance with methods built with five classic models: genomic best linear unbiased prediction (GBLUP); two methods based on a machine learning (ML) framework, light gradient boosting machine (LightGBM) and support vector regression (SVR); and two methods based on a DL framework, deep learning genomic selection (DeepGS) and deep learning genome-wide association study (DLGWAS). DNNGP is novel in five ways. First, it can be applied to a variety of omics data to predict phenotypes. Second, the multilayered hierarchical structure of DNNGP dynamically learns features from raw data, avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation (rectified linear unit) functions. Third, when small datasets were used, DNNGP produced results that are competitive with results from the other five methods, showing greater prediction accuracy than the other methods when large-scale breeding data were used. Fourth, the computation time required by DNNGP was comparable with that of commonly used methods, up to 10 times faster than DeepGS. Fifth, hyperparameters can easily be batch tuned on a local machine. Compared with GBLUP, LightGBM, SVR, DeepGS and DLGWAS, DNNGP is superior to these existing widely used genomic selection (GS) methods. Moreover, DNNGP can generate robust assessments from diverse datasets, including omics data, and quickly incorporate complex and large datasets into usable models, making it a promising and practical approach for straightforward integration into existing GS platforms.

Point-of-care testing in companion and food animal disease diagnostics

Laboratory diagnoses of animal diseases has advanced tremendously in recent decades with the advent of cutting-edge technologies such as real-time polymerase chain reaction, next generation sequencing (NGS), matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and others However, most of these technologies need sophisticated equipment, laboratory space and highly skilled workforce. Therefore, there is an increasing market demand for point-of-care testing (POCT) in animal health and disease diagnostics. A wide variety of assays based on antibodies, antigens, nucleic acid, and nanopore sequencing are currently available. Each one of these tests have their own advantages and disadvantages. However, a number of research and developmental activities are underway in both academia and industry to improve the existing tests and develop newer and better tests in terms of sensitivity, specificity, turnaround time and affordability. In both companion and food animal disease diagnostics, POCT has an increasing role to play, especially in resource-limited settings. It plays a critical role in improving animal health and wellbeing in rural communities in low- and middle-income countries. At the same time, ensuring high standard of quality through proper validation, quality assurance and regulation of these assays are very important for accurate diagnosis, surveillance, control and management of animal diseases. This review addresses the different types of POCTs currently available for companion and food animal disease diagnostics, tests in the pipeline and their advantages and disadvantages.

Lateral Flow Microimmunoassay (LFµIA) for the Reliable Quantification of Allergen Traces in Food Consumables

Quality assurance and food safety are of great concern within the food industry because of unknown quantities of allergens often present in food. Therefore, there is an ongoing need to develop rapid, sensitive, and easy to use methods that serve as an alternative to mass spectrometry and enzyme-linked immunosorbent assay (ELISA) for monitoring food safety. Lateral flow immunoassay is one of the most used point-of-need devices for clinical, environmental, and food safety applications. Compared to traditional methods, it appears to be a simple and fast alternative for detecting food allergens. However, its reliability is frequently questioned due to the lack of quantitative information. In this study, a lateral flow microimmunoassay (LFµIA) is presented that integrates up to 36 spots in microarray format in a single strip, providing semi-quantitative information about the level of allergens, positive and negative controls, internal calibration, and hook effect. The LFµIA has been evaluated for the on-site simultaneous and reliable quantification of almond and peanut allergens as a proof of concept, demonstrating high sensitivity (185 and 229 µg/kg, respectively), selectivity (77%), and accuracy (RSD 5-25%) when analyzing commercial allergen-suspicious food consumables.

Mechanical Timer-Actuated Fluidic Dispensing System: Applications to an Automated Multistep Lateral Flow Immunoassay with High Sensitivity

In this study, we present a fluidic dispensing system that can automate the sequential fluidic delivery of multiple reagents for lateral flow assays. Highly sensitive assays typically require multiple solution-based sequences, including washing steps and signal amplification. However, implementation of these types of sequences on an automated and highly sensitive point-of-care testing (POCT) platform remains challenging. Our platform consists of two disposable cartridges with reagent chambers and a test strip and an instrument that has a mechanical timer to actuate the cam-follower-gear components. The timer rotation sequentially shifts the position of the chambers and loads the reagents to the test paper strip. The dispensing intervals are controlled at a variation of <1% within a total actuation time of 60 min. Unlike other POCT devices, the timing of fluid delivery in our timer-actuated platform is not dependent on the selection of substrates and reagents, and the unique approach to fluidic delivery results in no reagent overlap or carryover, minimal reagent loss, and highly accurate fluidic timing control for highly sensitive solution-based assays. As a model application, the proposed platform applies a gold enhancement solution to amplify the detection signal and detect prostate-specific antigen with a limit of detection of 86 pg/mL within 27 min. This platform provides an opportunity for solution-based POCT applications with high sensitivity, thereby satisfying the requirement for user-friendly operations in resource-limited settings.

Toward Next Generation Lateral Flow Assays: Integration of Nanomaterials

Lateral flow assays (LFAs) are currently the most used point-of-care sensors for both diagnostic (e.g., pregnancy test, COVID-19 monitoring) and environmental (e.g., pesticides and bacterial monitoring) applications. Although the core of LFA technology was developed several decades ago, in recent years the integration of novel nanomaterials as signal transducers or receptor immobilization platforms has brought improved analytical capabilities. In this Review, we present how nanomaterial-based LFAs can address the inherent challenges of point-of-care (PoC) diagnostics such as sensitivity enhancement, lowering of detection limits, multiplexing, and quantification of analytes in complex samples. Specifically, we highlight the strategies that can synergistically solve the limitations of current LFAs and that have proven commercial feasibility. Finally, we discuss the barriers toward commercialization and the next generation of LFAs.

Nano-Filamented Textile Sensor Platform with High Structure Sensitivity

A key challenge to the creation of chemically responsive electro-functionality of nonconductive, hydrophobic, and free-contacted textile or fibrous network materials is how to impart the 3D structure with functional filaments to enable responsive structure sensitivity, which is critical in establishing the fibrous platform technology for sensor applications. We demonstrate this capability using an electrospun polymeric fibrous substrate embedded with nano-filaments defined by size-tunable gold nanoparticles and structurally sensitive dendrons as crosslinkers. The resulting interparticle properties strongly depend on the assembly of the nano-filaments, enabling an interface with high structure sensitivity to molecular interactions. This is demonstrated with chemiresistive responses to vaporous alcohol molecules with different chain lengths and isomers, which is critical in breath and sweat sensing involving a high-moisture or -humidity background. The sensitivity scales with the chain length and varies with their isomers. This approach harnesses the multifunctional tunability of the nano-filaments in a sensor array format, showing high structure sensitivity to the alcohol molecules with different chain lengths and isomers. The high structure sensitivity and its implications for a paradigm shift in the design of textile sensor arrays for multiplexing human performance monitoring via breath or sweat sensing and environmental monitoring of air quality are also discussed.

Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning

Microfluidic paper-based analytical devices (μPADs) have been widely used in point-of-care testing owing to their simple operation, low volume of the sample required, and the lack of the need for an external force. To obtain accurate semi-quantitative or quantitative results, μPADs need to respond to the challenges posed by differences in reaction conditions. In this paper, multi-layer μPADs are fabricated by the imprinting method for the colorimetric detection of C-reactive protein (CRP). Different lighting conditions and shooting angles of scenes are simulated in image acquisition, and the detection-related performance of μPADs is improved by using a machine learning algorithm. The You Only Look Once (YOLO) model is used to identify the areas of reaction in μPADs. This model can observe an image only once to predict the objects present in it and their locations. The YOLO model trained in this study was able to identify all the reaction areas quickly without incurring any error. These reaction areas were categorized by classification algorithms to determine the risk level of CRP concentration. Multi-layer perceptron, convolutional neural network, and residual network algorithms were used for the classification tasks, where the latter yielded the highest accuracy of 96%. It has a promising application prospect in fast recognition and analysis of μPADs.

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry

The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the handcrafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance suffers from poor image (cell diffraction pattern) signatures due to their small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as red blood cell (RBC) and white blood cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types.

REASSURED Multiplex Diagnostics: A Critical Review and Forecast

The diagnosis of infectious diseases is ineffective when the diagnostic test does not meet one or more of the necessary standards of affordability, accessibility, and accuracy. The World Health Organization further clarifies these standards with a set of criteria that has the acronym ASSURED (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free and Deliverable to end-users). The advancement of the digital age has led to a revision of the ASSURED criteria to REASSURED: Real-time connectivity, Ease of specimen collection, Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free or simple, and Deliverable to end-users. Many diagnostic tests have been developed that aim to satisfy the REASSURED criteria; however, most of them only detect a single target. With the progression of syndromic infections, coinfections and the current antimicrobial resistance challenges, the need for multiplexed diagnostics is now more important than ever. This review summarizes current diagnostic technologies for multiplexed detection and forecasts which methods have promise for detecting multiple targets and meeting all REASSURED criteria.

An automated fast-flow/delayed paper-based platform for the simultaneous electrochemical detection of hepatitis B virus and hepatitis C virus core antigen

Electrochemical paper-based analytical devices (ePADs) are useful analytical devices that serve as point-of-care testing (POCT) devices for various clinical biomarkers in view of their simplicity, portability, and low-cost format. However, multistep reagent manipulation usually restricts the performance of the device for end users. Herein, we developed a sequential ePAD for sequential immunosensing fluid delivery by integrating dual flow behaviors (fast-flow/delayed) within a single paper platform for the simultaneous detection of hepatitis B surface antigen (HBsAg) and hepatitis C core antigen (HCVcAg). In the present work, a fast-flow channel was used for the automated washing of unbound antigens, while a delayed channel was created to store a redox reagent for further electrochemical analysis with a single buffer loading (the analysis time can be completed within 500 s). Hence, the undesirable complex procedure of multi-step reagent manipulation is scarcely needed by the user. The detection limit of the proposed ePAD was as low as 18.2 pg mL^-1 for HBsAg and 1.19 pg mL^-1 for HCVcAg. In addition, this proposed ePAD was also proven to be effective in real clinical sera from patients to verify its biological applicability. The ePAD sensor shows high promise as an easy-to-use, portable, and extendable sensor for other multiplex biological assays.

Modular design principle based on compartmental drug delivery systems

The current manufacturing solutions for oral solid dosage forms are fundamentally based on technologies from the 19th century. This approach is well suited for mass production of one-size-fits-all products; however, it does not allow for a straight-forward personalization and mass customization of the pharmaceutical end-product. In order to provide better therapies to the patients, a need for innovative manufacturing concepts and product design principles has been rising. Additive manufacturing opens up a possibility for compartmentalization of drug products, including design of spatially separated multidrug and functional excipient compartments. This compartmentalized solution can be further expanded to modular design thinking. Modular design is referring to combination of building blocks containing a given amount of drug compound(s) and related functional excipients into a larger final product. Implementation of modular design principles is paving the way for implementing the emerging personalization potential within health sciences by designing compartmental and reactive product structures that can be manufactured based on the individual needs of each patient. This review will introduce the existing compartmentalized product design principles and discuss the integration of these into edible electronics allowing for innovative control of drug release.

Light People: Professor Aydogan Ozcan

In 2016, the news that Google's artificial intelligence (AI) robot AlphaGo, based on the principle of deep learning, won the victory over lee Sedol, the former world Go champion and the famous 9th Dan competitor of Korea, caused a sensation in both fields of AI and Go, which brought epoch-making significance to the development of deep learning. Deep learning is a complex machine learning algorithm that uses multiple layers of artificial neural networks to automatically analyze signals or data. At present, deep learning has penetrated into our daily life, such as the applications of face recognition and speech recognition. Scientists have also made many remarkable achievements based on deep learning. Professor Aydogan Ozcan from the University of California, Los Angeles (UCLA) led his team to research deep learning algorithms, which provided new ideas for the exploring of optical computational imaging and sensing technology, and introduced image generation and reconstruction methods which brought major technological innovations to the development of related fields. Optical designs and devices are moving from being physically driven to being data-driven. We are much honored to have Aydogan Ozcan, Fellow of the National Academy of Inventors and Chancellor's Professor of UCLA, to unscramble his latest scientific research results and foresight for the future development of related fields, and to share his journey of pursuing Optics, his indissoluble relationship with Light: Science & Applications (LSA), and his experience in talent cultivation.

A surface-based calibration approach to enable dynamic and accurate quantification of colorimetric assay systems

Colorimetry is widely used in assay systems for its low-cost, ease-of-use, rapidity, moderate storage requirements and intuitively visible effects. However, the application is limited due to its relatively low sensitivity. Conventional colorimetric calibration methods often use a fixed incubation time that can limit the detection range, system robustness and sensitivity. In this paper, we used color saturation to measure the accumulation of product (correlation coefficient R² = 0.9872), and we created a novel "calibration mesh" method based on an expanded sigmoid function to enhance sensitivity. The novel calibration mesh method can be adapted for a wide variety of assay systems to improve robustness and detection range, and provide a dynamic and faster output.

Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning

Particle agglutination assays are widely adopted immunological tests that are based on antigen-antibody interactions. Antibody-coated microscopic particles are mixed with a test sample that potentially contains the target antigen, as a result of which the particles form clusters, with a size that is a function of the antigen concentration and the reaction time. Here, we present a quantitative particle agglutination assay that combines mobile lens-free microscopy and deep learning for rapidly measuring the concentration of a target analyte; as its proof-of-concept, we demonstrate high-sensitivity C-reactive protein (hs-CRP) testing using human serum samples. A dual-channel capillary lateral flow device is designed to host the agglutination reaction using 4 μL of serum sample with a material cost of 1.79 cents per test. A mobile lens-free microscope records time-lapsed inline holograms of the lateral flow device, monitoring the agglutination process over 3 min. These captured holograms are processed, and at each frame the number and area of the particle clusters are automatically extracted and fed into shallow neural networks to predict the CRP concentration. 189 measurements using 88 unique patient serum samples were utilized to train, validate and blindly test our platform, which matched the corresponding ground truth concentrations in the hs-CRP range (0-10 μg mL^-1) with an R² value of 0.912. This computational sensing platform was also able to successfully differentiate very high CRP concentrations (e.g., >10-500 μg mL^-1) from the hs-CRP range. This mobile, cost-effective and quantitative particle agglutination assay can be useful for various point-of-care sensing needs and global health related applications.

Integration of personalized drug delivery systems into digital health

Personalized drug delivery systems (PDDS), implying the patient-tailored dose, dosage form, frequency of administration and drug release kinetics, and digital health platforms for diagnosis and treatment monitoring, patient adherence, and traceability of drug products, are emerging scientific areas. Both fields are advancing at a fast pace. However, despite the strong complementary nature of these disciplines, there are only a few successful examples of merging these areas. Therefore, it is important and timely to combine PDDS with an increasing number of high-end digital health solutions to create an interactive feedback loop between the actual needs of each patient and the drug products. This review provides an overview of advanced design solutions for new products such as interactive personalized treatment that would interconnect the pharmaceutical and digital worlds. Furthermore, we discuss the recent advancements in the pharmaceutical supply chain (PSC) management and related limitations of the current mass production model. We summarize the current state of the art and envision future directions and potential development areas.

Advances in point-of-care testing for cardiovascular diseases

Point-of-care testing (POCT) is a specific format of diagnostic testing that is conducted without accompanying infrastructure or sophisticated instrumentation. Traditionally, such rapid sample-to-answer assays provide inferior analytical performances to their laboratory counterparts when measuring cardiac biomarkers. Hence, their potentially broad applicability is somewhat bound by their inability to detect clinically relevant concentrations of cardiac troponin (cTn) in the early stages of myocardial injury. However, the continuous refinement of biorecognition elements, the optimization of detection techniques, and the fabrication of tailored fluid handling systems to manage the sensing process has stimulated the production of commercial assays that can support accelerated diagnostic pathways. This review will present the latest commercial POC assays and examine their impact on clinical decision-making. The individual elements that constitute POC assays will be explored, with an emphasis on aspects that contribute to economically feasible and highly sensitive assays. Furthermore, the prospect of POCT imparting a greater influence on early interventions for medium to high-risk individuals and the potential to re-shape the paradigm of cardiovascular risk assessments will be discussed.

Point-of-care detection of cytokines in cytokine storm management and beyond: Significance and challenges

Cytokines are signaling molecules between cells in immune system. Cytokine storm, due to the sudden acute increase in levels of pro-inflammatory circulating cytokines, can result in disease severity and major-organ damage. Thus, there is urgent need to develop rapid, sensitive, and specific methods for monitoring of cytokines in biology and medicine. Undoubtedly, point-of-care testing (POCT) will provide clinical significance in disease early diagnosis, management, and prevention. This review aims to summarize and discuss the latest technologies for detection of cytokines with a focus on POCT. The overview of diseases resulting from imbalanced cytokine levels, such as COVID-19, sepsis and other cytokine release syndromes are presented. The clinical cut-off levels of cytokine as biomarkers for different diseases are summarized. The challenges and perspectives on the development of cytokine POCT devices are also proposed and discussed. Cytokine POCT devices are expected to be the ongoing spotlight of disease management and prevention during COVID-19 pandemic and also the post COVID-19 pandemic era.

A single snapshot multiplex immunoassay platform utilizing dense test lines based on engineered beads

Co-circulation of coronavirus disease 2019 (COVID-19) and dengue fever has been reported. Accurate and timely multiplex diagnosis is required to prevent future pandemics. Here, we developed an innovative microfluidic chip that enables a snapshot multiplex immunoassay for timely on-site response and offers unprecedented multiplexing capability with an operating procedure similar to that of lateral flow assays. An open microchannel assembly of individually engineered microbeads was developed to construct nine high-density test lines, which can be imaged in a 1 mm² field-of-view. Thus, simultaneous detection of multiple antibodies would be achievable in a single high-resolution snapshot. Next, we developed a novel pixel intensity-based imaging process to distinguish effective and non-specific fluorescence signals, thereby improving the reliability of this fluorescence-based immunoassay. Finally, the chip specifically identified and classified random combinations of arbovirus (Zika, dengue, and chikungunya viruses) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies within 30 min. Therefore, we believe that this snapshot multiplex immunoassay chip is a powerful diagnostic tool to control current and future pandemics.

Capillary Microfluidics for Monitoring Medication Adherence

Medication adherence is a medical and societal issue worldwide, with approximately half of patients failing to adhere to prescribed treatments. The goal of this Minireview is to examine how recent work on microfluidics for point-of-care diagnostics may be used to enhance adherence to medication. It specifically focuses on capillary microfluidics since these devices are self-powered, easy to use, and well established for diagnostics and drug monitoring. Considering that an improvement in medication adherence can have a much larger effect than the development of new medical treatments, it is long overdue for the research communities working in chemistry, biology, pharmacology, and material sciences to consider developing technologies to enhance medication adherence. For these reasons, this Minireview is not meant to be exhaustive but rather to provide a quick starting point for researchers interested in joining this complex but intriguing and exciting field of research.

Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules

Insights into the fascinating molecular world of biological processes are crucial for understanding diseases, developing diagnostics, and effective therapeutics. These processes are complex as they involve interactions between four major classes of biomolecules, i.e., proteins, nucleic acids, carbohydrates, and lipids, which makes it important to be able to discriminate between all these different biomolecular species. In this work, a deep learning-augmented, chemically-specific nanoplasmonic technique that enables such a feat in a label-free manner to not disrupt native processes is presented. The method uses a highly sensitive multiresonant plasmonic metasurface in a microfluidic device, which enhances infrared absorption across a broadband mid-IR spectrum and in water, despite its strongly overlapping absorption bands. The real-time format of the optofluidic method enables the collection of a vast amount of spectrotemporal data, which allows the construction of a deep neural network to discriminate accurately between all major classes of biomolecules. The capabilities of the new method are demonstrated by monitoring of a multistep bioassay containing sucrose- and nucleotides-loaded liposomes interacting with a small, lipid membrane-perforating peptide. It is envisioned that the presented technology will impact the fields of biology, bioanalytics, and pharmacology from fundamental research and disease diagnostics to drug development.

Point-of-care diagnostics for infectious diseases: From methods to devices

The current widespread of COVID-19 all over the world, which is caused by SARS-CoV-2 virus, has again emphasized the importance of development of point-of-care (POC) diagnostics for timely prevention and control of the pandemic. Compared with labor- and time-consuming traditional diagnostic methods, POC diagnostics exhibit several advantages such as faster diagnostic speed, better sensitivity and specificity, lower cost, higher efficiency and ability of on-site detection. To achieve POC diagnostics, developing POC detection methods and correlated POC devices is the key and should be given top priority. The fast development of microfluidics, micro electro-mechanical systems (MEMS) technology, nanotechnology and materials science, have benefited the production of a series of portable, miniaturized, low cost and highly integrated POC devices for POC diagnostics of various infectious diseases. In this review, various POC detection methods for the diagnosis of infectious diseases, including electrochemical biosensors, fluorescence biosensors, surface-enhanced Raman scattering (SERS)-based biosensors, colorimetric biosensors, chemiluminiscence biosensors, surface plasmon resonance (SPR)-based biosensors, and magnetic biosensors, were first summarized. Then, recent progresses in the development of POC devices including lab-on-a-chip (LOC) devices, lab-on-a-disc (LOAD) devices, microfluidic paper-based analytical devices (μPADs), lateral flow devices, miniaturized PCR devices, and isothermal nucleic acid amplification (INAA) devices, were systematically discussed. Finally, the challenges and future perspectives for the design and development of POC detection methods and correlated devices were presented. The ultimate goal of this review is to provide new insights and directions for the future development of POC diagnostics for the management of infectious diseases and contribute to the prevention and control of infectious pandemics like COVID-19.

Kaleidoscopic fluorescent arrays for machine-learning-based point-of-care chemical sensing

Multiplexed analysis allows simultaneous measurements of multiple targets, improving the detection sensitivity and accuracy. However, highly multiplexed analysis has been challenging for point-of-care (POC) sensing, which requires a simple, portable, robust, and affordable detection system. In this work, we developed paper-based POC sensing arrays consisting of kaleidoscopic fluorescent compounds. Using an indolizine structure as a fluorescent core skeleton, named Kaleidolizine (KIz), a library of 75 different fluorescent KIz derivatives were designed and synthesized. These KIz derivatives are simultaneously excited by a single ultraviolet (UV) light source and emit diverse fluorescence colors and intensities. For multiplexed POC sensing system, fluorescent compounds array on cellulose paper was prepared and the pattern of fluorescence changes of KIz on array were specific to target chemicals adsorbed on that paper. Furthermore, we developed a machine-learning algorithm for automated, rapid analysis of color and intensity changes of individual sensing arrays. We showed that the paper sensor arrays could differentiate 35 different volatile organic compounds using a smartphone-based handheld detection system. Powered by the custom-developed machine-learning algorithm, we achieved the detection accuracy of 97% in the VOC detection. The highly multiplexed paper sensor could have favorable applications for monitoring a broad-range of environmental toxins, heavy metals, explosives, pathogens.

Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors

Biosensors are essential tools which have been traditionally used to monitor environmental pollution and detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication-enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to, potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization-induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors, as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology-enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications. The article aims to provide the reader a holistic overview of different nanostructures which have been used for biosensing purposes along with some specific applications in the field of cancer detection and the Internet of things (IoT), as well as a brief overview of machine-learning-based biosensing.