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Molecular diagnostics is typically outsourced to well-equipped centralized laboratories, often far from the patient. We developed molecular assays and portable optical imaging designs that permit on-site diagnostics with a cost-effective mobile-phone-based multimodal microscope. We demonstrate that targeted next-generation DNA sequencing reactions and in situ point mutation detection assays in preserved tumour samples can be imaged and analysed using mobile phone microscopy, achieving a new milestone for tele-medicine technologies. On-site diagnostics technologies allow for rapid, cost-effective diagnosis with a particular importance for remote communities. Here the authors demonstrate the use of mobile phone based microscopy for targeted DNA sequencing and in situ point mutation detection in tumours.

Smartphone fluorescence microscopy has various applications in point-of-care (POC) testing and diagnostics, ranging from e.g., quantification of immunoassays, detection of microorganisms, to sensing of viruses. An important need in smartphone-based microscopy and sensing techniques is to improve the detection sensitivity to enable quantification of extremely low concentrations of target molecules. Here, we demonstrate a general strategy to enhance the detection sensitivity of a smartphone-based fluorescence microscope by using surface-enhanced fluorescence (SEF) created by a thin metal-film. In this plasmonic design, the samples are placed on a silver-coated glass slide with a thin spacer, and excited by a laser-diode from the backside through a glass hemisphere, generating surface plasmon polaritons. We optimized this mobile SEF system by tuning the metal-film thickness, spacer distance, excitation angle and polarization, and achieved ~10-fold enhancement in fluorescence intensity compared to a bare glass substrate, which enabled us to image single fluorescent particles as small as 50 nm in diameter and single quantum-dots. Furthermore, we quantified the detection limit of this platform by using DNA origami-based brightness standards, demonstrating that ~80 fluorophores per diffraction-limited spot can be readily detected by our mobile microscope, which opens up new opportunities for POC diagnostics and sensing applications in resource-limited-settings.

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.

Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an automated and cost-effective cellphone-based 96-well microtiter-plate (MTP) reader, capable of performing AST without the need for trained diagnosticians. Our system includes a 3D-printed smartphone attachment that holds and illuminates the MTP using a light-emitting-diode array. An inexpensive optical fiber-array enables the capture of the transmitted light of each well through the smartphone camera. A custom-designed application sends the captured image to a server to automatically determine well-turbidity, with results returned to the smartphone in ~1 minute. We tested this mobile-reader using MTPs prepared with 17 antibiotics targeting Gram-negative bacteria on clinical isolates of Klebsiella pneumoniae, containing highly-resistant antimicrobial profiles. Using 78 patient isolate test-plates, we demonstrated that our mobile-reader meets the FDA-defined AST criteria, with a well-turbidity detection accuracy of 98.21%, minimum-inhibitory-concentration accuracy of 95.12%, and a drug-susceptibility interpretation accuracy of 99.23%, with no very major errors. This mobile-reader could eliminate the need for trained diagnosticians to perform AST, reduce the cost-barrier for routine testing, and assist in spatio-temporal tracking of bacterial resistance.

The optical detection of nanoparticles, including viruses and bacteria, underpins many of the biological, physical and engineering sciences. However, due to their low inherent scattering, detection of these particles remains challenging, requiring complex instrumentation involving extensive sample preparation methods, especially when sensing is performed in liquid media. Here we present an easy-to-use, high-throughput, label-free and cost-effective method for detecting nanoparticles in low volumes of liquids (25 nL) on a disposable chip, using an acoustically actuated lens-free holographic system. By creating an ultrasonic standing wave in the liquid sample, placed on a low-cost glass chip, we cause deformations in a thin liquid layer (850 nm) containing the target nanoparticles (≥140 nm), resulting in the creation of localized lens-like liquid menisci. We also show that the same acoustic waves, used to create the nanolenses, can mitigate against non-specific, adventitious nanoparticle binding, without the need for complex surface chemistries acting as blocking agents. Detection of small, translucent particles is challenging due to their low inherent scattering. Here, the authors present an easy, high-throughput, label-free method for detecting nanoparticles in low volumes of liquids on a disposable chip, using an acoustically actuated lens-free holographic system.

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.

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.

The ELISA is the mainstay for sensitive and quantitative detection of protein analytes. Despite its utility, ELISA is time-consuming, resource-intensive, and infrastructure-dependent, limiting its availability in resource-limited regions. Here, we describe a self-contained immunoassay platform (the “D4 assay”) that converts the sandwich immunoassay into a point-of-care test (POCT). The D4 assay is fabricated by inkjet printing assay reagents as microarrays on nanoscale polymer brushes on glass chips, so that all reagents are “on-chip,” and these chips show durable storage stability without cold storage. The D4 assay can interrogate multiple analytes from a drop of blood, is compatible with a smartphone detector, and displays analytical figures of merit that are comparable to standard laboratory-based ELISA in whole blood. These attributes of the D4 POCT have the potential to democratize access to high-performance immunoassays in resource-limited settings without sacrificing their performance.

Current contaminant and residue monitoring throughout the food chain is based on sampling, transport, administration, and analysis in specialized control laboratories. This is a highly inefficient and costly process since typically more than 99 % of the samples are found to be compliant. On-site simplified prescreening may provide a scenario in which only samples that are suspect are transported and further processed. Such a prescreening can be performed using a small attachment on a cellphone. To this end, a cellphone-based imaging platform for a microsphere fluorescence immunoassay that detects the presence of anti-recombinant bovine somatotropin (rbST) antibodies in milk extracts was developed. RbST administration to cows increases their milk production, but is illegal in the EU and a public health concern in the USA. The cellphone monitors the presence of anti-rbST antibodies (rbST biomarker), which are endogenously produced upon administration of rbST and excreted in milk. The rbST biomarker present in milk extracts was captured by rbST covalently coupled to paramagnetic microspheres and labeled by quantum dot (QD)-coupled detection antibodies. The emitted fluorescence light from these captured QDs was then imaged using the cellphone camera. Additionally, a dark-field image was taken in which all microspheres present were visible. The fluorescence and dark-field microimages were analyzed using a custom-developed Android application running on the same cellphone. With this setup, the microsphere fluorescence immunoassay and cellphone-based detection were successfully applied to milk sample extracts from rbST-treated and untreated cows. An 80 % true-positive rate and 95 % true-negative rate were achieved using this setup. Next, the cellphone-based detection platform was benchmarked against a newly developed planar imaging array alternative and found to be equally performing versus the much more sophisticated alternative. Using cellphone-based on-site analysis in future residue monitoring can limit the number of samples for laboratory analysis already at an early stage. Therewith, the entire monitoring process can become much more efficient and economical. Figure Cellphone-based detection platform for rbST biomarker analysis in milk extracts using a microsphere fluorescence immunoassay

Conventional light microscopy is the most common diagnostic technique, however laboratory infrastructure, qualified laboratory technicians, and permanent energy sources are limited in many schistosomiasis-endemic regions, making microscopy unavailable to most affected communities [4]. Mobile phone microscopy has several attributes that make it a promising tool for use in low-resource settings, namely that they are battery powered, handheld, easy to use, and have the potential for automated diagnoses [6]. Future studies will evaluate the sensitivity and specificity of this device compared to gold standard microscopy and evaluate their potential diagnostic role compared to other diagnostic techniques for S. haematobium, such as urine reagent sticks for haematuria.