Optical imaging techniques for point-of-care diagnostics

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH-, CO32-, PO43-, NH3, ClO-, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001-1.0 mol L-1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864-0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

Birefringence mapping of biological tissues based on polarization sensitive non-interferometric quantitative phase imaging technique

OBJECTIVE

Oral cancer is a leading cause of mortality globally, particularly affecting developing regions where oral hygiene is often overlooked. The optical properties of tissues are vital for diagnostics, with polarization imaging emerging as a label-free, contrast-enhancing technique widely employed in medical and scientific research over past few decades.

MATERIALS AND METHODS

We present a novel polarization sensitive quantitative phase imaging of biological tissues by incorporating the conventional polarization microscope and transport of intensity equation-based phase retrieval algorithm. This integration provides access to the birefringence mapping of biological tissues. The inherent optical anisotropy in biological tissues induces the polarization dependent refractive index variations which can provide the detailed insights into the birefringence characteristics of their extracellular constituents. Experimental investigations were conducted on both normal and cancerous oral tissue samples by recording a set of three polarization intensity images for each case with a step size of 2 μm.

RESULTS

A noteworthy increment in birefringence quantification was observed in cancerous as compared to the normal tissues, attributed to the proliferation of abnormal cells during cancer progression. The mean birefringence values were calculated for both normal and cancerous tissues, revealing a significant increase in birefringence of cancerous tissues (2.1 ± 0.2) × 10-2 compared to normal tissues (0.8 ± 0.2) × 10-2. Data were collected from 8 patients in each group under identical experimental conditions.

CONCLUSION

This polarization sensitive non-interferometric optical approach demonstrated effective discrimination between cancerous and normal tissues, with various parameters indicating elevated values in cancerous tissues.

AAPM Task Group Report 311: Guidance for performance evaluation of fluorescence-guided surgery systems

The last decade has seen a large growth in fluorescence-guided surgery (FGS) imaging and interventions. With the increasing number of clinical specialties implementing FGS, the range of systems with radically different physical designs, image processing approaches, and performance requirements is expanding. This variety of systems makes it nearly impossible to specify uniform performance goals, yet at the same time, utilization of different devices in new clinical procedures and trials indicates some need for common knowledge bases and a quality assessment paradigm to ensure that effective translation and use occurs. It is feasible to identify key fundamental image quality characteristics and corresponding objective test methods that should be determined such that there are consistent conventions across a variety of FGS devices. This report outlines test methods, tissue simulating phantoms and suggested guidelines, as well as personnel needs and professional knowledge bases that can be established. This report frames the issues with guidance and feedback from related societies and agencies having vested interest in the outcome, coming from an independent scientific group formed from academics and international federal agencies for the establishment of these professional guidelines.

Additive manufacturing leveraged microfluidic setup for sample to answer colorimetric detection of pathogens

Colorimetric readout for the detection of infectious diseases is gaining traction at the point of care/need owing to its ease of analysis and interpretation, and integration potential with highly specific loop-mediated amplification (LAMP) assays. However, coupling colorimetric readout with LAMP is rife with challenges including, rapidity, inter-user variability, colorimetric signal quantification, and user involvement in sequential steps of the LAMP assay, hindering its application. To address these challenges, for the first time, we propose a remotely smartphone-operated automated setup consisting of (i) an additively manufactured microfluidic cartridge, (ii) a portable reflected-light imaging setup with controlled epi-illumination (PRICE) module, and (iii) a control and data analysis module. The microfluidic cartridge facilitates sample collection, lysis, mixing of amplification reagents stored on-chip, and subsequent isothermal heating for initiation of amplification in a novel way by employing tunable elastomeric chambers and auxiliary components (heaters and linear actuators). PRICE offers a new imaging setup that captures the colorimetric change of the amplification media over a plasmonic nanostructured substrate in a controlled and noise-free environment for rapid minute-scale nucleic acid detection. The control and data analysis module employs microprocessors to automate cartridge operation in tandem with the imaging module. The different device components were characterized individually and finally, as a proof of concept, SARS-CoV-2 wild-type RNA was detected with a turnaround time of 13 minutes, showing the device's clinical feasibility. The suggested automated device can be adopted in future iterations for other detection and molecular assays that require sequential fluid handling steps.

Sensing of triglyceride concentration in blood solution using photoacoustic microscopy

The level of triglyceride (TG) in blood is essential to human health, and hypertriglyceridemia (TG level > 150 mg/dL) would lead to cardiovascular disease and acute pancreatitis that threaten human life. Routine methods for measuring the TG level in blood depend on a lipid panel blood test, which is invasive and not convenient. Here, we use photoacoustic (PA) microscopy to test the PA amplitude of blood solutions (based on hemoglobin powder as well as flowing sheep blood) with different TG concentrations. Interestingly, we observe that the PA amplitude increases with increasing TG concentration in blood solutions, which is attributed to the increase of the Grüneisen coefficient. The preliminary in vitro study shows that the PA methodology is able to detect the TG level down to 450 mg/dL. This finding provides an opportunity for using photoacoustics to noninvasively diagnose hypertriglyceridemia.

Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings

PURPOSE

Automated diagnosis of urogenital schistosomiasis using digital microscopy images of urine slides is an essential step toward the elimination of schistosomiasis as a disease of public health concern in Sub-Saharan African countries. We create a robust image dataset of urine samples obtained from field settings and develop a two-stage diagnosis framework for urogenital schistosomiasis.

APPROACH

Urine samples obtained from field settings were captured using the Schistoscope device, and S. haematobium eggs present in the images were manually annotated by experts to create the SH dataset. Next, we develop a two-stage diagnosis framework, which consists of semantic segmentation of S. haematobium eggs using the DeepLabv3-MobileNetV3 deep convolutional neural network and a refined segmentation step using ellipse fitting approach to approximate the eggs with an automatically determined number of ellipses. We defined two linear inequality constraints as a function of the overlap coefficient and area of a fitted ellipses. False positive diagnosis resulting from over-segmentation was further minimized using these constraints. We evaluated the performance of our framework on 7605 images from 65 independent urine samples collected from field settings in Nigeria, by deploying our algorithm on an Edge AI system consisting of Raspberry Pi + Coral USB accelerator.

RESULT

The SH dataset contains 12,051 images from 103 independent urine samples and the developed urogenital schistosomiasis diagnosis framework achieved clinical sensitivity, specificity, and precision of 93.8%, 93.9%, and 93.8%, respectively, using results from an experienced microscopist as reference.

CONCLUSION

Our detection framework is a promising tool for the diagnosis of urogenital schistosomiasis as our results meet the World Health Organization target product profile requirements for monitoring and evaluation of schistosomiasis control programs.

Android mobile-platform-based image reconstruction for photoacoustic tomography

Significance

In photoacoustic tomography (PAT), numerous reconstruction algorithms have been utilized to recover initial pressure rise distribution from the acquired pressure waves. In practice, most of these reconstructions are carried out on a desktop/workstation and the mobile-based reconstructions are far-flung. In recent years, mobile phones are becoming so ubiquitous, and most of them encompass a higher computing ability. Hence, realizing PAT image reconstruction on a mobile platform is intrinsic, and it will enhance the adaptability of PAT systems with point-of-care applications.

Aim

To implement PAT image reconstruction in Android-based mobile platforms.

Approach

For implementing PAT image reconstruction in Android-based mobile platforms, we proposed an Android-based application using Python to perform beamforming process in Android phones.

Results

The performance of the developed application was analyzed on different mobile platforms using both simulated and experimental datasets. The results demonstrate that the developed algorithm can accomplish the image reconstruction of in vivo small animal brain dataset in 2.4 s. Furthermore, the developed application reconstructs PAT images with comparable speed and no loss of image quality compared to that on a laptop. Employing a two-fold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming while preserving image quality with minimal degradation.

Conclusions

We proposed an Android-based application that achieves image reconstruction on cheap, small, and universally available phones instead of relatively bulky expensive desktop computers/laptops/workstations. A beamforming speed of 2.4 s is achieved without hampering the quality of the reconstructed image.

Depth-multiplexed ptychographic microscopy for high-throughput imaging of stacked bio-specimens on a chip

Imaging a large number of bio-specimens at high speed is essential for many biomedical applications. The common strategy is to place specimens at different lateral positions and image them sequentially. Here we report a new on-chip imaging strategy, termed depth-multiplexed ptychographic microscopy (DPM), for parallel imaging and sensing at high speed. Different from the common strategy, DPM stacks multiple specimens in the axial direction and images the entire z-stack all at once. In our prototype platform, we modify a low-cost car mirror for programmable steering of the incident laser beam. A blood-coated image sensor is then placed underneath the stacked sample for acquiring the resulting diffraction patterns. With the captured images, we perform blind recovery of the incident beam angle and model different layers of the stacked sample as different coded surfaces for object reconstruction. For in vitro experiment, we demonstrate time-lapse cell culture monitoring by imaging 3 stacked microfluidic channels on the coded sensor. For high-throughput cytometric analysis, we image 5 stacked brain sections with a 205-mm² field of view in ∼50 s. Cytometric analysis is also performed to quantify the cellular proliferation biomarkers on the slides. The DPM approach adds a new degree of freedom for data multiplexing in microscopy, enabling parallel imaging of multiple specimens using a single detector. The demonstrated 6-mm depth of field is among the longest ones in microscopy imaging. The novel depth-multiplexed configuration also complements the miniaturization provided by microfluidics devices, offering a solution for on-chip sensing and imaging with efficient sample handling.

Multi-Illumination Single-Holographic-Exposure Lensless Fresnel (MISHELF) Microscopy: Principles and Biomedical Applications

Lensless holographic microscopy (LHM) comes out as a promising label-free technique since it supplies high-quality imaging and adaptive magnification in a lens-free, compact and cost-effective way. Compact sizes and reduced prices of LHMs make them a perfect instrument for point-of-care diagnosis and increase their usability in limited-resource laboratories, remote areas, and poor countries. LHM can provide excellent intensity and phase imaging when the twin image is removed. In that sense, multi-illumination single-holographic-exposure lensless Fresnel (MISHELF) microscopy appears as a single-shot and phase-retrieved imaging technique employing multiple illumination/detection channels and a fast-iterative phase-retrieval algorithm. In this contribution, we review MISHELF microscopy through the description of the principles, the analysis of the performance, the presentation of the microscope prototypes and the inclusion of the main biomedical applications reported so far.

Partitioning and subsampling statistics in compartment-based quantification methods

The precision of compartment-based quantification methods is subject to multiple effects, of which partitioning and subsampling play a major role. Partitioning is the process of aliquoting the sample liquid and consequently the contained target molecules, whereas subsampling denotes the fact that usually only a portion of a sample is analyzed. In this work, we present a detailed statistical description comprising the effects of partitioning and subsampling on the relative uncertainty of the test result. We show that the state-of-the-art binomial model does not provide accurate results for the level of subsampling present when analyzing the nucleic acid content of single specific cells. Hence, in this work we address partitioning and subsampling effects separately and subsequently combine them to derive the relative uncertainty of a test system and compare it for single cell content analysis and body fluid analysis. In point-of-care test systems the area for partitioning and detection is usually limited, which means that a trade-off between the number of partitions (related to a partitioning uncertainty) and the amount of analyzed volume (related to a subsampling uncertainty) might be inevitable. In case of low target concentration, the subsampling uncertainty is dominant whereas for high target concentration, the partitioning uncertainty increases, and a larger number of partitions is beneficial to minimize the combined uncertainty. We show, that by minimizing the subsampling uncertainty in the test system, the quantification uncertainty of low target concentrations in single cell content analysis is much smaller than in body fluid analysis. In summary, the work provides the methodological basis for a profound statistical evaluation of partitioning and subsampling effects in compartment-based quantification methods and paves the way towards an improved design of future digital quantification devices for highly accurate molecular diagnostic analysis at the point-of-care.

Using an ultra-compact optical system to improve lateral flow immunoassay results quantitatively

The lateral flow immunoassay (LFIA) is a paper-based platform with extensive application in point-of-care (POC) testing and many fields. However, its clinical application is severely limited due to the lack of quantitative ability of standard LFIA tests; this augmentation provides the system with quantifying the signal from magenta-colored AuNPs. To address this issue, we proposed an ultra-compact optical system that allowed LFIAs to be performed more accurately and objectively. The experimental setup consisted of multiple optical accessories manufactured by 3D printing (STEP files were included). A high-resolution printer was used to print out a magenta card model for the LFIA, whose color code, ranging from 255, 255, 255 to 255, 0, 255 in the RGB (red, green, blue) format, represents different levels of magenta color intensity (from 0% to 100%) and thus the results of LFIA test strips. A mathematical model was built using a calibration curve to describe the relationship between magenta color value and reflectance spectrum. In addition, a spectrum module was integrated into the proposed system to identify and quantify LFIA results. This integration represents a pioneering step in developing portable detection techniques that facilitate quantifying LFIA results. Finally, we expect this ultra-compact optical spectroscopy system to have great potential for novel clinical applications.

Advances in metal graphitic nanocapsules for biomedicine

Metal graphitic nanocapsules have the advantages of both graphitic and metal nanomaterials, showing great promise in biomedicine. On one hand, the chemically inert graphitic shells are able to protect the metal core from external environments, quench the fluorescence signal from the biological system, offer robust platform for targeted molecules or drugs loading, and act as stable Raman labels or internal standard molecule. On the other hand, the metal cores with different compositions, sizes, and morphologies show unique physicochemical properties, and further broaden their biomedical functions. In this review, we firstly introduce the preparation, classification, and properties of metal graphitic nanocapsules, then summarize the recent progress of their applications in biodetection, bioimaging, and therapy. Challenges and their development prospects in biomedicine are eventually discussed in detail. We expect the versatile metal graphitic nanocapsules will advance the development of future clinical biomedicine.

Experimental optimization of lensless digital holographic microscopy with rotating diffuser-based coherent noise reduction

Laser-based lensless digital holographic microscopy (LDHM) is often spoiled by considerable coherent noise factor. We propose a novel LDHM method with significantly limited coherent artifacts, e.g., speckle noise and parasitic interference fringes. It is achieved by incorporating a rotating diffuser, which introduces partial spatial coherence and preserves high temporal coherence of laser light, crucial for credible in-line hologram reconstruction. We present the first implementation of the classical rotating diffuser concept in LDHM, significantly increasing the signal-to-noise ratio while preserving the straightforwardness and compactness of the LDHM imaging device. Prior to the introduction of the rotating diffusor, we performed LDHM experimental hardware optimization employing 4 light sources, 4 cameras, and 3 different optical magnifications (camera-sample distances). It was guided by the quantitative assessment of numerical amplitude/phase reconstruction of test targets, conducted upon standard deviation calculation (noise factor quantification), and resolution evaluation (information throughput quantification). Optimized rotating diffuser LDHM (RD-LDHM) method was successfully corroborated in technical test target imaging and examination of challenging biomedical sample (60 µm thick mouse brain tissue slice). Physical minimization of coherent noise (up to 50%) was positively verified, while preserving optimal spatial resolution of phase and amplitude imaging. Coherent noise removal, ensured by proposed RD-LDHM method, is especially important in biomedical inference, as speckles can falsely imitate valid biological features. Combining this favorable outcome with large field-of-view imaging can promote the use of reported RD-LDHM technique in high-throughput stain-free biomedical screening.

Low-intensity illumination for lensless digital holographic microscopy with minimized sample interaction

Exposure to laser light alters cell culture examination via optical microscopic imaging techniques based on label-free coherent digital holography. To mitigate this detrimental feature, researchers tend to use a broader spectrum and lower intensity of illumination, which can decrease the quality of holographic imaging due to lower resolution and higher noise. We study the lensless digital holographic microscopy (LDHM) ability to operate in the low photon budget (LPB) regime to enable imaging of unimpaired live cells with minimized sample interaction. Low-cost off-the-shelf components are used, promoting the usability of such a straightforward approach. We show that recording data in the LPB regime (down to 7 µW of illumination power) does not limit the contrast or resolution of the hologram phase and amplitude reconstruction compared to regular illumination. The LPB generates hardware camera shot noise, however, to be effectively minimized via numerical denoising. The ability to obtain high-quality, high-resolution optical complex field reconstruction was confirmed using the USAF 1951 amplitude sample, phase resolution test target, and finally, live glial restricted progenitor cells (as a challenging strongly absorbing and scattering biomedical sample). The proposed approach based on severely limiting the photon budget in lensless holographic microscopy method can open new avenues in high-throughout (optimal resolution, large field-of-view, and high signal-to-noise-ratio single-hologram reconstruction) cell culture imaging with minimized sample interaction.

Super-Resolution Displacement Spectroscopic Sensing over a Surface "Rainbow"

Subwavelength manipulation of light waves with high precision can enable new and exciting applications in spectroscopy, sensing, and medical imaging. For these applications, miniaturized spectrometers are desirable to enable the on-chip analysis of spectral information. In particular, for imaging-based spectroscopic sensing mechanisms, the key challenge is to determine the spatial-shift information accurately (i.e., the spatial displacement introduced by wavelength shift or biological or chemical surface binding), which is similar to the challenge presented by super-resolution imaging. Here, we report a unique "rainbow" trapping metasurface for on-chip spectrometers and sensors. Combined with super-resolution image processing, the low-setting 4× optical microscope system resolves a displacement of the resonant position within 35 nm on the plasmonic rainbow trapping metasurface with a tiny area as small as 0.002 mm2. This unique feature of the spatial manipulation of efficiently coupled rainbow plasmonic resonances reveals a new platform for miniaturized on-chip spectroscopic analysis with a spectral resolution of 0.032 nm in wavelength shift. Using this low-setting 4× microscope imaging system, we demonstrate a biosensing resolution of 1.92 × 109 exosomes per milliliter for A549-derived exosomes and distinguish between patient samples and healthy controls using exosomal epidermal growth factor receptor (EGFR) expression values, thereby demonstrating a new on-chip sensing system for personalized accurate bio/chemical sensing applications.

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

Portable, multi-modal Raman and fluorescence spectroscopic platform for point-of-care applications

SIGNIFICANCE

Point-of-care (POC) platforms utilizing optical biosensing strategies can achieve on-site detection of biomarkers to improve the quality of care for patients in low-resource settings.

AIM

We aimed to develop a portable, multi-modal spectroscopic platform capable of performing Raman and fluorescence measurements from a single sample site.

APPROACH

We designed the spectroscopic platform in OpticStudio using commercial optical components and built the system on a portable optical breadboard. Two excitation and collection arms were utilized to detect the two optical signals. The multi-modal functionality was validated using ratiometric Raman/fluorescence samples, and the potential utility was demonstrated using a model bioassay for cardiac troponin I.

RESULTS

The designed spectroscopic platform achieved a spectral resolution of 0.67 ± 0.2 nm across the Raman detection range (660 to 770 nm). The ratiometric Raman/fluorescence samples demonstrated no crosstalk between the two detector arms across a gradient of high molar concentrations. Testing of the model bioassay response showed that the integrated approach improved the linearity of the calibration curve from (R² = 0.977) for the Raman only and (R² = 0.972) for the fluorescence only to (R2 = 0.988) for the multi-modal approach.

CONCLUSION

These findings demonstrate the potential impact of a multi-modal POC spectroscopic platform to improve the sensitivity and robustness necessary for biomarker detection.

Accurate automatic object 4D tracking in digital in-line holographic microscopy based on computationally rendered dark fields

Building on Gabor seminal principle, digital in-line holographic microscopy provides efficient means for space–time investigations of large volumes of interest. Thus, it has a pivotal impact on particle tracking that is crucial in advancing various branches of science and technology, e.g., microfluidics and biophysical processes examination (cell motility, migration, interplay etc.). Well-established algorithms often rely on heavily regularized inverse problem modelling and encounter limitations in terms of tracking accuracy, hologram signal-to-noise ratio, accessible object volume, particle concentration and computational burden. This work demonstrates the DarkTrack algorithm—a new approach to versatile, fast, precise, and robust 4D holographic tracking based on deterministic computationally rendered high-contrast dark fields. Its unique capabilities are quantitatively corroborated employing a novel numerical engine for simulating Gabor holographic recording of time-variant volumes filled with predefined dynamic particles. Our solution accounts for multiple scattering and thus it is poised to secure an important gap in holographic particle tracking technology and allow for ground-truth-driven benchmarking and quantitative assessment of tracking algorithms. Proof-of-concept experimental evaluation of DarkTrack is presented via analyzing live spermatozoa. Software supporting both novel numerical holographic engine and DarkTrack algorithm is made open access, which opens new possibilities and sets the stage for democratization of robust holographic 4D particle examination.

Gires-Tournois Immunoassay Platform for Label-Free Bright-Field Imaging and Facile Quantification of Bioparticles

Bright-field imaging of nanoscale bioparticles is a challenging task for optical microscopy because the light-matter interactions of bioparticles are weak on conventional surfaces due to their low refractive index and small size. Alternatively, advanced imaging techniques, including near-field microscopy and phase microscopy, have enabled visualization and quantification of the bioparticles, but they require assistance of sophisticated/customized systems and post-processing with complex established algorithms. Here, a simple and fast immunoassay device, Gires-Tournois immunoassay platform (GTIP) is presented, which provides unique color dynamics in response to optical environment changes and thus enables the label-free bright-field imaging and facile quantification of bioparticles using conventional optical microscopy. Bioparticles on GTIP slow down the velocity of reflected light, leading to vivid color change according to the local particle density and maximizing chromatic contrast for high spatial distinguishability. The particle distribution and density on the surface of the resonator are readily analyzed through 2D raster-scanning-based chromaticity analysis. GTIP offers multiscale sensing capability for target analytes that possess different refractive indices and sizes.

Microscopic Imaging and Labeling Dataset for the Detection of Pneumocystis jirovecii Using Methenamine Silver Staining Method

Pneumocystis jirovecii pneumonia is one of the diseases that most affects immunocompromised patients today, and under certain circumstances, it can be fatal. On the other hand, more and more automatic tools based on artificial intelligence are required every day to help diagnose diseases and thus optimize the resources of the healthcare system. It is therefore important to develop techniques and mechanisms that enable early diagnosis. One of the most widely used techniques in diagnostic laboratories for the detection of its etiological agent, Pneumocystis jirovecii, is optical microscopy. Therefore, an image dataset of 29 different patients is presented in this work, which can be used to detect whether a patient is positive or negative for this fungi. These images were taken in at least four random positions on the specimen holder. The dataset consists of a total of 137 RGB images. Likewise, it contains realistic, annotated, and high-quality microscope images. In addition, we provide image segmentation and labeling that can also be used in numerous studies based on artificial intelligence implementation. The labeling was also validated by an expert, allowing it to be used as a reference in the training of automatic algorithms with supervised learning methods and thus to develop diagnostic assistance systems. Therefore, the dataset will open new opportunities for researchers working in image segmentation, detection, and classification problems related to Pneumocystis jirovecii pneumonia diagnosis.

Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings

For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.

Detection of Human Neutrophil Elastase by Fluorescent Peptide Sensors Conjugated to TEMPO-Oxidized Nanofibrillated Cellulose

Peptide–cellulose conjugates designed for use as optical protease sensors have gained interest for point-of-care (POC) detection. Elevated serine protease levels are often found in patients with chronic illnesses, necessitating optimal biosensor design for POC assessment. Nanocellulose provides a platform for protease sensors as a transducer surface, and the employment of nanocellulose in this capacity combines its biocompatibility and high specific surface area properties to confer sensitive detection of dilute biomarkers. However, a basic understanding of the spatiotemporal relationships of the transducer surface and sensor disposition is needed to improve protease sensor design and development. Here, we examine a tripeptide, fluorogenic elastase biosensor attached to TEMPO-oxidized nanofibrillated cellulose via a polyethylene glycol linker. The synthetic conjugate was found to be active in the presence of human neutrophil elastase at levels comparable to other cellulose-based biosensors. Computational models examined the relationship of the sensor molecule to the transducer surface. The results illustrate differences in two crystallite transducer surfaces ((110) vs. (1−10)) and reveal preferred orientations of the sensor. Finally, a determination of the relative (110) vs. (1−10) orientations of crystals extracted from cotton demonstrates a preference for the (1−10) conformer. This model study potentiates the HNE sensor results for enhanced sensor activity design.

Open-Source, Adaptable, All-in-One Smartphone-Based System for Quantitative Analysis of Point-of-Care Diagnostics

Point-of-care (POC) diagnostics, in particular lateral flow assays (LFA), represent a great opportunity for rapid, precise, low-cost and accessible diagnosis of disease. Especially with the ongoing coronavirus disease 2019 (COVID-19) pandemic, rapid point-of-care tests are becoming everyday tools for identification and prevention. Using smartphones as biosensors can enhance POC devices as portable, low-cost platforms for healthcare and medicine, food and environmental monitoring, improving diagnosis and documentation in remote, low-resource locations. We present an open-source, all-in-one smartphone-based system for quantitative analysis of LFAs. It consists of a 3D-printed photo box, a smartphone for image acquisition, and an R Shiny software package with modular, customizable analysis workflow for image editing, analysis, data extraction, calibration and quantification of the assays. This system is less expensive than commonly used hardware and software, so it could prove very beneficial for diagnostic testing in the context of pandemics, as well as in low-resource countries.

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning

Tomographic flow cytometry by digital holography is an emerging imaging modality capable of collecting multiple views of moving and rotating cells with the aim of recovering their refractive index distribution in 3D. Although this modality allows us to access high-resolution imaging with high-throughput, the huge amount of time-lapse holographic images to be processed (hundreds of digital holograms per cell) constitutes the actual bottleneck. This prevents the system from being suitable for lab-on-a-chip platforms in real-world applications, where fast analysis of measured data is mandatory. Here we demonstrate a significant speeding-up reconstruction of phase-contrast tomograms by introducing in the processing pipeline a multi-scale fully-convolutional context aggregation network. Although it was originally developed in the context of semantic image analysis, we demonstrate for the first time that it can be successfully adapted to a holographic lab-on-chip platform for achieving 3D tomograms through a faster computational process. We trained the network with input-output image pairs to reproduce the end-to-end holographic reconstruction process, i.e. recovering quantitative phase maps (QPMs) of single cells from their digital holograms. Then, the sequence of QPMs of the same rotating cell is used to perform the tomographic reconstruction. The proposed approach significantly reduces the computational time for retrieving tomograms, thus making them available in a few seconds instead of tens of minutes, while essentially preserving the high-content information of tomographic data. Moreover, we have accomplished a compact deep convolutional neural network parameterization that can fit into on-chip SRAM and a small memory footprint, thus demonstrating its possible exploitation to provide onboard computations for lab-on-chip devices with low processing hardware resources.

Wash-free operation of smartphone-integrated optical immunosensor using retroreflective microparticles

Herein, we introduce a smartphone-integrated immunosensor based on non-spectroscopic optical detection. Sedimentation of the retroreflector and gentle inversion of the microfluidic chip was chosen as biosensing principles to ensure minimal human involvement. To realize this, wash-free immunosensing was implemented on a polymeric microfluidic chip device fabricated for light signal penetration in retroreflection signal acquisition. Applying a transparent chip and passive modulation of retroreflectors enabled the minimization of human error during sensing. In addition, a retroreflection-detectable optical gadget was constructed for integration with the commercial smartphone. The gadget had an optical chamber that induced retroreflection by integration with a smartphone. When the micro-sized reflector, named the retroreflective Janus microparticle, reacted on the sensing surface, the incident light was retroreflected towards the image sensor and quantified by a smartphone-installed Android application package. The developed application package features include time-lapse image capture performed by manipulating LED flash and camera modules, and quantification of retroreflected signal counts by image processing of time-lapse images. With this platform, the user could independently commence optical signal processing without a complicated optical setup and running software on a PC, and sensitive and reproducible immunosensing results could be obtained. The applicability test for creatine kinase-myocardial band detection from the buffer to serum was conducted and presented a calibration curve of 0-1000 ng/mL within 1 h. With the developed system, we believe that the applicability of the platform in bioanalytical detection can be expanded.

Design, Calibration, and Application of a Robust, Cost-Effective, and High-Resolution Lensless Holographic Microscope

Lensless holographic microscope (LHM) is an emerging very promising technology that provides high-quality imaging and analysis of biological samples without utilizing any lens for imaging. Due to its small size and reduced price, LHM can be a very useful tool for the point-of-care diagnosis of diseases, sperm assessment, or microfluidics, among others, not only employed in advanced laboratories but also in poor and/or remote areas. Recently, several LHMs have been reported in the literature. However, complete characterization of their optical parameters remains not much presented yet. Hence, we present a complete analysis of the performance of a compact, reduced cost, and high-resolution LHM. In particular, optical parameters such as lateral and axial resolutions, lateral magnification, and field of view are discussed into detail, comparing the experimental results with the expected theoretical values for different layout configurations. We use high-resolution amplitude and phase test targets and several microbeads to characterize the proposed microscope. This characterization is used to define a balanced and matched setup showing a good compromise between the involved parameters. Finally, such a microscope is utilized for visualization of static, as well as dynamic biosamples.

Assessment of a Smartphone-Based Loop-Mediated Isothermal Amplification Assay for Detection of SARS-CoV-2 and Influenza Viruses

Importance

A critical need exists in low-income and middle-income countries for low-cost, low-tech, yet highly reliable and scalable testing for SARS-CoV-2 virus that is robust against circulating variants.

Objective

To assess whether a smartphone-based assay is suitable for SARS-CoV-2 and influenza virus testing without requiring specialized equipment, accessory devices, or custom reagents.

Design, Setting, and Participants

This cohort study enrolled 2 subgroups of participants (symptomatic and asymptomatic) at Santa Barbara Cottage Hospital. The symptomatic group consisted of 20 recruited patients who tested positive for SARS-CoV-2 with symptoms; 30 asymptomatic patients were recruited from the same community, through negative admission screening tests for SARS-CoV-2. The smartphone-based real-time loop-mediated isothermal amplification (smaRT-LAMP) was first optimized for analysis of human saliva samples spiked with either SARS-CoV-2 or influenza A or B virus; these results then were compared with those obtained by side-by-side analysis of spiked samples using the Centers for Disease Control and Prevention (CDC) criterion-standard reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) assay. Next, both assays were used to test for SARS-CoV-2 and influenza viruses present in blinded clinical saliva samples obtained from 50 hospitalized patients. Statistical analysis was performed from May to June 2021.

Exposures

Testing for SARS-CoV-2 and influenza A and B viruses.

Main Outcomes and Measures

SARS-CoV-2 and influenza infection status and quantitative viral load were determined.

Results

Among the 50 eligible participants with no prior SARS-CoV-2 infection included in the study, 29 were men. The mean age was 57 years (range, 21 to 93 years). SmaRT-LAMP exhibited 100% concordance (50 of 50 patient samples) with the CDC criterion-standard diagnostic for SARS-CoV-2 sensitivity (20 of 20 positive and 30 of 30 negative) and for quantitative detection of viral load. This platform also met the CDC criterion standard for detection of clinically similar influenza A and B viruses in spiked saliva samples (n = 20), and in saliva samples from hospitalized patients (50 of 50 negative). The smartphone-based LAMP assay was rapid (25 minutes), sensitive (1000 copies/mL), low-cost (<$7/test), and scalable (96 samples/phone).

Conclusions and Relevance

In this cohort study of saliva samples from patients, the smartphone-based LAMP assay detected SARS-CoV-2 infection and exhibited concordance with RT-qPCR tests. These findings suggest that this tool could be adapted in response to novel CoV-2 variants and other pathogens with pandemic potential including influenza and may be useful in settings with limited resources.

A Smartphone-Based Detection Method of Colloidal Gold Immunochromatographic Strip

The outbreak of the new coronavirus (SARS-CoV-2) infection has become a global public health crisis. Antigen detection strips (colloidal gold) can be widely used in novel coronavirus clinical screening and can even be extended to home self-testing, which provides a practical and effective way for people to obtain health status information away from the crowd. In this paper, a colloidal gold detection system without complex devices is proposed, which is based on smartphone usage along with a mobile-phone software embedded with normalization algorithms and a special designed background paper. The basic principle of the device relies on image processing. First, the data of the green channel of the image captured by a smartphone are selected to be processed. Second, the calibration curves are established using standard black and white card, and the calibration values under different detection environments are obtained by calibration curves. Finally, to verify the validity of the proposed method, various standard solutions with different concentrations are tested. Results show that this method can eliminate the influence of different environments on the test results, the test results in different detection environments have good stability and the variation coefficients are less than 5%. It fully proves that the detection system designed in this paper can detect the result of colloidal gold immunochromatographic strip in time, conveniently and accurately in different environments.

Integrated plasmonic biosensor on a vertical cavity surface emitting laser platform

Plasmonic devices can modulate light beyond the diffraction limit and thus have unique advantages in realizing an ultracompact feature size. However, in most cases, external light coupling systems are needed, resulting in a prohibitively bulky footprint. In this paper, we propose an integrated plasmonic biosensor on a vertical cavity surface emitting laser (VCSEL) platform. The plasmonic resonant wavelength of the nanohole array was designed to match (detune) with the emission peak wavelength of the VCSEL before (after) binding the molecules, thus the refractive index that represents the concentration of the molecule could be measured by monitoring the light output intensity. It shows that high contrast with relative intensity difference of 98.8% can be achieved for molecular detection at conventional concentrations. The size of the device chip could be the same as a VCSEL chip with regular specification of hundreds of micrometers in length and width. These results suggest that the proposed integrated sensor device offers great potential in realistic applications.

Recent Advances Towards Point-Of-Care Applications of Surface-Enhanced Raman Scattering Sensing

The ability to accurately diagnose at the point of care is crucial in many pathologies. However, current standard diagnostic practices can only be performed in specialized health or laboratory settings. To move diagnostic methods from a specialized lab to the point of care many alternate methods have been developed and proposed. Among them surface-enhanced Raman scattering (SERS) sensing offers advantageous features, such as simultaneous detection of multiple biotargets and increased accuracy. Many groups have been working towards the translation of SERS sensing methods from the lab to the point of need. In this mini review, we discuss interesting and recent developments in this effort, focusing on how different sensing mechanism can be used in point-of-care testing applications of SERS.

Biophotonic probes for bio-detection and imaging

The rapid development of biophotonics and biomedical sciences makes a high demand on photonic structures to be interfaced with biological systems that are capable of manipulating light at small scales for sensitive detection of biological signals and precise imaging of cellular structures. However, conventional photonic structures based on artificial materials (either inorganic or toxic organic) inevitably show incompatibility and invasiveness when interfacing with biological systems. The design of biophotonic probes from the abundant natural materials, particularly biological entities such as virus, cells and tissues, with the capability of multifunctional light manipulation at target sites greatly increases the biocompatibility and minimizes the invasiveness to biological microenvironment. In this review, advances in biophotonic probes for bio-detection and imaging are reviewed. We emphatically and systematically describe biological entities-based photonic probes that offer appropriate optical properties, biocompatibility, and biodegradability with different optical functions from light generation, to light transportation and light modulation. Three representative biophotonic probes, i.e., biological lasers, cell-based biophotonic waveguides and bio-microlenses, are reviewed with applications for bio-detection and imaging. Finally, perspectives on future opportunities and potential improvements of biophotonic probes are also provided.

Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip

Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and low-cost sensor for fluorescence detection. The proposed system provides stable DNA amplification. A quantitative analysis of fluorescence intensity changes shows the camera’s performance compared with that of commercial instruments. Changes in the performance between the experiments and the sets were also observed based on the threshold cycle values in a commercial RT-PCR system. The overall difference in the measured threshold cycles between the commercial system and the proposed camera was only 0.76 cycles, verifying the performance of the proposed system. The set calibration even reduced the difference to 0.41 cycles, which was less than the experimental variation in the commercial system, and there was no difference in performance.

Clinically oriented Alzheimer's biosensors: expanding the horizons towards point-of-care diagnostics and beyond

The development of minimally invasive and easy-to-use sensor devices is of current interest for ultrasensitive detection and signal recognition of Alzheimer's disease (AD) biomarkers. Over the years, tremendous effort has been made on diagnostic platforms specifically targeting neurological markers for AD in order to replace the conventional, laborious, and invasive sampling-based approaches. However, the sophistication of analytical outcomes, marker inaccessibility, and material validity strongly limit the current strategies towards effectively predicting AD. Recently, with the promising progress in biosensor technology, the realization of a clinically applicable sensing platform has become a potential option to enable early diagnosis of AD and other neurodegenerative diseases. In this review, various types of biosensors, which include electrochemical, fluorescent, plasmonic, photoelectrochemical, and field-effect transistor (FET)-based sensor configurations, with better clinical applicability and analytical performance towards AD are highlighted. Moreover, the feasibility of these sensors to achieve point-of-care (POC) diagnosis is also discussed. Furthermore, by grafting nanoscale materials into biosensor architecture, the remarkable enhancement in durability, functionality, and analytical outcome of sensor devices is presented. Finally, future perspectives on further translational and commercialization pathways of clinically driven biosensor devices for AD are discussed and summarized.

Development and Application of Mobile Apps for Molecular Sensing: A Review

Modern smartphone-based sensing devices are generally standalone detection platforms that can transduce signals (via the built-in USB port, audio jack, or camera), perform analysis through mobile applications (apps), and display results on the screen/user interface. The advancement toward this ultimate form of on-site chemical analysis and point-of-care diagnosis is tied closely with the evolution of mobile technology. Previous reviews in the field mainly focused on the physical platforms while overlooking the role of mobile apps in such devices. There exist three general stages throughout the development: (1) early generation telemedicine, (2) mobile phone-assisted clinical diagnosis (without apps), and (3) mobile app-based sensing devices for various analytes. This review presents the key breakthroughs during each stage, recent development, remaining challenges, and future perspectives of the field. Representative examples, spanning from the pioneering point-of-care testing to the latest devices with integrated mobile apps, are classified by their sensing mechanisms. The review also discusses the scarcity of open-source apps dedicated to molecular sensing. With the introduction of more open-source and commercial apps, the mobile app-based detection system is anticipated to dominate point-of-care diagnosis and on-site molecular sensing in our opinion.

Cancer Holography for Personalized Medicine

Digital holography can measure the 3D physiology and motion of cancer cells, allowing identification of effective chemotherapies for patients.

Affordable mobile microfluidic diagnostics: minimum requirements for smartphones and digital imaging for colorimetric and fluorometric anti-dengue and anti-SARS-CoV-2 antibody detection

Background: Miniaturised bioassays permit diagnostic testing near the patient, and the results can be recorded digitally using inexpensive cameras including smartphone and mobile phone cameras. Although digital cameras are now inexpensive and portable, the minimum performance required for microfluidic diagnostic bioassays has not been defined. We present a systematic comparison of a wide range of different digital cameras for capturing and measuring results of microfluidic bioassays and describe a framework to specify performance requirements to quantify immunoassays. Methods: A set of 200 µm diameter microchannels was filled with a range of concentrations of dyes used in colorimetric and fluorometric enzyme immunoassays. These were imaged in parallel using cameras of varying cost and performance ranging from <£30 to >£500. Results: Higher resolution imaging allowed larger numbers of microdevices to be resolved and analysed in a single image. In contrast, low quality cameras were still able to quantify results but for fewer samples. In some cases, an additional macro lens was added to focus closely. If image resolution was sufficient to identify individual microfluidic channels as separate lines, all cameras were able to quantify a similar range of concentrations of both colorimetric and fluorometric dyes. However, the mid-range cameras performed better, with the lowest cost cameras only allowing one or two samples to be quantified per image. Consistent with these findings, we demonstrate that quantitation (to determine endpoint titre) of antibodies against dengue and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viruses is possible using a wide range of digital imaging devices including the mid-range smartphone iPhone 6S and a budget Android smartphone costing <£50. Conclusions: In conclusion, while more expensive and higher quality cameras allow larger numbers of devices to be simultaneously imaged, even the lowest resolution and cheapest cameras were sufficient to record and quantify immunoassay results.

Inline holographic microscopy through fiber imaging bundles

Fiber imaging bundles are widely used as thin, passive image conduits for miniaturized and endoscopic microscopy, particularly for confocal fluorescence imaging. Holographic microscopy through fiber bundles is more challenging; phase conjugation approaches are complex and require extensive calibration. This paper describes how simple inline holographic microscopy can be performed through an imaging bundle using a partially coherent illumination source from a multimode fiber. The sample is imaged in transmission, with the intensity hologram sampled by the bundle and transmitted to a remote camera. The hologram can then be numerically refocused for volumetric imaging, achieving a resolution of approximately 6 µm over a depth range of 1 mm. The scheme does not require any complex prior calibration and hence is insensitive to bending.

Measurement of serum phosphate levels using a mobile sensor

The measurement of serum phosphate concentration is crucial for patients with advanced chronic kidney disease (CKD) and those on maintenance dialysis, as abnormal phosphate levels may be associated with severe health risks. It is important to monitor serum phosphate levels on a regular basis in these patients; however, such measurements are generally limited to every 0.5-3 months, depending on the severity of CKD. This is due to the fact that serum phosphate measurements can only be performed at regular clinic visits, in addition to cost considerations. Here we present a portable and cost-effective point-of-care device capable of measuring serum phosphate levels using a single drop of blood (<60 μl). This is achieved by integrating a paper-based microfluidic platform with a custom-designed smartphone reader. This mobile sensor was tested on patients undergoing dialysis, where whole blood samples were acquired before starting the hemodialysis and during the three-hour treatment. This sampling during the hemodialysis, under patient consent, allowed us to test blood samples with a wide range of phosphate concentrations, and our results showed a strong correlation with the ground truth laboratory tests performed on the same patient samples (Pearson coefficient r = 0.95 and p < 0.001). Our 3D-printed smartphone attachment weighs about 400 g and costs less than 80 USD, whereas the material cost for the disposable test is <3.5 USD (under low volume manufacturing). This low-cost and easy-to-operate system can be used to measure serum phosphate levels at the point-of-care in about 45 min and can potentially be used on a daily basis by patients at home.

Deep Learning-Based Holographic Polarization Microscopy

Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs, or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase-recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an inline lensfree holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including, for example, monosodium urate and triamcinolone acetonide crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.

Quantitative absorption imaging of red blood cells to determine physical and mechanical properties

Red blood cells or erythrocytes, constituting 40 to 45 percent of the total volume of human blood are vesicles filled with hemoglobin with a fluid-like lipid bilayer membrane connected to a 2D spectrin network. The shape, volume, hemoglobin mass, and membrane stiffness of RBCs are important characteristics that influence their ability to circulate through the body and transport oxygen to tissues. In this study, we show that a simple two-LED set up in conjunction with standard microscope imaging can accurately determine the physical and mechanical properties of single RBCs. The Beer–Lambert law and undulatory motion dynamics of the membrane have been used to measure the total volume, hemoglobin mass, membrane tension coefficient, and bending modulus of RBCs. We also show that this method is sensitive enough to distinguish between the mechanical properties of RBCs during morphological changes from a typical discocyte to echinocytes and spherocytes. Measured values of the tension coefficient and bending modulus are 1.27 × 10⁻⁶ J m⁻² and 7.09 × 10⁻²⁰ J for discocytes, 4.80 × 10⁻⁶ J m⁻² and 7.70 × 10⁻²⁰ J for echinocytes, and 9.85 × 10⁻⁶ J m⁻² and 9.69 × 10⁻²⁰ J for spherocytes, respectively. This quantitative light absorption imaging reduces the complexity related to the quantitative imaging of the biophysical and mechanical properties of a single RBC that may lead to enhanced yet simplified point of care devices for analyzing blood cells.

The constant thickness in the microfluidic channel is used for controlled absorption of red and blue light to measure red blood cell hemoglobin and height mapping. High speed recording of the height mapping provides us the membrane fluctuation.

Ultraviolet absorbance detector based on a high output power 235 nm surface mounted device-type light-emitting diode

A new miniaturised capillary flow-through deep-UV absorbance detector has been developed using a microscale surface mounted device- type light-emitting diode (LED) (Crystal IS OPTAN 3535-series), emitting at 235 nm and with a half-height band width of 12 nm, and a high-sensitivity Z-shaped flow-cell. Compared with a previously reported TO-39 ball lens LEDs emitting at 235 nm, the new generation LED produced a 20-fold higher optical output and delivered up to 35 times increase in external quantum efficiency (EQE). The Z-cell was based on a reflective rectangular optical path with cross-sectional dimensions of 100 × 100 µm and a physical optical pathlength of 1.2 mm. Inclusion of UV transparent fused-silica ball lenses, between the SMD and the Z-cell, improved light transmission by a factor of 9 and improved the detector signal-to-noise ratio by a factor of 2.2, at the same input current. The detector was housed within an Al-housing fitted with a cooling fan and demonstrated excellent linearity with stray light down to 0.06%, and an effective pathlength of 1.1 mm (92% of nominal pathlength). The resultant detector was fitted successfully into a briefcase-sized portable capillary HPLC system, and practically demonstrated with the detection of a mixture of 13 test compounds at the sub-mg L^-1 level in <5 min using gradient elution.

Instrumentation-Free Semiquantitative Immunoanalysis Using a Specially Patterned Lateral Flow Assay Device

In traditional colorimetric lateral flow immunoassay (LFI) using gold nanoparticles (AuNPs) as a probe, additional optical transducers are required to quantify the signal intensity of the test line because it presents as a single red-colored line. In order to eliminate external equipment, the LFI signal should be quantifiable by the naked eye without the involvement of optical instruments. Given this objective, the single line test zone of conventional LFI was converted to several spots that formed herringbone patterns. When the sandwich immunoassay was performed on a newly developed semi-quantitative (SQ)-LFI system using AuNPs as an optical probe, the spots were colorized and the number of colored spots increased proportionally with the analyte concentration. By counting the number of colored spots, the analyte concentration can be easily estimated with the naked eye. To demonstrate the applicability of the SQ-LFI system in practical immunoanalysis, microalbumin, which is a diagnostic marker for renal failure, was analyzed using microalbumin-spiked artificial urine samples. Using the SQ-LFI system, the calibration results for artificial urine-based microalbumin were studied, ranging from 0 to 500 μg/mL, covering the required clinical detection range, and the limit of detection (LOD) value was calculated to be 15.5 μg/mL. Thus, the SQ-LFI system provides an avenue for the realization of an efficient quantification diagnostic device in resource-limited conditions.

An aptamer-based magnetic flow cytometer using matched filtering

Facing unprecedented population-ageing, the management of noncommunicable diseases (NCDs) urgently needs a point-of-care (PoC) testing infrastructure. Magnetic flow cytometers are one such solution for rapid cancer cellular detection in a PoC setting. In this work, we report a giant magnetoresistive spin-valve (GMR SV) biosensor array with a multi-stripe sensor geometry and matched filtering to improve detection accuracy without compromising throughput. The carefully designed sensor geometry generates a characteristic signature when cells labeled with magnetic nanoparticles (MNPs) pass by thus enabling multi-parametric measurement like optical flow cytometers (FCMs). Enumeration and multi-parametric information were successfully measured across two decades of throughput (37 - 2730 cells/min). 10-μm polymer microspheres were used as a biomimetic model where MNPs and MNP-decorated polymer conjugates were flown over the GMR SV sensor array and detected with a signal-to-noise ratio (SNR) as low as 2.5 dB due to the processing gain afforded by the matched filtering. The performance was compared against optical observation, exhibiting a 92% detection efficiency. The system achieved a 95% counting accuracy for biomimetic models and 98% for aptamer-based pancreatic cancer cell detection. This system demonstrates the ability to perform reliable flow cytometry toward PoC diagnostics to benefit NCD control plans.

Automated screening of sickle cells using a smartphone-based microscope and deep learning

Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2-0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementary deep neural networks. The first neural network enhances and standardizes the blood smear images captured by the smartphone microscope, spatially and spectrally matching the image quality of a laboratory-grade benchtop microscope. The second network acts on the output of the first image enhancement neural network and is used to perform the semantic segmentation between healthy and sickle cells within a blood smear. These segmented images are then used to rapidly determine the SCD diagnosis per patient. We blindly tested this mobile sickle cell detection method using blood smears from 96 unique patients (including 32 SCD patients) that were imaged by our smartphone microscope, and achieved ~98% accuracy, with an area-under-the-curve of 0.998. With its high accuracy, this mobile and cost-effective method has the potential to be used as a screening tool for SCD and other blood cell disorders in resource-limited settings.

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.

Lensless microscopy platform for single cell and tissue visualization

Today, 3D imaging techniques are emerging, not only as a new tool in early drug discovery but also for the development of potential therapeutics to treat disease. Particular efforts are directed towards in vivo physiology to avoid perturbing the system under study. Here, we assess non-invasive 3D lensless imaging and its impact on cell behavior and analysis. We test our concept on various bio-applications and present here the first results. The microscopy platform based on in-holography provides large fields of view images (several mm² compared to several hundred µm²) with sub-micrometer spatial resolution. 3D image reconstructions are achieved using back propagation functions post-processing.

A novel naphthofluorescein-based probe for ultrasensitive point-of-care testing of zinc(II) ions and its bioimaging in living cells and zebrafishes

The combination of fluorescence method with paper-based diagnostic device is quite suitable in point-of-care testing (POCT). Herein, we designed a novel hybrid fluorescein-based probe ZN-2 and investigated its fluorescent properties thoroughly in the detection of Zn²⁺. In comparison with the fluorescein-based probe ZN-1, ZN-2 displayed better sensitivity, long-wavelength and faster response to Zn²⁺ within 20 min. Interestingly, we could achieve ultrasensitive, high-throughput and visual detection in the POCT analysis of Zn²⁺ by anchoring this probe ZN-2 on the paper-based device. This device with satisfied performance for Zn²⁺ detection was achieved in real samples including cytochylema, serum and lake water. Finally, the probe ZN-2 was further applied to visualize and monitor the level changes of Zn²⁺ in the living cells and zebrafishes.