Use of a platform with lens-free shadow imaging technology to monitor natural killer cell activity.

Natural killer (NK) cells are a crucial component of the innate immune system. This study introduces Cellytics NK, a novel platform for rapid and precise measurement of NK cell activity. This platform combines an NK-specific activation stimulator cocktail (ASC) and lens-free shadow imaging technology (LSIT), using optoelectronic components. LSIT captures digital hologram images of resting and ASC-activated NK cells, while an algorithm evaluates cell size and cytoplasmic complexity using shadow parameters. The combined shadow parameter derived from the peak-to-peak distance and width standard deviation rapidly distinguishes active NK cells from inactive NK cells at the single-cell level within 30 s. Here, the feasibility of the system was demonstrated by assessing NK cells from healthy donors and immunocompromised cancer patients, demonstrating a significant difference in the innate immunity index (I3). Cancer patients showed a lower I3 value (161%) than healthy donors (326%). I3 was strongly correlated with NK cell activity measured using various markers such as interferon-gamma, tumor necrosis factor-alpha, perforin, granzyme B, and CD107a. This technology holds promise for advancing immune functional assays, offering rapid and accurate on-site analysis of NK cells, a crucial innate immune cell, with its compact and cost-effective optoelectronic setup, especially in the post-COVID-19 era.

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology.

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland-Altman analysis confirmed the model's reliability, with a mean bias of -2.29 and 95% limits of agreement between 18.49 and -23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise.

Facile microfluidic synthesis of monodispersed size-controllable quantum dot (QD) microbeads using custom developed QD photoresist.

Fluorescent microbeads (MBs) are widely used as next-generation biosensors for the detection of target chemicals at highly sensitive concentrations, and for imaging and tracking in vitro and in vivo. However, most known methods for producing fluorescent MBs require complicated multistep processes that result in low production rates. In this study, we report a method for fabricating micrometer-sized quantum dot microbeads (QD-MBs) using a microfluidic chip and specially designed QD photoresist (QD-PR). This on-demand lab-on-a-chip method yielded monodispersed QD-MBs ranging from 1.89 to 33 µm with a coefficient of variation of less than 10%. The size distribution of the fabricated QD-MBs was Gaussian with a peak around the mean diameter and a spread of sizes around the peak. Compared with nanoscale QDs, the fabricated QD-MBs showed no emission loss. The full-width at half-maximum of the emission peak of the QD-MBs was smaller than that of the colloidal QDs, indicating a more uniform distribution and a higher density of QDs within the MB structure. In addition, we investigated the microfluidic flow regime that yielded the most uniform and controllable QD-MB. The MBs in the dripping regime were spherical and monodisperse, with an excellent particle size distribution. In this study, we present a simple and effective strategy for producing QD-MBs with controllable sizes, which can be crucial in diverse fields such as biosensing, drug delivery, and imaging.

Colloidal Quantum Dot Nanolithography: Direct Patterning via Electron Beam Lithography.

Micro/nano patterns based on quantum dots (QDs) are of great interest for applications ranging from electronics to photonics to sensing devices for biomedical purposes. Several patterning methods have been developed, but all lack the precision and reproducibility required to fabricate precise, complex patterns of less than one micrometer in size, or require specialized crosslinking ligands, limiting their application. In this study, we present a novel approach to directly pattern QD nanopatterns by electron beam lithography using commercially available colloidal QDs without additional modifications. We have successfully generated reliable dot and line QD patterns with dimensions as small as 140 nm. In addition, we have shown that using a 10 nm SiO2 spacer layer on a 50 nm Au layer substrate can double the fluorescence intensity compared to QDs on the Au layer without SiO2. This method takes advantage of traditional nanolithography without the need for a resist layer.

Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays.

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, p = 0.008; Pearson correlation coefficient: 0.56, p = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

Plasmonic Sensors: A New Frontier in Nanotechnology.

Plasmonics is the study of surface plasmons formed by the interaction of incident light with electrons to form a surface-bound electromagnetic wave [...].

Field-portable seawater toxicity monitoring platform using lens-free shadow imaging technology.

The accidental spill of hazardous and noxious substances (HNSs) in the ocean has serious environmental and human health consequences. Assessing the ecotoxicity of seawater exposed to various HNS is challenging due to the constant development of new HNS or mixtures, and assessment methods are also limited. Microalgae viability tests are often used among the various biological indicators for ecotoxicity testing, as they are the primary producers in aquatic ecosystems. However, since the conventional cell growth rate test measures cell viability over three to four days using manual inspection under a conventional optical microscope, it is labor- and time-intensive and prone to subjective errors. In this study, we propose a rapid and automated method to evaluate seawater ecotoxicity by quantification of the morphological changes of microalgae exposed to more than 30 HNSs. This method was further validated using conventional growth rate test results. Dunaliella tertiolecta, a microalgae species without rigid cell walls, was selected as the test organism. Its morphological changes in response to HNS exposure were measured at the single cell level using a custom-developed device that uses lens-free shadow imaging technology. The ecotoxicity evaluation induced by the morphological change could be available in as little as 5 min using the proposed method and device, and it could be effective for 20 HNSs out of 30 HNSs tested. Moreover, the test results of six selected HNSs with high marine transport volume and toxicity revealed that the sensitivity of the proposed method extends to half the maximum effective concentration (EC50) and even to the lowest observed effective concentration (LOEC). Furthermore, the average correlation index between the growth inhibition test (three to four days) and the proposed morphology changes test (5 min) for the six selected HNSs was 0.84, indicating great promise in the field of various point-of-care water quality monitoring. Thus, the proposed equipment and technology may provide a viable alternative to traditional on-site toxicity testing, and the potential of rapid morphological analysis may replace traditional growth inhibition testing.

Quantum dot photolithography using a quantum dot photoresist composed of an organic-inorganic hybrid coating layer.

Quantum dots (QDs) have emerged as an important class of materials for diverse applications such as solid-state lighting, energy conversion, displays, biomedicine, and plasmonics due to their excellent photonic properties and durability. Soft lithography, inkjet printing, nanoimprinting, and polymer deep-pen lithography are primary lithography techniques employed to implement micro-patterns with QDs, however, there are limited reports on QD photolithography using conventional photolithography processes suitable for mass production. This study reports a QD photolithography technique using a custom-developed QD photoresist made of an organic-inorganic hybrid coating layer. Using this QD photoresist, various QD micro-patterns, including red or green micro lines, RGB color filters for smartphone displays at 340 ppi, and atypical micro logo patterns of the Korea University, were successfully fabricated. Furthermore, various process parameters were developed for the QD photolithography with this custom QD photoresist, and the optical properties of the QD films were also investigated. To demonstrate its applicability in contemporary smartphone displays, the color coordinates of the QD films were compared to those of the BT.2020 standard. The chromaticity of the QD photoresist in CIE 1931 color space covered 98.7% of the NTSC (1987) area while providing more expansive color space. Overall, the QD photoresist and its photolithography techniques reported in this study hold great promise in various fields of QD-based applications, including bio-labeling, optical detectors, and solar cells.

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

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

A CMOS Image Sensor Based Refractometer without Spectrometry.

The refractive index (RI), an important optical property of a material, is measured by commercial refractometers in the food, agricultural, chemical, and manufacturing industries. Most of these refractometers must be equipped with a prism for light dispersion, which drastically limits the design and size of the refractometer. Recently, there have been several reports on the development of a surface plasmon resonance (SPR)-based RI detector, which is characterized by its high sensitivity and simplicity. However, regardless of the prism, an expensive spectrometer is required to analyze the resonance wavelength or angle of incidence. This paper proposes a method that eliminates the need for the prism and other conventional spectrometer components. For this purpose, total internal reflection SPR technology was used on an Ag thin film, and RI analysis was combined with a lens-free CMOS image sensor or a smartphone camera. A finite-difference time-domain (FDTD) numerical simulation was performed to evaluate the relationship between the output power intensity and Ag film thickness for different RIs at three wavelengths of commercial light-emitting diodes (LEDs). The maximum sensitivity of -824.54 RIU-1 was achieved with AG20 at an incident wavelength of 559 nm. Due to its simple design and cost effectiveness, this prism-less, SPR-based refractometer combined with a lens-free CMOS image sensor or a smartphone could be a superior candidate for a point-of-care device that can determine the RIs of various analytes in the field of biological or chemical sensing.

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique.

The complete blood count (CBC) is one of the most important clinical steps in clinical diagnosis. The instruments used for CBC are usually expensive and bulky and require well-trained operators. Therefore, it is difficult for medical institutions below the tertiary level to provide these instruments, especially in underprivileged countries. Several reported on-chip blood cell tests are still in their infancy and do not deviate from conventional microscopic or impedance measurement methods. In this study, we (i) combined magnetically activated cell sorting and the differential density method to develop a method to selectively isolate three types of leukocytes from blood and obtain samples with high purity and concentration for portable leukocyte classification using the lens-free shadow imaging technique (LSIT), and (ii) established several shadow parameters to identify the type of leukocytes in a complete leukocyte shadow image by shadow image analysis. The purity of the separated leukocytes was confirmed by flow cytometry. Several shadow parameters such as the "order ratio" and "minimum ratio" were developed to classify the three types of leukocytes. A shadow image library corresponding to each type of leukocyte was created from the tested samples. Compared with clinical reference data, a correlation index of 0.98 was obtained with an average error of 6% and a confidence level of 95%. This technique offers great potential for biological, pharmaceutical, environmental, and clinical applications, especially where point-of-care detection of rare cells is required.

Detection of Particulate Matters with a Field-Portable Microscope Using Side-Illuminated Total Internal Reflection.

Field-portable observation and analysis of particulate matter (PM) is required to enhance healthy lives. Various types of the PM measurement methods are in use; however, each of these methods has significant limitations in that real time measurement is impossible, the detection system is bulky, or the measurement accuracy is insufficient. In this work, we introduce an optical method to perform a fast and accurate PM analysis with a higher-contrast microscopic image enabled by a side-illuminated total internal reflection (TIR) technique to be implemented in a compact device. The superiority of the proposed method was quantitatively demonstrated by comparing the signal-to-noise ratio of the proposed side-illuminated TIR method with a traditional halogen lamp-based transmission microscope. With the proposed device, signal-to-noise ratios (SNRs) for microbeads (5~20 µm) and ultrafine dust particles (>5 µm) increased 4.5~17 and 4~10 dB, respectively, compared to the conventional transmission microscope. As a proof of concept, the proposed method was also applied to a low-cost commercial smartphone toy microscope enabling field-portable detection of PMs. We believe that the proposed side-illuminated TIR PM detection device holds significant advantages over other commonly used systems due to its sufficient detection capability along with simple and compact configuration as well as low cost.

A Field-Portable Cell Analyzer without a Microscope and Reagents.

This paper demonstrates a commercial-level field-portable lens-free cell analyzer called the NaviCell (No-stain and Automated Versatile Innovative cell analyzer) capable of automatically analyzing cell count and viability without employing an optical microscope and reagents. Based on the lens-free shadow imaging technique, the NaviCell (162 × 135 × 138 mm³ and 1.02 kg) has the advantage of providing analysis results with improved standard deviation between measurement results, owing to its large field of view. Importantly, the cell counting and viability testing can be analyzed without the use of any reagent, thereby simplifying the measurement procedure and reducing potential errors during sample preparation. In this study, the performance of the NaviCell for cell counting and viability testing was demonstrated using 13 and six cell lines, respectively. Based on the results of the hemocytometer (de facto standard), the error rate (ER) and coefficient of variation (CV) of the NaviCell are approximately 3.27 and 2.16 times better than the commercial cell counter, respectively. The cell viability testing of the NaviCell also showed an ER and CV performance improvement of 5.09 and 1.8 times, respectively, demonstrating sufficient potential in the field of cell analysis.

Flexible heartbeat sensor for wearable device.

We demonstrate a flexible strain-gauge sensor and its use in a wearable application for heart rate detection. This polymer-based strain-gauge sensor was fabricated using a double-sided fabrication method with polymer and metal, i.e., polyimide and nickel-chrome. The fabrication process for this strain-gauge sensor is compatible with the conventional flexible printed circuit board (FPCB) processes facilitating its commercialization. The fabricated sensor showed a linear relation for an applied normal force of more than 930 kPa, with a minimum detectable force of 6.25Pa. This sensor can also linearly detect a bending radius from 5mm to 100mm. It is a thin, flexible, compact, and inexpensive (for mass production) heart rate detection sensor that is highly sensitive compared to the established optical photoplethysmography (PPG) sensors. It can detect not only the timing of heart pulsation, but also the amplitude or shape of the pulse signal. The proposed strain-gauge sensor can be applicable to various applications for smart devices requiring heartbeat detection.

A review of recent progress in lens-free imaging and sensing.

Recently, lens-free imaging has evolved as an alternative imaging technology. The key advantages of this technology, including simplicity, compactness, low cost, and flexibility of integration with other components, have facilitated the realization of many innovative applications, especially, in the fields of the on-chip lens-free imaging and sensing. In this review, we discuss the development of lens-free imaging, from theory to applications. This article includes the working principle of lens-free digital inline holography (DIH) with coherent and semi coherent light, on-chip lens-free fluorescence imaging and sensing, lens-free on-chip tomography, lens-free on-chip gigapixel nanoscopy, detection of nanoparticles using on-chip microscopy, wide field microscopy, and lens-free shadow image based point-of-care systems. Additionally, this review also discusses the lens-free fluorescent imaging and its dependence on structure and optical design, the advantage of using the compact lens-free driven equilibrium Fourier transform (DEFT) resolved imaging technique for on-chip tomography, the pixel super-resolved algorithm for gigapixel imaging, and the lens-free technology for point-of-care applications. All these low-cost, compact, and fast-processing lens-free imaging and sensing techniques may play a crucial role especially in the fields of environmental, pharmaceutical, biological, and clinical applications of the resource-limited settings.

A Human Serum-Based Enzyme-Free Continuous Glucose Monitoring Technique Using a Needle-Type Bio-Layer Interference Sensor.

The incidence of diabetes is continually increasing, and by 2030, it is expected to have increased by 69% and 20% in underdeveloped and developed countries, respectively. Therefore, glucose sensors are likely to remain in high demand in medical device markets. For the current study, we developed a needle-type bio-layer interference (BLI) sensor that can continuously monitor glucose levels. Using dialysis procedures, we were able to obtain hypoglycemic samples from commercial human serum. These dialysis-derived samples, alongside samples of normal human serum were used to evaluate the utility of the sensor for the detection of the clinical interest range of glucose concentrations (70-200 mg/dL), revealing high system performance for a wide glycemic state range (45-500 mg/dL). Reversibility and reproducibility were also tested over a range of time spans. Combined with existing BLI system technology, this sensor holds great promise for use as a wearable online continuous glucose monitoring system for patients in a hospital setting.

Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology.

Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings.

Low-cost telemedicine device performing cell and particle size measurement based on lens-free shadow imaging technology.

Recent advances in lens-free shadow imaging technology have enabled a new class of cell imaging platform, which is a suitable candidate for point-of-care facilities. In this paper, we firstly demonstrate a compact and low-cost telemedicine device providing automated cell and particle size measurement based on lens-free shadow imaging technology. Using the generated shadow (or diffraction) patterns, the proposed approach can detect and measure the sizes of more than several hundreds of micro-objects simultaneously within a single digital image frame. In practical experiments, we defined four types of shadow parameters extracted from each micro-object shadow pattern, and found that a specific shadow parameter (peak-to-peak distance, PPD) demonstrated a linear relationship with the actual micro-object sizes. By using this information, a new algorithm suitable for operation on both a personal computer (PC) and a cell phone was also developed, providing automated size detection of poly-styrenemicro-beads and biological cells such as red blood cells, MCF-7, HepG2, and HeLa. Results from the proposed device were compared with those of a conventional optical microscope, demonstrating good agreement between two approaches. In contrast to other existing cell and particle size measurement approaches, such as Coulter counter, flow-cytometer, particle-size analyzer, and optical microscope, this device can provide accurate cell and particle size information with a 2 µm maximum resolution, at almost no cost (less than 100 USD), within a compact instrumentation size (9.3×9.0×9.0 cm(3)), and in a rapid manner (within 1 min). The proposed lens-free automated particle and cell size measurement device, based on shadow imaging technology, can be utilized as a powerful tool for many cell and particle handling procedures, including environmental, pharmaceutical, biological, and clinical applications.

A simple and low-cost biofilm quantification method using LED and CMOS image sensor.

A novel biofilm detection platform, which consists of a cost-effective red, green, and blue light-emitting diode (RGB LED) as a light source and a lens-free CMOS image sensor as a detector, is designed. This system can measure the diffraction patterns of cells from their shadow images, and gather light absorbance information according to the concentration of biofilms through a simple image processing procedure. Compared to a bulky and expensive commercial spectrophotometer, this platform can provide accurate and reproducible biofilm concentration detection and is simple, compact, and inexpensive. Biofilms originating from various bacterial strains, including Pseudomonas aeruginosa (P. aeruginosa), were tested to demonstrate the efficacy of this new biofilm detection approach. The results were compared with the results obtained from a commercial spectrophotometer. To utilize a cost-effective light source (i.e., an LED) for biofilm detection, the illumination conditions were optimized. For accurate and reproducible biofilm detection, a simple, custom-coded image processing algorithm was developed and applied to a five-megapixel CMOS image sensor, which is a cost-effective detector. The concentration of biofilms formed by P. aeruginosa was detected and quantified by varying the indole concentration, and the results were compared with the results obtained from a commercial spectrophotometer. The correlation value of the results from those two systems was 0.981 (N = 9, P < 0.01) and the coefficients of variation (CVs) were approximately threefold lower at the CMOS image-sensor platform.

Chemiluminometric immuno-analysis of innate immune response against repetitive bacterial stimulations for the same mammalian cells.

For monitoring of human cellular response to repetitive bacterial stimulations (e.g., Pseudomonas aeruginosa in a lysate form), we devised a chemiluminescent immuno-analytical system for toll-like receptor 1 (TLR1) as marker present on cell surfaces (e.g., A549). Upon stimulation, TLR1 recognizes pathogen-associated molecular patterns of the infectious agent and are then up-regulated via activation of the nuclear factor-κB (NF-κB) pathway. In this study, the receptor density was quantified by employing an antibody specific to the target receptor and by producing a chemiluminometric signal from an enzyme labeled to the binder. The activated status was then switched back to normal down-regulated stage, by changing the culture medium to one containing animal serum. The major factors affecting activation were the stimulation dose of the bacterial lysate, stimulation timing during starvation, and up- and down-regulation time intervals. Reiterative TLR regulation switching up to three times was not affected by either antibody remained after immunoassay or enzyme substrate (e.g., hydrogen peroxide) in solution. This immuno-analysis for TLRs could be unique to acquire accumulated response of the human cells to repeated stimulations and, therefore, can eventually apply to persistency testing of the cellular regulation in screening of anti-inflammatory substances.