Fluorescence microscopes are widely used to detect fluore-scent objects in a biological sample. However, traditional benchtop fluorescence microscopes are limited to high cost and nonportability. Recently, smartphone-based fluorescence microscopes (SFMs) have been developed, which capture images using a smartphone camera with the help of an external optical device and serve as an inexpensive and portable alternative. However, to analyze the images and get the accurate count of bioparticles, external software and computational resources are required; moreover, the lack of single and clustered count capabilities in SFMs without relying on an image library limits the use of SFMs. Here, we present an automated method as a mobile application with the ability to provide the bioparticle counts at the point of care by imaging human leukocytes and fluorescent microparticles. The application employs efficient image processing techniques that result in faster automated counting and reduced computational cost while providing comparable counts to industrial and academic standards. Compared with counting results calculated by ImageJ, a correlation coefficient of R-2 > 0.99 was observed along with the average relative error of similar to 0.5% on different kinds of fluorescent images.

Chronic myelogenous/myeloid leukemia (CML) is a type of cancer of bone marrow that arises from hematopoietic stem cells and affects millions of people worldwide. Eighty-five percent of the CML cases are diagnosed during chronic phase, most of which are detected through routine tests. Leukocytes, micro-Ribonucleic Acids, and myeloid markers are the primary biomarkers for CML diagnosis and are mainly detected using real-time reverse transcription polymerase chain reaction, flow cytometry, and genetic testing. Though multiple therapies have been developed to treat CML, early detection still plays a pivotal role in the overall patient survival rate. The current technologies used for CML diagnosis are costly and are confined to laboratory settings which impede their application in the point-of-care settings for early-stage detection of CML. This study provides detailed analysis and insights into the significance of CML, patient symptoms, biomarkers used for testing, and best possible detection techniques responsible for the enhancement in survival rates. A critical and detailed review is provided around potential microfluidic devices that can be adapted to detect the biomarkers associated with CML while enabling point-of-care testing for early diagnosis of CML to improve patient survival rates.

Fluorescence microscopes are commonly used to detect micro and nano biological entities (blood cells, microbes, functionalized particles etc.). Several experimental assays have been developed for biomarker measurements using this microscopy instrument. Common examples include cell viability analysis, surface receptor based specific blood cell enumeration, phagocytosis determination etc. However, the laboratory-based bench top fluorescence microscope suffers from high capital cost, maintenance, experienced staff for its operation and lack of portability. To address several of these challenges, here we have developed a hand-held, portable, 3D printed fluorescence microscope to enable single cell enumeration and nanoparticle imaging operated by smartphone. 3D printed platform is composed of top and bottom portions. The bottom portion includes excitation optics including an LED and excitation filter. While the top portion houses an emission filter, and a lens, while a smartphone can be placed on top portion for imaging. The excitation and emission filters can be swapped out for different fluorophores used. We have used the platform for imaging leukocytes collected from the whole blood. Blood samples were procured from Robert Wood Johnson Medical Hospital. We ran the leukocytes on our photonics platform and laboratory florescence microscope concurrently for a comparative analysis and found a high correlation of R2 = 0.99 in between cell counting results. Further, swapping different lenses can achieve high resolution for the imaging system. The resolution was verified by US Airforce Resolution Chart. This allowed us to image fluorescent nanoparticles up to 800nm at various concentrations and excitation modalities. This platform can be used for single-cell analysis, specific leukocytes enumeration and nanoparticles imaging. The demonstrated results can enable significant applications to develop many biomedical and diagnostic assays.

Micro-nanoparticle and leukocyte imaging find significant applications in the areas of infectious disease diagnostics, cellular therapeutics, and biomanufacturing. Portable fluorescence microscopes have been developed for these measurements, however, quantitative assessment of the quality of images (micro-nanoparticles, and leukocytes) captured using these devices remains a challenge. Here, we present a novel method for automated quality assessment of fluorescent images (AQAFI) captured using smartphone fluorescence microscopes (SFM). AQAFI utilizes novel feature extraction methods to identify and measure multiple features of interest in leukocyte and micro-nanoparticle images. For validation of AQAFI, fluorescent particles of different diameters (8.3, 2, 1, 0.8 μm) were imaged using custom-designed SFM at a range of excitation voltages (3.8-4.5 V). Particle intensity, particle vicinity intensity, and image background noise were chosen as analytical parameters of interest and measured by the AQAFI algorithm. A control method was developed by manual calculation of these parameters using ImageJ which was subsequently used to validate the performance of the AQAFI method. For micro-nanoparticle images, correlation coefficients with R2 > 0.95 were obtained for each parameter of interest while comparing AQAFI vs. control (ImageJ). Subsequently, key performance indicators (KPIs) i.e., signal difference to noise ratio (SDNR) and contrast to noise ratio (CNR) were defined and calculated for these micro-nano particle images using both AQAFI and control methods. Finally, we tested the performance of the AQAFI method on the fluorescent images of human peripheral blood leukocytes captured using our custom SFM. Correlation coefficients of R2 = 0.99 were obtained for each parameter of interest (leukocyte intensity, vicinity intensity, background noise) calculated using AQAFI and control (ImageJ). A high correlation was also found between the CNR and SDNR values calculated using both methods. The developed AQAFI method thus presents an automated and precise way to quantify and assess the quality of fluorescent images (micro-nano particles and leukocytes) captured using portable SFMs. Similarly, this study finds broader applicability and can also be employed with benchtop microscopes for the quantitative assessment of their imaging performance.

Patients suffering with bacterial infections are commonly prescribed antibiotic drugs for a certain period until their treatment is complete. Utilizing patient’s bio fluid samples (e.g., blood or urine), we can quantify the antibiotic levels to understand its effective absorption. Laboratory spectrophotometer is normally used as a gold standard instrument to perform such analysis by measuring light absorption through a sample solution in real-time. Here, we have developed a 3D printed Point-of-Care (POC) device that can be used to detect the concentration of proteins in biological samples. We quantified Bovine Serum Albumin (BSA) concentration using a Bradford protein assay to test the 3D printed device efficacy against an instrument spectrophotometer and obtained a correlation co-efficient of R 2 = 0.989 between the two methods. Our results show the potential of our 3D printed spectrophotometer to quantify other proteins and specific antibiotics (e.g., Rifampin for Tuberculosis management) to monitor and manage patients' treatment.

Biological cell quantification is an important step in diagnosing and strategizing treatment for many infections, cardiovascular diseases, and biomarker discovery which in turn helps in understanding immunological and genetic disorders, cancers, etc. A point-of-care diagnostic device integrated with microfluidic systems can benefit such applications by accelerating the diagnosis procedures and making it accessible throughout the world. Here, we present a computer vision methodology to aid particle and cell counting from images acquired by the novel smartphone based microfluidic biosensor. We implement a convolutional neural network architecture to train, validate and test it with different experimental datasets. This method proved to obtain results faster and analogous to that of the benchmark techniques.

Smartphone fluorescent microscopes (SFM) offer many functional characteristics similar to their benchtop counterparts at a fraction of the cost and have been shown to work for biomarker detection in many biomedical applications. However, imaging and quantification of bioparticles in the sub-micron and nanometer range remains challenging as it requires aggressive robustness and high-performance metrics of the building blocks of SFM. Here, we explored multiple excitation modalities and their performance on the imaging capability of an SFM. Employing spatial positional variations of the excitation source with respect to the imaging sample plane (i.e., parallel, perpendicular, oblique), we developed three distinct SFM variants. These SFM variants were tested using green-fluorescent beads of four different sizes (8.3, 2, 1, 0.8 μm). Optimal excitation voltage range was determined by imaging these beads at multiple excitation voltages to optimize for no data loss and acceptable noise levels for each SFM variant. The SFM with parallel excitation was able to only image 8.3 μm beads while the SFM variants with perpendicular and oblique excitation were able to image all four bead sizes. Relative performance of the SFM variants was quantified by calculating signal difference to noise ratio (SDNR) and contrast to noise ratio (CNR) from the captured images. SFM with oblique excitation generated the highest SDNR and CNR values, whereas, for power consumption, SFM with perpendicular excitation generated the best results. This study sheds light on significant findings related to performance of SFM systems and their potential utility in biomedical applications involving sub-micron imaging. Similarly, findings of this study are translatable to benchtop microscopy instruments as well as to enhance their imaging performance metrics.

Proteins are useful biomarkers for a wide range of applications such as cancer detection, discovery of vaccines, and determining exposure to viruses and pathogens. Here, we present a low-noise front-end analog circuit interface towards development of a portable readout system for the label-free sensing of proteins using Nanowell array impedance sensing with a form factor of approximately 35cm2. The electronic interface consists of a low-noise lock-in amplifier enabling reliable detection of changes in impedance as low as 0.1% and thus detection of proteins down to the picoMolar level. The sensitivity of our system is comparable to that of a commercial bench-top impedance spectroscope when using the same sensors. The aim of this work is to demonstrate the potential of using impedance sensing as a portable, low-cost, and reliable method of detecting proteins, thus inching us closer to a Point-of-Care (POC) personalized health monitoring system. We have demonstrated the utility of our system to detect antibodies at various concentrations and protein (45 pM IL-6) in PBS, however, our system has the capability to be used for assaying various biomarkers including proteins, cytokines, virus molecules and antibodies in a portable setting.

Leukocyte quantification from whole blood can aid in detecting and managing infections, cardiovascular diseases, and immune system responses. In addition to traditional leukocyte quantification devices such as flow cytometers and benchtop fluorescent microscopes, smartphone-based particle quantifiers are becoming popular because they provide results at a fraction of the cost. One major limitation of these smartphone-based devices is their dependence on desktop computers for data processing, which keeps them from reaching their true translation potential as point of care (POC) devices. In this paper, we present a computer vision and machine learning-enabled methodology to count particles imaged from our 3D printed smartphone-coupled fluorescent microscope. Multiple convolution neural networks (CNN) using different filter sizes were implemented and trained with various learning rates (0.001, 0.0001) and batch sizes (8,16,32). The performance of these trained networks was then tested on green fluorescent microparticles and leukocytes and compared against the ground truth obtained using ImageJ. An R2 value of 0.99 was observed. Next, when cross-validation was done to validate the efficacy of the designed CNN architecture, and the predicted results showed a good correlation (R2 = 0.99) when compared against the ground truth. The performance of the trained model was also evaluated on particles conjugated with a different fluorophore and an R2 value of 0.99 was observed, showcasing its efficacy and versatility. This trained model was then integrated into an Application Programming Interface (API) and is available online for the broader community usage.

COVID-19 has been declared a global pandemic which has brought the world economy and the society to a standstill. The current emphasis of testing is on detection of genetic material of SARS-CoV-2. Such tests are useful for assessing the current state of a subject: Infected or not infected. In addition to such tests, antibody testing is necessary to stratify the population into three groups: never exposed, infected, and immune. Such a stratification is necessary for safely reopening the society and remobilizing the economy. The aim of this review article is to inform the audience of the current diagnostic and surveillance technologies that are being employed for the detection of SARS-CoV-2 antibodies along with their shortcomings, and to highlight microfluidic sensors and devices that show promise of being commercialized for detection and quantification of SARS-CoV-2 antibodies in low-resource and Point-of-Care (POC) settings.

Tuberculosis (TB) is a highly infectious disease and remains as one the leading causes of mortality around the globe. Many drugs, such as rifampin, isoniazid, ethambutol, and pyrazinamide, are routinely prescribed for its treatment. The dosage requirements for treatment depend on a patients age, gender, and preexisting conditions, if any. Using therapeutic drug monitoring (TDM), clinicians can observe drug absorbance in a patient and adjust the drug dosage accordingly. Here, we have developed a 3-D printed hand-held portable spectrophotometer, which can be used for quantifying antibiotics in biological samples (e.g., urine) for TDM. Using the portable spectrophotometer, we quantified different amounts of rifampin in phosphate buffer saline and synthetic urine and found R-2 values of 0.92 and 0.94, respectively, compared to the standard clinical instrument. The presented system can potentially aide clinicians in making more informed decisions and serves as a first step toward the development of a fully automated point-of-care (POC) system for TDM in TB patients undergoing therapy.

The ability to kill infecting microbes is an essential facet of our immune response to an infection. However, phagocytic ability is often overlooked as a part of immunological profile in infected patients' diagnosis, as the understanding of phagocytic capabilities in disease states is incomplete. In this work, we have evaluated for the first time the relationship between blood lactate level and the neutrophil phagocytic activity at a single-cell level. Blood samples (N = 19) were grouped on the basis of their blood lactate levels i.e., below (control) or above 2 mmol L-1 (high-risk) (i.e., 2 mmol L-1 is a common clinical lactate threshold used for patients' triage). Neutrophils were isolated from whole blood and then incubated with fluorescent IgG coated beads for 40 minutes, and the ability of each neutrophil to internalize beads was quantified. Single-cell phagocytic activity analysis has shown interesting findings such as: (i) a single neutrophil was able to internalize up to 7 beads, (ii) for a control group, 39.76% cells didn't internalize any beads, while for a high-risk group, 30.65% cells didn't show any phagocytic activity, (iii) similarly, 30.46% cells internalize only 1 bead in a control group, while for a high-risk group the activity is slightly higher with only 31.73% cells showing single bead internalization, and (iv) 7 bead internalization activity was much higher for samples in a high-risk group (0.6% cells) compared to a control group (0.17% cells). We used multiple statistical tests to compare these differences. For a two-tailed T-test, we used the mean phagocytic activity of the cells (i.e., the average number of beads internalized by cells) isolated from the blood samples in the two groups (1.14 vs. 1.35) and found the p-value to be 0.08. We also used principal component analysis (PCA) on this high dimensional phagocytic activity distribution data and performed dimension reduction. However, the first 3 principal components didn't show a clear distinction between groups. Next, we developed machine learning models using artificial neural networks (ANNs) to differentiate between the distribution of phagocytic activity in neutrophil populations of the two groups. Our models yielded area under curve (AUC) values below 0.7 for receiver operator characteristic curves. Although our study highlighted interesting phagocytic activity findings at a single cell level, it further highlights the need for integration of an individual patient's medical record to get more personalized insights into individual phagocytic activity in the future.

Silver nanoparticles (AgNPs) exhibit strong antimicrobial properties against many pathogens. Traditionally employed chemical methods for AgNPs synthesis are toxic for the environment. Here, we report a quicker, simpler, and environmentally benign process to synthesize AgNPs by using an aqueous ‘root extract’ of Salvadora persica (Sp) plant as a reducing agent. The synthesized Salvadora persica nano particles (SpNPs) showed significantly higher antimicrobial efficacy compared to earlier reported studies. We characterized SpNPs using UV–Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Transmission Electron Microscopy (TEM), Field Emission Scanning Electron Microscopy (FE-SEM), Dynamic Light Scattering (DLS) and X-ray powder diffraction (P-XRD). UV–Vis spectrum showed the highest absorbance at 420 nm. FTIR analysis depicts presence of bond stretching including OH– (3300 cm −1 ), C=N– (2100 cm −1 ) and NH– (1630 cm −1 ) which are attributed in the involvement of phenolics, proteins or nitrogenous compounds in reduction and stabilization of AgNPs. TEM, FE-SEM and DLS analysis revealed the spherical and rod nature of SpNPs and an average size of particles as 37.5 nm. XRD analysis showed the presence of the cubic structure of Ag which confirmed the synthesis of silver nanoparticles. To demonstrate antimicrobial efficacy, we evaluated SpNPs antimicrobial activity against two bacterial pathogens ( Escherichia coli (ATCC 11229) and Staphylococcus epidermidis (ATCC 12228)). SpNPs showed a significantly high inhibition for both pathogens and minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were found to be 0.39 µg/mL and 0.78 µg/mL for E. coli while 0.19 µg/mL and 0.39 µg/mL for S. epidermidis respectively. Further, Syto 16 staining of bacterial cells provided a supplemental confirmation of the antimicrobial efficacy as the bacterial cells treated with SpNPs stop to fluoresce compared to the untreated bacterial cells. Our highly potent SpNPs will likely have a great potential for many antimicrobial applications including wound healing, water purification, air filtering and other biomedical applications.

Portable smartphone-based fluorescent microscopes are becoming popular owing to their ability to provide major functionalities offered by regular benchtop microscopes at a fraction of the cost. However, smartphone-based microscopes are still limited to a single fluorophore, fixed magnification, the inability to work with a different smartphones, and limited usability to either glass slides or cover slips. To overcome these challenges, here we present a modular smartphone-based microscopic attachment. The modular design allows the user to easily swap between different sets of filters and lenses, thereby enabling utility of multiple fluorophores and magnification levels. Our microscopic smartphone attachment can also be used with different smartphones and was tested with Nokia Lumia 1020, Samsung Galaxy S9+, and an iPhone XS. Further, we showed imaging results of samples on glass slides, cover slips, and microfluidic devices. A 1951 USAF resolution test target was used to quantify the maximum resolution of the microscope which was found to be 3.9 μm. The performance of the smartphone-based microscope was compared with a benchtop microscope and we found an R2 value of 0.99 using polystyrene beads and blood cells isolated from human blood samples collected from Robert Wood Johnson Medical Hospital. Additionally, to count the particles (cells and beads) imaged from the smartphone-based fluorescent microscope, we developed artificial neural networks (ANNs) using multiple training algorithms, and evaluated their performances compared to the control (ImageJ). Finally, we did ANOVA and Tukey's post-hoc analysis and found a p-value of 0.97 which shows that no statistical significant difference exists between the performance of the trained ANN and control (ImageJ).

The architecture and working of a smartphone-based biosensor for the quantification of leukocytes at point of care is presented in this paper. The biosensor consists of a microscopic smartphone attachment with a resolution of 6.2 μm and a disposable microfluidic biochip for capturing leukocytes. Polymorphonuclear leukocytes (PMNL) were isolated from whole blood before being seeded into PBS solution to mimic the biological samples from patients suffering from various diseases. To capture all the leukocytes, antihuman CD45 antibody was immobilized in the capture chamber of microfluidic biochip for one hour for adsorption. Leukocyte spiked PBS sample was then flowed through the microfluidic biochip at 10 μl/min for capturing leukocytes. 50 μl of a green nuclear stain was then flowed through the biochip for fluorescent imaging. Leukocyte capture was verified by imaging the biochip in the smartphone setup. ImageJ was then used for detection and quantification of leukocytes from the captured images. The obtained results showcase the feasibility of this setup for detection of multiple biomarkers from different body fluids at point of care.