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Early detection and identification of pathogenic bacteria such as Escherichia coli (E. coli) is an essential task for public health. The conventional culture-based methods for bacterial colony detection usually take >24 hours to get the final read-out. Here, we demonstrate a bacterial colony-forming-unit (CFU) detection system exploiting a thin-film-transistor (TFT)-based image sensor array that saves ~12 hours compared to the Environmental Protection Agency (EPA)-approved methods. To demonstrate the efficacy of this CFU detection system, a lensfree imaging modality was built using the TFT image sensor with a sample field-of-view of ~10 cm^2. Time-lapse images of bacterial colonies cultured on chromogenic agar plates were automatically collected at 5-minute intervals. Two deep neural networks were used to detect and count the growing colonies and identify their species. When blindly tested with 265 colonies of E. coli and other coliform bacteria (i.e., Citrobacter and Klebsiella pneumoniae), our system reached an average CFU detection rate of 97.3% at 9 hours of incubation and an average recovery rate of 91.6% at ~12 hours. This TFT-based sensor can be applied to various microbiological detection methods. Due to the large scalability, ultra-large field-of-view, and low cost of the TFT-based image sensors, this platform can be integrated with each agar plate to be tested and disposed of after the automated CFU count. The imaging field-of-view of this platform can be cost-effectively increased to >100 cm^2 to provide a massive throughput for CFU detection using, e.g., roll-to-roll manufacturing of TFTs as used in the flexible display industry., 18 Pages, 6 Figures

Soft, skin-mounted microfluidic devices can collect microliter volumes of eccrine sweat and are capable of in situ real-time analysis of different biomarkers to assess physiological state and health. Chrono-analysis of sweat can be implemented to monitor temporal variations of biomarker concentrations over a certain period of interest. Conventional methods used to capture sweat or some of the newly developed microfluidic platforms for sweat collection and analysis are based on absorbent pads. They suffer from evaporation, leading to considerable deviations in the concentration of the biomarkers. Here, a paperintegrated microfluidic device is presented for sequential analysis of sweat that is easy to fabricate and does not include air exits for each reservoir, which reduces undesirable effects of sweat evaporation. Furthermore, the high capillary force of filter paper is leveraged to route the liquid into the chambers in a sequential fashion and allow further chemical analysis. The employed design of the paper-embedded microfluidic device successfully samples and analyzes artificial sweat sequentially for flow rates up to 5 ?L min?1 without showing any leakage. We demonstrated the performance of the device, employing colorimetric assays for chrono-analysis of glucose standard solutions at concentrations in the range of 10– 100 mM and pH of sweat during exercise. The results reveal the presented approach's functionality and potential to analyze the concentration of biomarkers over a certain period sequentially., Scientific and Technological Research Council of Turkey (TÜBİTAK); European Union (EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship

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.

Environmental factors such as temperature, nutrients, and pollutants affect the growth rates and physical characteristics of microalgae populations. As algae play a vital role in marine ecosystems,...

We report a rapid, automated, portable and high-throughput bacterial colony detection and classification system using a thin-film-transistor array and deep learning, achieving >12 hours of time savings compared to US Environmental Protection Agency-approved culture-based methods.

We present a deep learning-aided imaging system for early detection and classification of live bacterial colonies by capturing time-lapse holographic images of an agar plate and analyzing these images using deep neural networks. We blindly tested our system by identifying Escherichia coli and total coliform bacteria in spiked water samples and successfully detected 90% of the bacterial colonies within 7-10 h, while keeping 99.2~100% precision. We further classified the corresponding species within 7.6-12 h of incubation with 80% accuracy, which represents >12 h time-savings. Our system also achieved a limit-of-detection of ~1 CFU/L within 9 h of total test time.

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 (e). 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.

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., Imaging: Detecting parasites with holograms and AI Scientists have developed a cost-effective and portable technology that uses three-dimensional holograms to detect parasites in human bodily fluids, providing a more sensitive technique for diagnosing diseases, without the need for chemical labeling. A major global health problem, parasitic infections affect billions of people around the world and can lead to severe illnesses and death. Some of the current screening techniques, however, use manual microscopic examinations that struggle to provide the sensitivity required for early diagnoses. Now, Aydogan Ozcan and colleagues from the University of California Los Angeles in the United States have developed a cost-effective and portable optical device that uses lensless time-resolved holographic speckle imaging combined with artificial intelligence to automatically detect rare parasites in bodily fluids. The device has the potential to perform rapid and early diagnoses of diseases caused by parasitic infections and could be used in developing countries.

We report a label-free, field-portable, holographic imaging flow cytometer that can automatically detect and count Giardia lamblia cysts in water samples with a throughput of 100 mL/h. Our cytometer has the dimensions of 19×19×16 cm and a laptop computer-connected to it reconstructs the phase and intensity images of the flowing microparticles in the sample at three different wavelengths and classifies them by a trained convolutional neural network, thereby detecting the Giardia cysts in real time. We experimentally demonstrated that our system can detect Giardia contamination in fresh and seawater samples containing as low as

We present a field-portable and high-throughput imaging flow-cytometer, which performs phenotypic analysis of microalgae using image processing and deep learning. This computational cytometer weighs ~1.6kg, and captures holographic images of water samples containing microalgae, flowing in a microfluidic channel at a rate of 100mL/h. Automated analysis is performed by extracting the spatial and spectral features of the reconstructed images to automatically identify/count the target algae within the sample, using image processing and convolutional neural networks. Changes within the measured features and the composition of the microalgae can be rapidly analyzed to reveal even minute deviations from the normal state of the population.

We present a deep learning-based high-throughput cytometer to detect rare cells in whole blood using a cost-effective and light-weight design. This system uses magnetic-particles to label and enrich the target cells. Then, a periodically-alternating magnetic-field creates time-modulated diffraction patterns of the target cells that are recorded using a lensless microscope. Finally, a custom-designed convolutional network is used to detect and classify the target cells based on their modulated spatio-temporal patterns. This cytometer was tested with cancer cells spiked in whole blood to achieve a limit-of-detection of 10 cells/mL. This compact, cost-effective and high-throughput cytometer might serve diagnostics needs in resource-limited-settings.

We present a field-portable and label-free holographic imaging flow-cytometer, which quantifies the amount of Giardia lamblia cysts in water samples with a detection limit of

Using deep learning and lensfree holographic imaging, we report early detection and classification of bacterial colonies in water samples. Our system detects 1 colony-forming unit (CFU) per Liter within 9 h of total test time.

We report a field-portable and high-throughput imaging flow-cytometer to perform label-free phenotypic analysis of microalgae populations by extracting and processing the spatial and spectral features of their reconstructed holographic images using deep learning.

We report a deep learning-based framework which can be used to screen thin blood smears for sickle-cell-disease using images captured by a smartphone-based microscope. This framework first uses a deep neural network to enhance and standardize the smartphone images to the quality of a diagnostic level benchtop microscope, and a second deep neural network performs cell segmentation. We experimentally demonstrated that this technique can achieve 98% accuracy with an area-under-the-curve (AUC) of 0.998 on a blindly tested dataset made up of thin blood smears coming from 96 patients, of which 32 had been diagnosed with sickle cell disease.

We present a high-throughput and cost-effective computational cytometer for rare cell detection, where the target cells are specifically labeled with magnetic particles and exhibit an oscillatory motion under a periodically-changing magnetic field. The time-varying diffraction patterns of the oscillating cells are then captured with a holographic imaging system and are further classified by a customized pseudo-3D convolutional network. To evaluate the performance of our technique, we detected serially-diluted MCF7 cancer cells that were spiked in whole blood, achieving a limit of detection (LoD) of 10 cells per 1 mL of whole blood.

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.

We report a highly-sensitive, high-throughput, and cost-effective bacteria identification system which continuously captures and reconstructs holographic images of an agar-plate and analyzes the time-lapsed images with deep learning models for early detection of colonies. The performance of our system was confirmed by detection and classification of Escherichia coli, Enterobacter aerogenes, and Klebsiella pneumoniae in water samples. We detected 90% of the bacterial colonies and their growth within 7-10h (>95% within 12h) with ~100% precision, and correctly identified the corresponding species within 7.6-12h with 80% accuracy, and achieved time savings of >12h as compared to the gold-standard EPA-approved methods.

We report a field-portable and cost-effective imaging flow cytometer that uses deep learning to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL/h. This flow cytometer uses lensfree 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 x 19 cm x 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., 17 Pages, 5 Figures, 1 Table

We present a cost-effective and high-throughput computational cytometer using a magnetically-modulated lensless imaging technique and deep learning-based classification, to rapidly detect rare cells in whole blood, achieving a detection limit of 10 cells/mL.

Stormwater control measures (SCM) can remove and accumulate microplastics and may serve as a long-term source of microplastics for groundwater pollution because of their potential for downward mobility in subsurface. Furthermore, the number of microplastics accumulated in SCM may have been underestimated as the calculation typically only accounts for microplastics accumulated via episodic stormwater loading and ignores microplastic accumuation via continuous atmospheric deposition. To evaluate the source pathways of accumulated microplastics and their potential for downward mobility to groundwater, we analyzed spatial distributions of microplastics above ground on the canopy around SCM and below ground in the subsurface in and outside the boundaries of fourteen SCM in Los Angeles. Using an exponential model, we link subsurface retardation of microplastics to the median particle size of soil (D

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.

Schistosomiasis affects over 170 million people in Africa. Here we compare a novel, low-cost mobile phone microscope to a conventional light microscope for the label-free diagnosis of Schistosoma haematobium infections in a rural Ghanaian school setting. We tested the performance of our handheld microscope using 60 slides that were randomly chosen from an ongoing epidemiologic study in school-aged children. The mobile phone microscope had a sensitivity of 72.1% (95% confidence interval [CI]: 56.1-84.2), specificity of 100% (95% CI: 75.9-100), positive predictive value of 100% (95% CI: 86.3-100), and a negative predictive value of 57.1% (95% CI: 37.4-75.0). With its modest sensitivity and high specificity, this handheld and cost-effective mobile phone-based microscope is a stepping-stone toward developing a powerful tool in clinical and public health settings where there is limited access to conventional laboratory diagnostic support.

Giardia lambliais a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting ofGiardiacysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection ofGiardiacysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved a limit of detection of 12 cysts per 10 ml, an average cyst capture efficiency of ~79%, and an accuracy of ~95%. Providing rapid detection and quantification of waterborne pathogens without the need for a microbiology expert, this field-portable imaging and sensing platform running on a smartphone could be very useful for water quality monitoring in resource-limited settings.

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., Deep Learning Cytometry: Magnetically modulating rare cells for high-throughput detection Rare cells of medical significance can be detected in blood by a high-throughput imaging technique that analyzes movements of magnetically-labelled target cells using deep-learning. Analysis of magnetically-modulated light interference using an artificial neural network enhances the ability of the system to specifically and sensitively detect the tagged cells as they respond to a periodically changing magnetic field. Researchers in the USA, led by Aydogan Ozcan at the University of California, Los Angeles, demonstrated the technique using rare cancer cells added to blood. They achieved a detection limit as low as ten cells per milliliter. The optical system uses a holographic process called speckle imaging, which is much more compact and achieves higher throughput than traditional methods. It holds significant potential for the rapid analysis of blood and other bodily fluids to diagnose and monitor disease.

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.

We introduce a field-portable and cost-effective holographic imaging flow cytometer, which provides phase contrast microscopic images of the contents of water samples at a throughput of 100 ml/h. This imaging cytometer uses a high power multi-colored LED and a custom designed circuit to illuminate continuously flowing water samples with short-pulses of red, green, and blue light that are simultaneously on, thereby eliminating motion blur and making the system vibration resistant. The recorded color holograms are segmented and reconstructed in real time and are phase recovered using a deep learning-based algorithm. Weighing 1kg with the dimensions of 15.5 cm × 15 cm × 12.5 cm, our label-free imaging flow-cytometer is controlled by a laptop computer equipped with a graphical processing unit. We tested the capabilities of our field-portable device by imaging micro- and nano-plankton inside ocean water samples collected at six beaches along the California coastline. We also determined Pseudo-Nitzschia algae concentration of these samples, providing a good agreement with the measurements made by the California Department of Public Health. Our device represents 1-2 orders of magnitude reduction in the cost and size of an imaging flow cytometer compared to state-of-the-art designs, while providing a similar or better performance in terms of volumetric throughput, detection limit and imaging resolution.

There is an increasing but unmet need for accurate, label-free and automated bio-aerosol sensing. To address this need, we developed a high-throughput, cost-effective and portable bio-aerosol sensor based on computational microscopy and deep-learning. Our device is composed of an impactor and a lens-less digital holographic on-chip microscope. It screens air at 13 liters per minute, and captures bio-aerosols on the impactor substrate. An image sensor then records the in-line holograms of these captured bio-aerosols in real time. Using these recorded in-line holograms, the captured bio-aerosols are analyzed within a minute, facilitated by two deep convolutional neural networks (CNNs): the first CNN simultaneously performs auto-focusing and phase-recovery to reconstruct both the amplitude and phase images of each bio-aerosol with sub-micron resolution; and the second CNN performs automatic classification of the reconstructed bio-aerosols into pre-trained classes and counting their densities in air. As a proof-of-concept, we demonstrated reconstruction and label-free sensing of five different types of bio-aerosols: Bermuda grass pollen, oak tree pollen, ragweed pollen, Aspergillus spore, and Alternaria spore. These bio-aerosols form some of the most common allergens in air. Using our mobile bio-aerosol sensor, we achieved ~94% precision and recall in differentiating these bio-aerosols without the use of any labeling. We also demonstrated successful sensing of oak tree pollens in the field using our mobile device. To the best of our knowledge, this is the first demonstration of automated label-free sensing of bio-aerosols using a portable device, which is enabled by computational microscopy and deep-learning. It has broad applications in label-free bio-aerosol sensing and air-quality monitoring.

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.

We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h. The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel. These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling. Motion blur is eliminated by simultaneously illuminating the sample with red, green, and blue light-emitting diodes that are pulsed. Operated by a laptop computer, this portable device measures 15.5 cm × 15 cm × 12.5 cm, weighs 1 kg, and compared to standard imaging flow cytometers, it provides extreme reductions of cost, size and weight while also providing a high volumetric throughput over a large object size range. We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro- and nanoplankton composition. Furthermore, we measured the concentration of a potentially toxic alga (Pseudo-nitzschia) in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health. The cost-effectiveness, compactness, and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for large-scale and continuous monitoring of the ocean microbiome, including its plankton composition.

Fluorescence sensing through skin using wearable and cost-effective sensors can enable numerous biomedical applications, including continuous monitoring and quantification of biomarkers in conjunction with embedded fluorescent biosensors. Strong autofluorescence of the skin and its varying temporal response to excitation make it challenging to achieve highly sensitive fluorescence sensing at the visible part of the electromagnetic spectrum. Here, we demonstrate a compact, cost-effective and light-weight fluorescence imaging system for quantitative sensing through a highly autofluorescent, scattering and absorbing medium. We built a mobile fluorescence microscope weighing < 40 grams to sensitively quantify the concentration of fluorescent dyes through a skin tissue phantom. The optical characteristics of the skin phantom, such as scattering, absorption, and autofluorescence were designed to closely resemble the characteristics of human skin. In order to separate the target fluorescence emission signal from the tissue phantom’s autofluorescence we utilized an oblique Gaussian excitation profile, and performed our processing in the spatial frequency domain. Using an excitation intensity that is 8-fold below the safe exposure limit determined for human skin, we detected and quantified the concentration of Alexa 647 dye molecules in a microfluidic reservoir (with a volume of 0.01 µl) that is positioned 0.5 mm and 2 mm underneath our skin phantom, and achieved a detection limit of ~106 pg/ml and 5.3 ng/ml, respectively. Our method is also robust to lateral misalignments between the sample and the imaging device, with e.g., ~2-fold loss in limit of detection for ~0.6 mm lateral misalignment.

Mobile health technologies offer great promise for reducing healthcare costs and improving patient care. Wearable and implantable technologies are contributing to a transformation in the mobile health era in terms of improving healthcare and health outcomes and providing real-time guidance on improved health management and tracking. In this article, we review the biomedical applications of wearable and implantable medical devices and sensors, ranging from monitoring to prevention of diseases, as well as the materials used in the fabrication of these devices and the standards for wireless medical devices and mobile applications. We conclude by discussing some of the technical challenges in wearable and implantable technology and possible solutions for overcoming these difficulties.

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.