Digital monitoring of the microchannel filling flow dynamics using a non-contactless smartphone-based nano-liter precision flow velocity meter.

Microfluidic systems find widespread applications in diagnostics, biological research, chemistry, and engineering studies. Among their many critical parameters, flow rate plays a pivotal role in maintaining the functionality of microfluidic systems, including droplet-based microfluidic devices and those used in cell culture. It also significantly influences microfluidic mixing processes. Although various flow rate measurement devices have been developed, the challenge remains in accurately measuring flow rates within customized channels. This paper presents the development of a 3D-printed smartphone-based flow velocity meter. The 3D-printed platform is angled at 30° to achieve transparent flow visualization, and it doesn't require any external optical components such as external lenses and filters. Two LED modules integrated into the platform create a uniform illumination environment for video capture, powered directly by the smartphone. The performance of our platform, combined with a customized video processing algorithm, was assessed in three different channel types: uniform straight channels, straight channels with varying widths, and vessel-like channel patterns to demonstrate its versatility. Our device effectively measured flow velocities from 5.43 mm/s to 24.47 mm/s, with video quality at 1080p resolution and 60 frames per second, for which the measurement range can be extended by adjusting the frame rate. This flow velocity meter can be a useful analytical tool to evaluate and enhance microfluidic channel designs of various lab-on-a-chip applications.

Foldable low-cost point-of-care device for testing blood coagulation using smartphones.

Cardiovascular diseases (CVDs) caused by thrombotic events are a significant global health concern, affecting millions of people worldwide. The international normalized ratio (INR) is the most widely used measure of coagulation status, and frequent testing is required to adjust blood-thinning drug dosage, requiring hospital visits and experts to perform the test. Here we present a low-cost and portable smartphone-based device for screening INR levels from whole blood samples at the point of care. Our device uses a 3D printed platform and light-emitting diode backlight modules to create a uniform optical environment, and its foldable design allows for easy transport. Our device also features an algorithm that allows users to acquire and process video of sample flow in a microfluidic channel on their smartphone, providing a cost-effective and convenient option for blood coagulation monitoring at the point of care. We tested the performance of our smartphone-based INR device using both commercially available control samples and clinical human blood samples, demonstrating high accuracy and reliability. Our device has the potential to improve patient outcomes by enabling more frequent monitoring and, as appropriate, dosage adjustments of blood-thinning drugs, providing an affordable and portable option for screening INR levels at the point of care.

Non-invasive biomedical sensors for early detection and monitoring of bacterial biofilm growth at the point of care.

Bacterial infections have long been a serious global health issue. Biofilm formation complicates matters even more. The biofilm's extracellular polymeric substances (EPSs) matrix protects bacteria from the host's immune responses, yielding strong adhesion and drug resistance as the biofilm matures. Early bacterial biofilm detection and bacterial biofilm growth monitoring are crucial to treating biofilm-associated infections. Current detection methods are highly sensitive but not portable, are time-consuming, and require expensive equipment and complex operating procedures, limiting their use at the point of care. Therefore, there is an urgent need to develop affordable, on-body, and non-invasive biomedical sensors to continuously monitor and detect early biofilm growth at the point of care through personalized telemedicine. Herein, recent advances in developing non-invasive biomedical sensors for early detection and monitoring bacterial biofilm growth are comprehensively reviewed. First, biofilm's life cycle and its impact on the human body, such as biofilm-associated disease and infected medical devices, are introduced together with the challenges of biofilm treatment. Then, the current methods used in clinical and laboratory settings for biofilm detection and their challenges are discussed. Next, the current state of non-invasive sensors for direct and indirect detection of bacterial biofilms are summarized and highlighted with the detection parameters and their design details. Finally, commercially available products, challenges of current devices, and the further trend in biofilm detection sensors are discussed.

Mobile Diagnostic Devices for Digital Transformation in Personalized Healthcare.

Mobile devices have increasingly become an essential part of the healthcare system worldwide [...].

Sensing of electrolytes in urine using a miniaturized paper-based device.

Analyzing electrolytes in urine, such as sodium, potassium, calcium, chloride, and nitrite, has significant diagnostic value in detecting various conditions, such as kidney disorder, urinary stone disease, urinary tract infection, and cystic fibrosis. Ideally, by regularly monitoring these ions with the convenience of dipsticks and portable tools, such as cellphones, informed decision making is possible to control the consumption of these ions. Here, we report a paper-based sensor for measuring the concentration of sodium, potassium, calcium, chloride, and nitrite in urine, accurately quantified using a smartphone-enabled platform. By testing the device with both Tris buffer and artificial urine containing a wide range of electrolyte concentrations, we demonstrate that the proposed device can be used for detecting potassium, calcium, chloride, and nitrite within the whole physiological range of concentrations, and for binary quantification of sodium concentration.

Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer.

We report a field-portable and cost-effective imaging flow cytometer that uses deep learning and holography to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL h-1. This flow cytometer uses lens free color holographic imaging to capture and reconstruct phase and intensity images of microscopic objects in a continuously flowing sample, and automatically identifies Giardia lamblia cysts in real-time without the use of any labels or fluorophores. The imaging flow cytometer is housed in an environmentally-sealed enclosure with dimensions of 19 cm × 19 cm × 16 cm and weighs 1.6 kg. We demonstrate that this portable imaging flow cytometer coupled to a laptop computer can detect and quantify, in real-time, low levels of Giardia contamination (e.g., <10 cysts per 50 mL) in both freshwater and seawater samples. The field-portable and label-free nature of this method has the potential to allow rapid and automated screening of drinking water supplies in resource limited settings in order to detect waterborne parasites and monitor the integrity of the filters used for water treatment.

Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning.

Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.

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.

Smartphone-based turbidity reader.

Water quality is undergoing significant deterioration due to bacteria, pollutants and other harmful particles, damaging aquatic life and lowering the quality of drinking water. It is, therefore, important to be able to rapidly and accurately measure water quality in a cost-effective manner using e.g., a turbidimeter. Turbidimeters typically use different illumination angles to measure the scattering and transmittance of light through a sample and translate these readings into a measurement based on the standard nephelometric turbidity unit (NTU). Traditional turbidimeters have high sensitivity and specificity, but they are not field-portable and require electricity to operate in field settings. Here we present a field-portable and cost effective turbidimeter that is based on a smartphone. This mobile turbidimeter contains an opto-mechanical attachment coupled to the rear camera of the smartphone, which contains two white light-emitting-diodes to illuminate the water sample, optical fibers to transmit the light collected from the sample to the camera, an external lens for image formation, and diffusers for uniform illumination of the sample. Including the smartphone, this cost-effective device weighs only ~350 g. In our mobile turbidimeter design, we combined two illumination approaches: transmittance, in which the optical fibers were placed directly below the sample cuvette at 180° with respect to the light source, and nephelometry in which the optical fibers were placed on the sides of the sample cuvette at a 90° angle with respect to the to the light source. Images of the end facets of these fiber optic cables were captured using the smart phone and processed using a custom written image processing algorithm to automatically quantify the turbidity of each sample. Using transmittance and nephelometric readings, our mobile turbidimeter achieved accurate measurements over a large dynamic range, from 0.3 NTU to 2000 NTU. The accurate performance of our smartphone-based turbidimeter was also confirmed with various water samples collected in Los Angeles (USA), bacteria spiked water samples, as well as diesel fuel contaminated water samples. Having a detection limit of ~0.3 NTU, this cost-effective smartphone-based turbidimeter can be a useful analytical tool for screening of water quality in resource limited settings.

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.

Early detection of E. coli and total coliform using an automated, colorimetric and fluorometric fiber optics-based device.

Lack of access to clean water is a major global issue that affects millions of people worldwide. Drinking contaminated water can be extremely hazardous, so it is imperative that it is tested sufficiently. One method commonly used to determine the quality of water is testing for both E. coli and total coliform. Here, we present a cost-effective and automated device which can concurrently test drinking water samples for both E. coli and total coliform using an EPA-approved reagent. Equipped with a Raspberry Pi microcontroller and camera, we perform automated periodic measurements of both the absorption and fluorescence of the water under test over 24 hours. In each test, 100 mL of the water sample is split into a custom designed 40-well plate, where the transmitted blue light and the fluorescent light (under UV excitation) are collected by 520 individual optical fibers. Images of these fiber outputs are then acquired periodically, and digitally processed to determine the presence of the bacteria in each well of the 40-well plate. We demonstrate that this cost-effective device, weighing 1.66 kg, can automatically detect the presence of both E. coli and total coliform in drinking water within ∼16 hours, down to a level of one colony-forming unit (CFU) per 100 mL. Furthermore, due to its automated analysis, this approach is also more sensitive than a manual count performed by an expert, reducing the time needed to determine whether the water under test is safe to drink or not.

Smartphone-imaged microfluidic biochip for measuring CD64 expression from whole blood.

Sepsis, a life-threatening syndrome that contributes to millions of deaths annually worldwide, represents a moral and economic burden to the healthcare system. Although no single, or even a combination of biomarkers has been validated for the diagnosis of sepsis, multiple studies have shown the high specificity of CD64 expression on neutrophils (nCD64) to sepsis. The analysis of elevated nCD64 in the first 2-6 hours after infection during the pro-inflammatory stage could significantly contribute to early sepsis diagnosis. Therefore, a rapid and automated device to periodically measure nCD64 expression at the point-of-care (POC) could lead to timely medical intervention and reduced mortality rates. Current accepted technologies for measuring nCD64 expression, such as flow cytometry, require manual sample preparation and long incubation times. For POC applications, however, the technology should be able to measure nCD64 expression with little to no sample preparation. In this paper, we demonstrate a smartphone-imaged microfluidic biochip for detecting nCD64 expression in under 50 min. In our assay, first unprocessed whole blood is injected into a capture chamber to immunologically capture nCD64 along a staggered array of pillars, which were previously functionalized with an antibody against CD64. Then, an image of the capture channel is taken using a smartphone-based microscope. This image is used to measure the cumulative fraction of captured cells (γ) as a function of length in the channel. During the image analysis, a statistical model is fitted to γ in order to extract the probability of capture of neutrophils per collision with a pillar (ε). The fitting shows a strong correlation with nCD64 expression measured using flow cytometry (R2 = 0.82). Finally, the applicability of the device to sepsis was demonstrated by analyzing nCD64 from 8 patients (37 blood samples analyzed) along the time they were admitted to the hospital. Results from this analysis, obtained using the smartphone-imaged microfluidic biochip were compared with flow cytometry. Again, a correlation coefficient R2 = 0.82 (slope = 0.99) was obtained demonstrating a good linear correlation between the two techniques. Deployment of this technology in ICU could significantly enhance patient care worldwide.

Rapid imaging, detection, and quantification of Nosema ceranae spores in honey bees using mobile phone-based fluorescence microscopy.

Recent declines in honey bee colonies in the United States have put increased strain on agricultural pollination. Nosema ceranae and Nosema apis, are microsporidian parasites that are highly pathogenic to honey bees and have been implicated as a factor in honey bee losses. While traditional methods for quantifying Nosema infection have high sensitivity and specificity, there is no field-portable device for field measurements by beekeepers. Here we present a field-portable and cost-effective smartphone-based platform for detection and quantification of chitin-positive Nosema spores in honey bees. The handheld platform, weighing only 374 g, consists of a smartphone-based fluorescence microscope, a custom-developed smartphone application, and an easy to perform sample preparation protocol. We tested the performance of the platform using samples at different parasite concentrations and compared the method with manual microscopic counts and qPCR quantification. We demonstrated that this device provides results that are comparable with other methods, having a limit of detection of 0.5 × 106 spores per bee. Thus, the assay can easily identify infected colonies and provide accurate quantification of infection levels requiring treatment of infection, suggesting that this method is potentially adaptable for diagnosis of Nosema infection in the field by beekeepers. Coupled with treatment recommendations, this protocol and smartphone-based optical platform could improve the diagnosis and treatment of nosemosis in bees and provide a powerful proof-of-principle for the use of such mobile diagnostics as useful analytical tools for beekeepers in resource-limited settings.

Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning.

Parasitic infections constitute a major global public health issue. Existing screening methods that are based on manual microscopic examination often struggle to provide sufficient volumetric throughput and sensitivity to facilitate early diagnosis. Here, we demonstrate a motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism. Based on this principle, a cost-effective and mobile instrument, which rapidly screens ~3.2 mL of fluid sample in three dimensions, was built to automatically detect and count motile microorganisms using their holographic time-lapse speckle patterns. We demonstrate the capabilities of our platform by detecting trypanosomes, which are motile protozoan parasites, with various species that cause deadly diseases affecting millions of people worldwide. Using a holographic speckle analysis algorithm combined with deep learning-based classification, we demonstrate sensitive and label-free detection of trypanosomes within spiked whole blood and artificial cerebrospinal fluid (CSF) samples, achieving a limit of detection of ten trypanosomes per mL of whole blood (~five-fold better than the current state-of-the-art parasitological method) and three trypanosomes per mL of CSF. We further demonstrate that this platform can be applied to detect other motile parasites by imaging Trichomonas vaginalis, the causative agent of trichomoniasis, which affects 275 million people worldwide. With its cost-effective, portable design and rapid screening time, this unique platform has the potential to be applied for sensitive and timely diagnosis of neglected tropical diseases caused by motile parasites and other parasitic infections in resource-limited regions.

Smartphones Democratize Advanced Biomedical Instruments and Foster Innovation.

From microscopy to diagnostics and monitoring of vital parameters, scientists, engineers, and educators have been making use of smartphones and smartphone components in various innovative ways, helping to democratize advanced measurement instruments used in research and education.

Identification of pathogenic bacteria in complex samples using a smartphone based fluorescence microscope.

Diagnostics based on fluorescence imaging of biomolecules is typically performed in well-equipped laboratories and is in general not suitable for remote and resource limited settings. Here we demonstrate the development of a compact, lightweight and cost-effective smartphone-based fluorescence microscope, capable of detecting signals from fluorescently labeled bacteria. By optimizing a peptide nucleic acid (PNA) based fluorescence in situ hybridization (FISH) assay, we demonstrate the use of the smartphone-based microscope for rapid identification of pathogenic bacteria. We evaluated the use of both a general nucleic acid stain as well as species-specific PNA probes and demonstrated that the mobile platform can detect bacteria with a sensitivity comparable to that of a conventional fluorescence microscope. The PNA-based FISH assay, in combination with the smartphone-based fluorescence microscope, allowed us to qualitatively analyze pathogenic bacteria in contaminated powdered infant formula (PIF) at initial concentrations prior to cultivation as low as 10 CFU per 30 g of PIF. Importantly, the detection can be done directly on the smartphone screen, without the need for additional image analysis. The assay should be straightforward to adapt for bacterial identification also in clinical samples. The cost-effectiveness, field-portability and simplicity of this platform will create various opportunities for its use in resource limited settings and point-of-care offices, opening up a myriad of additional applications based on other fluorescence-based diagnostic assays.

Quantitative Fluorescence Sensing Through Highly Autofluorescent, Scattering, and Absorbing Media Using Mobile Microscopy.

Compact and cost-effective systems for in vivo fluorescence and near-infrared imaging in combination with activatable reporters embedded inside the skin to sample interstitial fluid or blood can enable a variety of biomedical applications. However, the strong autofluorescence of human skin creates an obstacle for fluorescence-based sensing. Here we introduce a method for quantitative fluorescence sensing through highly autofluorescent, scattering, and absorbing media. For this, we created a compact and cost-effective fluorescence microscope weighing <40 g and used it to measure various concentrations of a fluorescent dye embedded inside a tissue phantom, which was designed to mimic the optical characteristics of human skin. We used an elliptical Gaussian beam excitation to digitally separate tissue autofluorescence from target fluorescence, although they severely overlap in both space and optical spectrum. Using ∼10-fold less excitation intensity than the safety limit for skin radiation exposure, we successfully quantified the density of the embedded fluorophores by imaging the skin phantom surface and achieved a detection limit of ∼5 × 10(5) and ∼2.5 × 10(7) fluorophores within ∼0.01 µL sample volume that is positioned 0.5 and 2 mm below the phantom surface, corresponding to a concentration of 105.9 pg/mL and 5.3 ng/mL, respectively. We also confirmed that this approach can track the spatial misalignments of the mobile microscope with respect to the embedded target fluorescent volume. This wearable microscopy platform might be useful for designing implantable biochemical sensors with the capability of spatial multiplexing to continuously monitor a panel of biomarkers and chronic conditions even at patients' home.

MEMS biosensors for detection of methicillin resistant Staphylococcus aureus.

This review presents the current state of the conventional methods, microfluidic based biosensors, and the commercial products used in the detection of methicillin resistant Staphylococcus aureus (MRSA), which is one of the most important threats of nosocomial infections in many parts of the world. The early detection of MRSA in the specimens of the patients is important to enable the appropriate treatment, to decrease morbidity and mortality rates, and to manage control actions in the healthcare units. Thus, rapid and inexpensive diagnostic systems with high sensitivity and specificity are essential to prevent MRSA to be an emerging public health threat. The design and fabrication of new diagnostic systems necessitates working in collaboration between different disciplines to make new challenges in the field of clinical diagnosis and to meet the demands of clinicians. It is certain that in the near future, MEMS and nanotechnology based detection methods will take the place of current methods in clinical diagnosis. The evaluation of new trends for specificity, sensitivity, cost effectiveness, disposability, low weight, ease of use, and facile access should be taken into consideration.