Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning

Portable droplet-based real-time monitoring of pancreatic α-amylase in postoperative patients

Postoperative complications after pancreatic surgery are frequent and can be life-threatening. Current clinical diagnostic strategies involve time-consuming quantification of α-amylase activity in abdominal drain fluid, which is performed on the first and third postoperative day. The lack of real-time monitoring may delay adjustment of medical treatment upon complications and worsen prognosis for patients. We report a bedside portable droplet-based millifluidic device enabling real-time sensing of drain α-amylase activity for postoperative monitoring of patients undergoing pancreatic surgery. Here, a tiny amount of drain liquid of patient samples is continuously collected and co-encapsulated with a starch reagent in nanoliter-sized droplets to track the fluorescence intensity released upon reaction with α-amylase. Comparing the α-amylase levels of 32 patients, 97 % of the results of the droplet-based millifluidic system matched the clinical data. Our method reduces the α-amylase assay duration to approximately 3 min with the limit of detection 7 nmol/s·L, enabling amylase activity monitoring at the bedside in clinical real-time. The presented droplet-based platform can be extended for analysis of different body fluids, diseases, and towards a broader range of biomarkers, including lipase, bilirubin, lactate, inflammation, or liquid biopsy markers, paving the way towards new standards in postoperative patient monitoring.

Speckle wavemeter based on a multi-core fiber and compressive imaging

Random speckle patterns contain valuable information about the incident light. Researchers have successfully constructed spectrometers and wavemeters by utilizing the speckles generated by inter-mode interferences of a multimode fiber (MMF). However, cameras were often employed to record the speckle data in previous reports. The camera's high cost (especially in the near-infrared range), large size, and low response speed limit the applications in optical communications, metrology, and optical sensing. A seven-core fiber (SCF) was fused with an MMF to capture the speckle pattern, where each core coupled part of the speckle field. Furthermore, we take advantage of the space division multiplexing capability of the SCF by incorporating an optical switch. This allows the variety of speckles generated by the incidence of different cores into the MMF. A convolutional neural network (CNN) regression algorithm was designed to analyze the complicated speckle data. The experimental results show that the proposed wavemeter can resolve adjacent wavelengths of 1 pm with an error of about 0.2 pm. We also discussed how different lengths of MMF influence the wavelength resolution. In conclusion, our research presents a robust and cost-effective approach to a wavelength measurement device by use of a seven-core optical fiber.

Roadmap on Label-Free Super-Resolution Imaging

Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles which need to be overcome to break the classical diffraction limit of the LFSR imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability which are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.

Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering

Surface plasmon resonance microscopy (SPRM) combines the principles of traditional microscopy with the versatility of surface plasmons to develop label-free imaging methods. This paper describes a proof-of-principles approach based on deep learning that utilized the Y-Net convolutional neural network model to improve the detection and analysis methodology of SPRM. A machine-learning based image analysis technique was used to provide a method for the one-shot analysis of SPRM images to estimate scattering parameters such as the scatterer location. The method was assessed by applying the approach to SPRM images and reconstructing an image from the network output for comparison with the original image. The results showed that deep learning can localize scatterers and predict other variables of scattering objects with high accuracy in a noisy environment. The results also confirmed that with a larger field of view, deep learning can be used to improve traditional SPRM such that it localizes and produces scatterer characteristics in one shot, considerably increasing the detection capabilities of SPRM.

High-resolution compact spectrometer based on periodic tapered coreless fiber

This Letter proposes a method that balances miniaturization and high performance of fiber optic speckle spectrometers. The periodically tapered coreless fiber is used as the scattering element to excite more higher-order modes in the coreless fiber. As a result, a remarkable spectral resolution of 0.03 nm in the near-infrared spectrum can be achieved with a 5-cm-long fiber. Narrow linewidth and broadband spectra in the wavelength of 1540-1560 nm are reconstructed separately, demonstrating the excellent performance of the designed all-fiber spectrometer. The spectral resolution of our proposed spectrometer is comparable to that of a 2-m multimode fiber spectrometer and has a significant improvement in miniaturization.

LSPR-Based Biosensing Enables the Detection of Antimicrobial Resistance Genes

The development of rapid, simple, and accurate bioassays for the detection of nucleic acids has received increasing demand in recent years. Here, localized surface plasmon resonance (LSPR) spectroscopy for the detection of an antimicrobial resistance gene, sulfhydryl variable β-lactamase (blaSHV), which confers resistance against a broad spectrum of β-lactam antibiotics is used. By performing limit of detection experiments, a 23 nucleotide (nt) long deoxyribonucleic acid (DNA) sequence down to 25 nm was detected, whereby the signal intensity is inversely correlated with sequence length (23, 43, 63, and 100 nt). In addition to endpoint measurements of hybridization events, the setup also allowed to monitor the hybridization events in real-time, and consequently enabled to extract kinetic parameters of the studied binding reaction. Performing LSPR measurements using single nucleotide polymorphism (SNP) variants of blaSHV revealed that these sequences can be distinguished from the fully complementary sequence. The possibility to distinguish such sequences is of utmost importance in clinical environments, as it allows to identify mutations essential for enzyme function and thus, is crucial for the correct treatment with antibiotics. Taken together, this system provides a robust, label-free, and cost-efficient analytical tool for the detection of nucleic acids and will enable the surveillance of antimicrobial resistance determinants.

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.

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.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

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.

Plasmonic nanosensors for point-of-care biomarker detection

Advancement of materials along with their fascinating properties play increasingly important role in facilitating the rapid progress in medicine. An excellent example is the recent development of biosensors based on nanomaterials that induce surface plasmon effect for screening biomarkers of various diseases ranging from cancer to Covid-19. The recent global pandemic re-confirmed the trend of real-time diagnosis in public health to be in point-of-care (POC) settings that can screen interested biomarkers at home, or literally anywhere else, at any time. Plasmonic biosensors, thanks to its versatile designs and extraordinary sensitivities, can be scaled into small and portable devices for POC diagnostic tools. In the meantime, efforts are being made to speed up, simplify and lower the cost of the signal readout process including converting the conventional heavy laboratory instruments into lightweight handheld devices. This article reviews the recent progress on the design of plasmonic nanomaterial-based biosensors for biomarker detection with a perspective of POC applications. After briefly introducing the plasmonic detection working mechanisms and devices, the selected highlights in the field focusing on the technology's design including nanomaterials development, structure assembly, and target applications are presented and analyzed. In parallel, discussions on the sensor's current or potential applicability in POC diagnosis are provided. Finally, challenges and opportunities in plasmonic biosensor for biomarker detection, such as the current Covid-19 pandemic and its testing using plasmonic biosensor and incorporation of machine learning algorithms are discussed.

Physics of surface vibrational resonances: pillared phononic crystals, metamaterials, and metasurfaces

The introduction of engineered resonance phenomena on surfaces has opened a new frontier in surface science and technology. Pillared phononic crystals, metamaterials, and metasurfaces are an emerging class of artificial structured media, featuring surfaces that consist of pillars-or branching substructures-standing on a plate or a substrate. A pillared phononic crystal exhibits Bragg band gaps, while a pillared metamaterial may feature both Bragg band gaps and local resonance hybridization band gaps. These two band-gap phenomena, along with other unique wave dispersion characteristics, have been exploited for a variety of applications spanning a range of length scales and covering multiple disciplines in applied physics and engineering, particularly in elastodynamics and acoustics. The intrinsic placement of pillars on a semi-infinite surface-yielding a metasurface-has similarly provided new avenues for the control and manipulation of wave propagation. Classical waves are admitted in pillared media, including Lamb waves in plates and Rayleigh and Love waves along the surfaces of substrates, ranging in frequency from hertz to several gigahertz. With the presence of the pillars, these waves couple with surface resonances richly creating new phenomena and properties in the subwavelength regime and in some applications at higher frequencies as well. At the nanoscale, it was shown that atomic-scale resonances-stemming from nanopillars-alter the fundamental nature of conductive thermal transport by reducing the group velocities and generating mode localizations across the entire spectrum of the constituent material well into the terahertz regime. In this article, we first overview the history and development of pillared materials, then provide a detailed synopsis of a selection of key research topics that involve the utilization of pillars or similar branching substructures in different contexts. Finally, we conclude by providing a short summary and some perspectives on the state of the field and its promise for further future development.

Neural Network-Based On-Chip Spectroscopy Using a Scalable Plasmonic Encoder

Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework using a compact and low-cost on-chip sensing scheme that is not constrained by many of the design trade-offs inherent to grating-based spectroscopy. The system employs a plasmonic spectral encoder chip containing 252 different tiles of nanohole arrays fabricated using a scalable and low-cost imprint lithography method, where each tile has a specific geometry and thus a specific optical transmission spectrum. The illumination spectrum of interest directly impinges upon the plasmonic encoder, and a CMOS image sensor captures the transmitted light without any lenses, gratings, or other optical components in between, making the entire hardware highly compact, lightweight, and field-portable. A trained neural network then reconstructs the unknown spectrum using the transmitted intensity information from the spectral encoder in a feed-forward and noniterative manner. Benefiting from the parallelization of neural networks, the average inference time per spectrum is ∼28 μs, which is much faster compared to other computational spectroscopy approaches. When blindly tested on 14 648 unseen spectra with varying complexity, our deep-learning based system identified 96.86% of the spectral peaks with an average peak localization error, bandwidth error, and height error of 0.19 nm, 0.18 nm, and 7.60%, respectively. This system is also highly tolerant to fabrication defects that may arise during the imprint lithography process, which further makes it ideal for applications that demand cost-effective, field-portable, and sensitive high-resolution spectroscopy tools.

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.

Decoding Optical Data with Machine Learning

Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.

Design of high-performance plasmonic nanosensors by particle swarm optimization algorithm combined with machine learning

Metallic plasmonic nanosensors that are ultra-sensitive, label-free, and operate in real time hold great promise in the field of chemical and biological research. Conventionally, the design of these nanostructures has strongly relied on time-consuming electromagnetic simulations that iteratively solve Maxwell's equations to scan multi-dimensional parameter space until the desired sensing performance is attained. Here, we propose an algorithm based on particle swarm optimization (PSO), which in combination with a machine learning (ML) model, is used to design plasmonic sensors. The ML model is trained with the geometric structure and sensing performance of the plasmonic sensor to accurately capture the geometry-sensing performance relationships, and the well-trained ML model is then applied to the PSO algorithm to obtain the plasmonic structure with the desired sensing performance. Using the trained ML model to predict the sensing performance instead of using complex electromagnetic calculation methods allows the PSO algorithm to optimize the solutions fours orders of magnitude faster. Implementation of this composite algorithm enabled us to quickly and accurately realize a nanoridge plasmonic sensor with sensitivity as high as 142,500 nm/RIU. We expect this efficient and accurate approach to pave the way for the design of nanophotonic devices in future.

Nanocytometer for smart analysis of peripheral blood and acute myeloid leukemia: a pilot study

We realize an ultracompact nanocytometer for real-time impedimetric detection and classification of subpopulations of living cells. Nanoscopic nanowires in a microfluidic channel act as nanocapacitors and measure in real time the change of the amplitude and phase of the output voltage and, thus, the electrical properties of living cells. We perform the cell classification in the human peripheral blood (PBMC) and demonstrate for the first time the possibility to discriminate monocytes and subpopulations of lymphocytes in a label-free format. Further, we demonstrate that the PBMC of acute myeloid leukemia and healthy samples grant the label free identification of the disease. Using the algorithm based on machine learning, we generated specific data patterns to discriminate healthy donors and leukemia patients. Such a solution has the potential to improve the traditional diagnostics approaches with respect to the overall cost and time effort, in a label-free format, and restrictions of the complex data analysis.

Elevating Chemistry Research with a Modern Electronics Toolkit

With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.

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

We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular disease (CVD). A machine learning-based framework was developed to (1) determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a sensing membrane, and (2) to accurately infer target analyte concentration. Using a custom-designed handheld VFA reader, a clinical study with 85 human samples showed a competitive coefficient-of-variation of 11.2% and linearity of R 2 = 0.95 among blindly-tested VFAs in the hsCRP range (i.e., 0-10 mg/L). We also demonstrated a mitigation of the hook-effect due to the multiplexed immunoreactions on the sensing membrane. This paper-based computational VFA could expand access to CVD testing, and the presented framework can be broadly used to design cost-effective and mobile point-of-care sensors.

Portable and field-deployed surface plasmon resonance and plasmonic sensors

Plasmonic sensors are ideally suited for the design of small, integrated, and portable devices that can be employed in situ for the detection of analytes relevant to environmental sciences, clinical diagnostics, infectious diseases, food, and industrial applications. To successfully deploy plasmonic sensors, scaled-down analytical devices based on surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) must integrate optics, plasmonic materials, surface chemistry, fluidics, detectors and data processing in a functional instrument with a small footprint. The field has significantly progressed from the implementation of the various components in specifically designed prism-based instruments to the use of nanomaterials, optical fibers and smartphones to yield increasingly portable devices, which have been shown for a number of applications in the laboratory and deployed on site for environmental, biomedical/clinical, and food applications. A roadmap to deploy plasmonic sensors is provided by reviewing the current successes and by laying out the directions the field is currently taking to increase the use of field-deployed plasmonic sensors at the point-of-care, in the environment and in industries.

Scalable Fabrication of Quasi-One-Dimensional Gold Nanoribbons for Plasmonic Sensing

Plasmonic nanostructures have a wide range of applications, including chemical and biological sensing. However, the development of techniques to fabricate submicrometer-sized plasmonic structures over large scales remains challenging. We demonstrate a high-throughput, cost-effective approach to fabricate Au nanoribbons via chemical lift-off lithography (CLL). Commercial HD-DVDs were used as large-area templates for CLL. Transparent glass slides were coated with Au/Ti films and functionalized with self-assembled alkanethiolate monolayers. Monolayers were patterned with lines via CLL. The lifted-off, exposed regions of underlying Au were selectively etched into large-area grating-like patterns (200 nm line width; 400 nm pitch; 60 nm height). After removal of the remaining monolayers, a thin In2O3 layer was deposited and the resulting gratings were used as plasmonic sensors. Distinct features in the extinction spectra varied in their responses to refractive index changes in the solution environment with a maximum bulk sensitivity of ∼510 nm/refractive index unit. Sensitivity to local refractive index changes in the near-field was also achieved, as evidenced by real-time tracking of lipid vesicle or protein adsorption. These findings show how CLL provides a simple and economical means to pattern large-area plasmonic nanostructures for applications in optoelectronics and sensing.

Metasurfaces-based imaging and applications: from miniaturized optical components to functional imaging platforms

This review focuses on the imaging applications of metasurfaces. These optical elements provide a unique platform to control light; not only do they have a reduced size and complexity compared to conventional imaging systems but they also enable novel imaging modalities, such as functional-imaging techniques. This review highlights the development of metalenses, from their basic principles, to the achievement of achromatic and tunable lenses, and metasurfaces implemented in functional optical imaging applications.

Optical Interrogation Techniques for Nanophotonic Biochemical Sensors

The manipulation of light via nanoengineered surfaces has excited the optical community in the past few decades. Among the many applications enabled by nanophotonic devices, sensing has stood out due to their capability of identifying miniscule refractive index changes. In particular, when free-space propagating light effectively couples into subwavelength volumes created by nanostructures, the strongly-localized near-fields can enhance light’s interaction with matter at the nanoscale. As a result, nanophotonic sensors can non-destructively detect chemical species in real-time without the need of exogenous labels. The impact of such nanophotonic devices on biochemical sensor development became evident as the ever-growing research efforts in the field started addressing many critical needs in biomedical sciences, such as low-cost analytical platforms, simple quantitative bioassays, time-resolved sensing, rapid and multiplexed detection, single-molecule analytics, among others. In this review, the optical transduction methods used to interrogate optical resonances of nanophotonic sensors will be highlighted. Specifically, the optical methodologies used thus far will be evaluated based on their capability of addressing key requirements of the future sensor technologies, including miniaturization, multiplexing, spatial and temporal resolution, cost and sensitivity.

Deep Learning Approach for Enhanced Detection of Surface Plasmon Scattering

A deep learning approach has been taken to improve detection characteristics of surface plasmon microscopy (SPM) of light scattering. Deep learning based on the convolutional neural network algorithm was used to estimate the effect of scattering parameters, mainly the number of scatterers. The improvement was assessed on a quantitative basis by applying the approach to SPM images formed by coherent interference of scatterers. It was found that deep learning significantly improves the accuracy over conventional detection: the enhancement in the accuracy was shown to be significantly higher by almost 6 times and useful for scattering by polydisperse mixtures. This suggests that deep learning can be used to find scattering objects effectively in the noisy environment. Furthermore, deep learning can be extended directly to label-free molecular detection assays and provide considerably improved detection in imaging and microscopy techniques.

Nanoplasmonic Approaches for Sensitive Detection and Molecular Characterization of Extracellular Vesicles

All cells release a multitude of nanoscale extracellular vesicles (nEVs) into circulation, offering immense potential for new diagnostic strategies. Yet, clinical translation for nEVs remains a challenge due to their vast heterogeneity, our insufficient ability to isolate subpopulations, and the low frequency of disease-associated nEVs in biofluids. The growing field of nanoplasmonics is poised to address many of these challenges. Innovative materials engineering approaches based on exploiting nanoplasmonic phenomena, i.e., the unique interaction of light with nanoscale metallic materials, can achieve unrivaled sensitivity, offering real-time analysis and new modes of medical and biological imaging. We begin with an introduction into the basic structure and function of nEVs before critically reviewing recent studies utilizing nanoplasmonic platforms to detect and characterize nEVs. For the major techniques considered, surface plasmon resonance (SPR), localized SPR, and surface enhanced Raman spectroscopy (SERS), we introduce and summarize the background theory before reviewing the studies applied to nEVs. Along the way, we consider notable aspects, limitations, and considerations needed to apply plasmonic technologies to nEV detection and analysis.

Array-based "Chemical Nose" Sensing in Diagnostics and Drug Discovery

Array-based sensor "chemical nose/tongue" platforms are inspired by the mammalian olfactory system. Multiple sensor elements in these devices selectively interact with target analytes, producing a distinct pattern of response and enabling analyte identification. This approach offers unique opportunities relative to "traditional" highly specific sensor elements such as antibodies. Array-based sensors excel at distinguishing small changes in complex mixtures, and this capability is being leveraged for chemical biology studies and clinical pathology, enabled by a diverse toolkit of new molecular, bioconjugate and nanomaterial technologies. Innovation in the design and analysis of arrays provides a robust set of tools for advancing biomedical goals, including precision medicine.

Color-sensitive and spectrometer-free plasmonic sensor for biosensing applications

A color-sensitive and spectrometer-free sensing method using plasmonic nanohole arrays and the color components, L* , a* , and b* , of the CIELAB defined by the international commission on illumination (CIE) is introduced for the analysis of optically transparent materials in the visible range. Spectral analysis based on plasmonic nanoparticles or nanostructures can be applied to real-time bio-detection, but complex optical instrumentations and low spatial resolution have limited the sensing ability. Therefore, we take an advantage of color image processing instead of spectral analysis which induces the distinctive color information of plasmonic nanohole arrays with different transparent materials. It guarantees high spatial resolution which is essential to bio-detection such as living cells. To establish our sensing platform, the color components, L* , a* , and b* , were extracted from photo images by an image sensor, statistically processed using a JAVA program, and finally utilized as three individual sensing factors. Additionally, our study on a correlation between the spacing of plasmonic sensors and the color sensitivity to the refractive index reveals geometrically optimal conditions of nanohole arrays. The weighted mean calculation with the three individual sensing factors offers an enhanced distinction of the optical difference for transparent materials. In this work, a color sensitivity of 156.94 RIU^-1 and a minimum mean absolute error of 1.298×10^-4 RIU were achieved. The difference in the refractive index can be recognized up to 10^-4 level with the suggested sensing platform and the signal process. This unique color-sensitive sensing method enables a simple, easy-to-control, and highly accurate analysis without complicated measurement systems including a spectrometer. Therefore, our sensing platform can be applied as a very powerful tool to in-situ label-free bio-detection fields.

Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance

Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient's sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patient-tailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria "superbugs." This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?

Plasmonic molecular assays: Recent advances and applications for mobile health

Plasmonics-based biosensing assays have been extensively employed for biomedical applications. Significant advancements in use of plasmonic assays for the construction of point-of-care (POC) diagnostic methods have been made to provide effective and urgent health care of patients, especially in resourcelimited settings. This rapidly progressive research area, centered on the unique surface plasmon resonance (SPR) properties of metallic nanostructures with exceptional absorption and scattering abilities, has greatly facilitated the development of cost-effective, sensitive, and rapid strategies for disease diagnostics and improving patient healthcare in both developed and developing worlds. This review highlights the recent advances and applications of plasmonic technologies for highly sensitive protein and nucleic acid biomarker detection. In particular, we focus on the implementation and penetration of various plasmonic technologies in conventional molecular diagnostic assays, and discuss how such modification has resulted in simpler, faster, and more sensitive alternatives that are suited for point-of-use. Finally, integration of plasmonic molecular assays with various portable POC platforms for mobile health applications are highlighted.

Nanoparticle-Enhanced Plasmonic Biosensor for Digital Biomarker Detection in a Microarray

Nanoplasmonic devices have become a paradigm for biomolecular detection enabled by enhanced light-matter interactions in the fields from biological and pharmaceutical research to medical diagnostics and global health. In this work, we present a bright-field imaging plasmonic biosensor that allows visualization of single subwavelength gold nanoparticles (NPs) on large-area gold nanohole arrays (Au-NHAs). The sensor generates image heatmaps that reveal the locations of single NPs as high-contrast spikes, enabling the detection of individual NP-labeled molecules. We implemented the proposed method in a sandwich immunoassay for the detection of biotinylated bovine serum albumin (bBSA) and human C-reactive protein (CRP), a clinical biomarker of acute inflammatory diseases. Our method can detect 10 pg/mL of bBSA and 27 pg/mL CRP in 2 h, which is at least 4 orders of magnitude lower than the clinically relevant concentrations. Our sensitive and rapid detection approach paired with the robust large-area plasmonic sensor chips, which are fabricated using scalable and low-cost manufacturing, provides a powerful platform for multiplexed biomarker detection in various settings.

Mobile Technologies for the Discovery, Analysis, and Engineering of the Global Microbiome

The microbiome has been heralded as a gauge of and contributor to both human health and environmental conditions. Current challenges in probing, engineering, and harnessing the microbiome stem from its microscopic and nanoscopic nature, diversity and complexity of interactions among its members and hosts, as well as the spatiotemporal sampling and in situ measurement limitations induced by the restricted capabilities and norm of existing technologies, leaving some of the constituents of the microbiome unknown. To facilitate significant progress in the microbiome field, deeper understanding of the constituents' individual behavior, interactions with others, and biodiversity are needed. Also crucial is the generation of multimodal data from a variety of subjects and environments over time. Mobile imaging and sensing technologies, particularly through smartphone-based platforms, can potentially meet some of these needs in field-portable, cost-effective, and massively scalable manners by circumventing the need for bulky, expensive instrumentation. In this Perspective, we outline how mobile sensing and imaging technologies could lead the way to unprecedented insight into the microbiome, potentially shedding light on various microbiome-related mysteries of today, including the composition and function of human, animal, plant, and environmental microbiomes. Finally, we conclude with a look at the future, propose a computational microbiome engineering and optimization framework, and discuss its potential impact and applications.

Nanoplasmonic sensors for biointerfacial science

In recent years, nanoplasmonic sensors have become widely used for the label-free detection of biomolecules across medical, biotechnology, and environmental science applications. To date, many nanoplasmonic sensing strategies have been developed with outstanding measurement capabilities, enabling detection down to the single-molecule level. One of the most promising directions has been surface-based nanoplasmonic sensors, and the potential of such technologies is still emerging. Going beyond detection, surface-based nanoplasmonic sensors open the door to enhanced, quantitative measurement capabilities across the biointerfacial sciences by taking advantage of high surface sensitivity that pairs well with the size of medically important biomacromolecules and biological particulates such as viruses and exosomes. The goal of this review is to introduce the latest advances in nanoplasmonic sensors for the biointerfacial sciences, including ongoing development of nanoparticle and nanohole arrays for exploring different classes of biomacromolecules interacting at solid-liquid interfaces. The measurement principles for nanoplasmonic sensors based on utilizing the localized surface plasmon resonance (LSPR) and extraordinary optical transmission (EOT) phenomena are first introduced. The following sections are then categorized around different themes within the biointerfacial sciences, specifically protein binding and conformational changes, lipid membrane fabrication, membrane-protein interactions, exosome and virus detection and analysis, and probing nucleic acid conformations and binding interactions. Across these themes, we discuss the growing trend to utilize nanoplasmonic sensors for advanced measurement capabilities, including positional sensing, biomacromolecular conformation analysis, and real-time kinetic monitoring of complex biological interactions. Altogether, these advances highlight the rich potential of nanoplasmonic sensors and the future growth prospects of the community as a whole. With ongoing development of commercial nanoplasmonic sensors and analytical models to interpret corresponding measurement data in the context of biologically relevant interactions, there is significant opportunity to utilize nanoplasmonic sensing strategies for not only fundamental biointerfacial science, but also translational science applications related to clinical medicine and pharmaceutical drug development among countless possibilities.

Paper-based assays for urine analysis

A transformation of the healthcare industry is necessary and imminent: hospital-centered, reactive care will soon give way to proactive, person-centered care which focuses on individuals' well-being. However, this transition will only be made possible through scientific innovation. Next-generation technologies will be the key to developing affordable and accessible care, while also lowering the costs of healthcare. A promising solution to this challenge is low-cost continuous health monitoring; this approach allows for effective screening, analysis, and diagnosis and facilitates proactive medical intervention. Urine has great promise for being a key resource for health monitoring; unlike blood, it can be collected effortlessly on a daily basis without pain or the need for special equipment. Unfortunately, the commercial rapid urine analysis tests that exist today can only go so far-this is where the promise of microfluidic devices lies. Microfluidic devices have a proven record of being effective analytical devices, capable of controlling the flow of fluid samples, containing reaction and detection zones, and displaying results, all within a compact footprint. Moving past traditional glass- and polymer-based microfluidics, paper-based microfluidic devices possess the same diagnostic ability, with the added benefits of facile manufacturing, low-cost implementation, and disposability. Hence, we review the recent progress in the application of paper-based microfluidics to urine analysis as a solution to providing continuous health monitoring for proactive care. First, we present important considerations for point-of-care diagnostic devices. We then discuss what urine is and how paper functions as the substrate for urine analysis. Next, we cover the current commercial rapid tests that exist and thereby demonstrate where paper-based microfluidic urine analysis devices may fit into the commercial market in the future. Afterward, we discuss various fabrication techniques that have been recently developed for paper-based microfluidic devices. Transitioning from fabrication to implementation, we present some of the clinically implemented urine assays and their importance in healthcare and clinical diagnosis, with a focus on paper-based microfluidic assays. We then conclude by providing an overview of select biomarker research tailored towards urine diagnostics. This review will demonstrate the applicability of paper-based assays for urine analysis and where they may fit into the commercial healthcare market.

Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring

Optical compound microscope has been a major tool in biomedical imaging for centuries. Its performance relies on relatively complicated, bulky and expensive lenses and alignment mechanics. In contrast, the lensless microscope digitally reconstructs microscopic images of specimens without using any lenses, as a result of which it can be made much smaller, lighter and lower-cost. Furthermore, the limited space-bandwidth product of objective lenses in a conventional microscope can be significantly surpassed by a lensless microscope. Such lensless imaging designs have enabled high-resolution and high-throughput imaging of specimens using compact, portable and cost-effective devices to potentially address various point-of-care, global-health and telemedicine related challenges. In this review, we discuss the operation principles and the methods behind lensless digital holographic on-chip microscopy. We also go over various applications that are enabled by cost-effective and compact implementations of lensless microscopy, including some recent work on air quality monitoring, which utilized machine learning for high-throughput and accurate quantification of particulate matter in air. Finally, we conclude with a brief future outlook of this computational imaging technology.