Quantitative hematocrit measurement of whole blood in a point-of-care lateral flow device using a smartphone flow tracking app

Smartphone-enabled medical diagnostics and environmental monitoring for rural Africa

Africa has made significant progress in healthcare metrics over the past two decades. However, providing access and quality of healthcare to people living in rural regions remains a challenge. In this respect, so-called mobile health (mHealth) and smartphone-enabled diagnostics methods could be of great use in providing services, both necessary and novel, to rural populations. This review provides a basic background for understanding various concepts behind smartphone-enabled methods and summarizes existing examples in the fields of medical diagnostics and environmental monitoring. The aim is to engage young African students to contribute to the field but also to provide decision makers with ideas on low-cost solutions for rural healthcare.

A passive blood separation sensing platform for point-of-care devices

The blood test is one of the most performed investigations in clinical practice, with samples typically analysed in a centralised laboratory. Many of these tests monitor routine conditions that would benefit from a point-of-care approach, reducing the burden on practitioners, patients and healthcare systems. Such a decentralised model requires the development of sophisticated, yet easy-to-use technology; however, platforms that combine high-performance with low-cost and simplicity remain scarce. Moreover, most research papers only address a subset of requirements and study specific aspects in isolation. A systems approach that considers the interplay between each element of the technology is clearly required to develop a coherent solution. Here, we present such a systems approach in the context of testing for C-reactive protein (CRP), a commonly requested test in clinical practise that indicates inflammation and is particularly relevant for monitoring patients with chronic diseases, e.g. those with rheumatoid arthritis or who are undergoing cancer therapy. The approach we take here features an entirely passive microfluidic cartridge for blood separation, integrated with a high-performance sensing platform which we have tested in a real-world context. The device is compatible with a handheld detection unit and is simple to use yet can accurately detect CRP levels at clinically relevant levels.

Innovations in animal health: artificial intelligence-enhanced hematocrit analysis for rapid anemia detection in small ruminants

Due to their value as a food source, fiber, and other products globally, there has been a growing focus on the wellbeing and health of small ruminants, particularly in relation to anemia induced by blood-feeding gastrointestinal parasites like Haemonchus contortus. The objective of this study was to assess the packed cell volume (PCV) levels in blood samples from small ruminants, specifically goats, and create an efficient biosensor for more convenient, yet accurate detection of anemia for on-farm use in agricultural environments for animal production optimization. The study encompassed 75 adult male Spanish goats, which underwent PCV testing to ascertain their PCV ranges and their association with anemic conditions. Using artificial intelligence-powered machine learning algorithms, an advanced, easy-to-use sensor was developed for rapidly alerting farmers as to low red blood cell count of their animals in this way to enable timely medical intervention. The developed sensor utilizes a semi-invasive technique that requires only a small blood sample. More precisely, a volume of 30 μL of blood was placed onto Whatman filter paper No. 1, previously soaked with anhydrous glycerol. The blood dispersion pattern on the glycerol-infused paper was then recorded using a smartphone after 180 s. Subsequently, these images were examined in correlation with established PCV values obtained from conventional PCV analysis. Four separate machine learning models (ML) supported models, namely support vector machine (SVM), K-nearest neighbors (KNN), backpropagation neural network (BPNN), and image classification-based Keras model, were created and assessed using the image dataset. The dataset consisted of 1,054 images that were divided into training, testing, and validation sets in a 70:20:10 ratio. The initial findings indicated a detection accuracy of 76.06% after only 10 epochs for recognizing different levels of PCV in relation to anemia, ranging from healthy to severely anemic. This testing accuracy increased markedly, to 95.8% after 100 epochs and other model parameter optimization. Results for SVM had an overall F1 score of 74-100% in identifying the PCV range for blood pattern images representing healthy to severely anemic animals, and BPNN showed 91-100% accuracy in identifying the PCV range for anemia detection. This work demonstrates that AI-driven biosensors can be used for on-site rapid anemia detection. Optimized machine learning models maximize detection accuracy, proving the sensor's validity and rapidity in assessing anemia levels. This breakthrough will allow farmers, with rapid results, to increase animal wellbeing and agricultural productivity.

Automatic characterization of capillary flow profile of liquid samples on μTADs based on capacitance measurement

Capillary flow profile of liquid samples in porous media is closely related to the important properties of liquid samples, including the viscosity and the surface energy. Therefore, capillary flow profile can be used as an index to differentiate liquid samples with different properties. Fast and automatic characterization of capillary flow profile of liquid samples is necessary. In this work, we develop a portable and economical capacitance acquisition system (CASY) to easily obtain the capillary flow profile of liquid samples on microfluidic thread-based analytical devices (μTADs) by measuring the capacitance during the capillary flow. At first, we validate the accuracy of this method by comparing with the traditional method by video analysis in obtaining the capillary flow profiles in μTADs of cotton threads or glass fiber threads. Then we use it to differentiate liquid samples with different viscosity (mixture of water and glycerol). In addition, capillary flow profile on μTADs with chemical valves (chitosan or sucrose) can also be obtained on this device. Lastly, we show the potential of this device in measurement of hematocrit (HCT) of whole blood samples. This device can be used to catalog liquid biological samples with different properties in point-of-care diagnostics in the near future.

Smartphone based lateral flow immunoassay quantifications

Lateral Flow Immunoassay (LFI) is a disposable tool designed to detect target substances using minimal resources. For qualitative analysis, LFI does not require a device (i.e., reader) to interpret test results. However, various studies have been conducted to implement quantitative analysis using LFI systems, incorporating LFI along with electrical/electronic readers, to overcome the limitations associated with qualitative LFI analysis. The reader used for the quantitative analysis of LFI should ensure mobility for easy on-site diagnostics and inspections, be user-friendly in operation, and have a fast processing speed until the results are obtained. Due to these requirements, smartphones are increasingly utilized as readers in quantitative analysis of LFI. Among the various components constituting a smartphone, high-performance cameras can serve as sensors converting visual signals into electrical signals. With powerful processing units, large storage capacity, and network capabilities for transmitting analysis results, smartphones are also utilized as interfaces for quantitative analysis. Absolutely, the widespread global use of smartphones is a key advantage, leading to their utilization as diagnostic devices for acquiring, analyzing, storing, and transmitting assay test results. This paper summarizes research cases where smartphones are utilized as readers for quantitative LFI systems used in confirming contamination in food or the environment, detecting drugs, and diagnosing diseases in humans or animals. The systems are classified based on the types of label particles used in the assay, and efforts to improve the quantitative analysis performance for each are examined. Cases where smartphones were used as LFI readers for the diagnosis of the 2019 Coronavirus Disease (COVID-19), which has recently caused significant global damage, have also been investigated.

Development of a Paper-based Hematocrit Test and a Lateral Flow Assay to Detect Critical Fibrinogen Concentrations Using a Bottom-Up Pyramid Workflow Approach

Fibrinogen is a coagulation factor in human blood and the first one to reach critical levels in major bleeding. Hypofibrinogenemia (a too low fibrinogen concentration in blood) poses great challenges to first responders, clinicians, and healthcare providers since it represents a risk factor for exsanguination and massive transfusion requirements. Thus, the rapid assessment of the fibrinogen concentration at the point of care has gained considerable importance in preventing and managing major blood loss. However, in whole blood measurements, hematocrit variations affect the amount (volume fraction) of plasma that passes the detection zone. In an attempt to accurately determine realistic critical levels of fibrinogen (<1.5 mg/mL) in patients needing immediate treatment and medical interventions, we have developed novel diagnostic systems capable of estimating hematocrit and critical fibrinogen concentrations. A lateral flow assay (LFA) for the detection of fibrinogen has been developed by establishing a workflow employing rapid characterization methods to streamline LFA development. The integration of two detection lines enables (i) the identification of fibrinogen (first line) present in the sample and (ii) the determination of the clinically critical fibrinogen concentrations below 1.5 mg/mL (second line). Furthermore, the paper-based separation of blood cells from plasma provides a semiquantitative estimate of the hematocrit by analyzing the fractions. Initial validation of the point-of-care (PoC) hematocrit test revealed good comparability to a standard laboratory method. The developed diagnostic systems have the ability to accelerate decision-making in cases with major bleeding.

Current trends in digital camera-based bioassays for point-of-care tests

Point-of-care and bedside tests are analytical devices suitable for a growing role in the current healthcare system and provide the opportunity to achieve an exact diagnosis by an untrained person and in various conditions and sites where it is necessary. Using a digital camera integrated into a well-accessible device like a smartphone brings a new way in which a colorimetric point-of-care diagnostic test can provide unbiased data. This review summarizes basic facts about the colorimetric point-of-care tests, principles of how to use a portable device with a camera in the assay, applications of digital cameras for the current tests, and new devices described in the recent papers. An overview of the recent literature and a discussion of recent developments and future trends are provided.

Music Box-Inspired Semi-Automatic Hematocrit Validation Device

A high hematocrit (HCT) level is strongly associated with the risk of cardiovascular disease. For early diagnosis of cardiovascular disease, it is vital to regularly measure the HCT, which is typically achieved by centrifuging a blood sample to measure the percentage of red blood cells. However, the centrifugal modalities are usually bulky, expensive, and require a stable electric input, which restrict the availability. This research develops a semi-automatic and portable centrifugal device for HCT measurement. This torque-actuated semi-automatic centrifuge, which we call the tFuge, is inspired by a music box, allowing different operators to generate the same rhythm. It is electricity-free and can be controlled based on a constant torque mechanism. Repeatable test results can be received from among different users regardless of their age, sex, and activity. With the assistance of the Boycott effect on the tFuge, we proved that the HCT level is in high linearity to the length of the sedimentation of the blood cells in a tube (R2 = 0.99, sample HCT range 10-60%). The tFuge takes less than 4 min and requires no more than 10 μL of blood that can be obtained by a less-invasive finger prick to complete the testing procedure. Calibrated gradient numbers are printed onto the rotation disc for instant HCT results that can be read by the naked eye. We expect this proposed point-of-care testing device possesses the potential to replace the microhematocrit centrifuge in the regions with limited resources.

Measurement of Blood Dilution during Lancet-Free Blood Sampling

In this paper, we report on a fluorescent and colorimetric system for measuring the dilution of capillary blood released by a needle-free jet injector. Jet injection uses a high-speed liquid jet to penetrate tissue, and in the process can release capillary blood that can be collected for performing blood tests. In this way, blood sampling can be performed without the use of a lancet. However, any injectate that mixes with the collected blood dilutes the sample and may significantly impact subsequent analyses. By adding the fluorescent marker indocyanine green to the injected liquid, the fraction of injectate mixed into the collected blood can be measured. The incorporation of colorimetry allows our system to also correct for the impact of hematocrit on fluorescence. The results from this system show that it can determine the dilution of blood that has been diluted by up to 10 %, the upper limit of dilution typically observed in lancet-free blood sampling via jet injection.Clinical Relevance- Blood samples can be collected by jet injection without significant dilution, avoiding the need for lancing.

Advances in paper based isothermal nucleic acid amplification tests for water-related infectious diseases

Water-associated or water-related infectious disease outbreaks are caused by pathogens such as bacteria, viruses, and protozoa, which can be transmitted through contaminated water sources, poor sanitation practices, or insect vectors. Low- and middle-income countries bear the major burden of these infections due to inadequate hygiene and subpar laboratory facilities, making it challenging to monitor and detect infections in a timely manner. However, even developed countries are not immune to these diseases, as inadequate wastewater management and contaminated drinking water supplies can also contribute to disease outbreaks. Nucleic acid amplification tests have proven to be effective for early disease intervention and surveillance of both new and existing diseases. In recent years, paper-based diagnostic devices have made significant progress and become an essential tool in detecting and managing water-associated diseases. In this review, we highlight the importance of paper and its variants as a diagnostic tool and discuss the properties, design modifications, and various paper-based device formats developed and used for detecting water-associated pathogens.

Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay

Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health. However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities. Here, we present deep learning-assisted smartphone-based LFA (SMART^AI-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500). We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones. Furthermore, with more low-titer tests, we observed that the accuracy of SMART^AI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMART^AI-LFA. We envision a smartphone-based SMART^AI-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.

Last Trends in Point-of-Care (POC) Diagnostics for the Management of Hematological Indices in Home Care Patients

Today, complete blood count (CBC) analyses are highly automated and allow for high throughput and accurate and reliable results. However, new analytical tools are in great demand to provide simple, rapid and cost-effective management of hematological indices in home care patients. Chronic disease monitoring at home has become a benefit for patients who are finding cost savings in programs designed to monitor/treat patients in offsite locations. This review reports the latest trends in point-of-care (POC) diagnostics useful for home testing of key hematological counts that may be affected during home therapy treatment.

Microfluidic techniques for mechanical measurements of biological samples

The use of microfluidics to make mechanical property measurements is increasingly common. Fabrication of microfluidic devices has enabled various types of flow control and sensor integration at micrometer length scales to interrogate biological materials. For rheological measurements of biofluids, the small length scales are well suited to reach high rates, and measurements can be made on droplet-sized samples. The control of flow fields, constrictions, and external fields can be used in microfluidics to make mechanical measurements of individual bioparticle properties, often at high sampling rates for high-throughput measurements. Microfluidics also enables the measurement of bio-surfaces, such as the elasticity and permeability properties of layers of cells cultured in microfluidic devices. Recent progress on these topics is reviewed, and future directions are discussed.

Simultaneous quantitative detection of hematocrit and hemoglobin from whole blood using a multiplexed paper sensor with a smartphone interface

We report a highly accurate single-step label-free testing technology for simultaneous and independent hematocrit (Hct) and hemoglobin (Hb) level detection from a drop of whole blood by employing a disposable paper strip sensor interfaced with a portable impedimetric device. The paper strip is fabricated by in situ automated printing of a customized electrode template on the non-glossy side of a commercially available photo paper substrate followed by graphite deposition. The integrated platform device technology additionally includes a compact detection cum readout unit comprising a high precision impedance converter system that combines an on-board frequency generator with an analog-to-digital converter evaluation board, collectively interfaced with a central processor, calibration circuit, and smartphone. Employing a dispensed blood sample volume of 25 μL, the device is shown to have a sensitivity of 92 Ω/Hct and 287 Ω/Hb at an optimal frequency of 57 kHz. The respective linear response regimes appear to be wide enough to cover physiologically relevant limits, with excellent stability and reproducibility. Validation with clinical samples reveals limits of detection of Hct and Hb levels as low as 4.66% and 1.89 g dL^-1, respectively, which are beyond the quantitative capability of commonly used affordable point of care test kits. The envisaged paradigm of rapid, robust, highly accurate, energy-efficient, simple, user-friendly, multiplex portable detection, obviating any possible ambiguities in interpretation due to common artefacts of colorimetric detection technologies such as optical interference with the image analytical procedure due to the inherent redness of blood samples and background illumination, renders this ideal for deployment in resource-limited settings.

Smartphone-Integrated Label-Free Rapid Screening of Anemia from the Pattern Formed by One Drop of Blood on a Wet Paper Strip

Screening of anemic patients poses demanding challenges in extreme point-of-care settings where the gold standard diagnostic technologies are not pragmatic and the alternative point-of-care technologies suffer from compromised accuracy, prohibitive cost, process complexity, or reagent stability issues. As a disruption to this paradigm, here, we report the development of a smartphone-based sensor for rapid screening of anemic patients by exploiting the patterns formed by a spreading drop of blood on a wet paper strip wherein blood attempts to displace a more viscous fluid, on the porous matrix of a paper, leading to "finger-like" projections at the interface. We analyze the topological features of the pattern via smartphone-enabled image analytics and map the same with the relative occupancy of the red blood cells in the blood sample, allowing for label-free screening and classification of blood samples corresponding to moderate to severe anemic conditions. The accuracy of detection is verified by comparing with gold standard reports of hematology analyzer, showing a strong correlation coefficient (R²) of 0.975. This technique is likely to provide a crucial decision-making tool that obviates delicate reagents and skilled technicians for supreme functionality in resource-limited settings.

Recent Advances and Applications in Paper-Based Devices for Point-of-Care Testing

Point-of-care testing (POCT), as a portable and user-friendly technology, can obtain accurate test results immediately at the sampling point. Nowadays, microfluidic paper-based analysis devices (μPads) have attracted the eye of the public and accelerated the development of POCT. A variety of detection methods are combined with μPads to realize precise, rapid and sensitive POCT. This article mainly introduced the development of electrochemistry and optical detection methods on μPads for POCT and their applications on disease analysis, environmental monitoring and food control in the past 5 years. Finally, the challenges and future development prospects of μPads for POCT were discussed.

An Overview for the Nanoparticles-Based Quantitative Lateral Flow Assay

The development of the lateral flow assay (LFA) has received much attention in both academia and industry because of their broad applications to food safety, environmental monitoring, clinical diagnosis, and so forth. The user friendliness, low cost, and easy operation are the most attractive advantages of the LFA. In recent years, quantitative detection has become another focus of LFA development. Here, the most recent studies of quantitative LFAs are reviewed. First, the principles and corresponding formats of quantitative LFAs are introduced. In the biomaterial and nanomaterial sections, the detection, capture, and signal amplification biomolecules and the optical, fluorescent, luminescent, and magnetic labels used in LFAs are described. The invention of dedicated strip readers has drawn further interest in exploiting the better performance of LFAs. Therefore, next, the development of dedicated reader devices is described and the usefulness and specifications of these devices for LFAs are discussed. Finally, the applications of LFAs in the detection of metal ions, biotoxins, pathogenic microorganisms, veterinary drugs, and pesticides in the fields of food safety and environmental health and the detection of nucleic acids, biomarkers, and viruses in clinical analyses are summarized.

High-Performance Passive Plasma Separation on OSTE Pillar Forest

Plasma separation is of high interest for lateral flow tests using whole blood as sample liquids. Here, we built a passive microfluidic device for plasma separation with high performance. This device was made by blood filtration membrane and off-stoichiometry thiol-ene (OSTE) pillar forest. OSTE pillar forest was fabricated by double replica moldings of a laser-cut polymethylmethacrylate (PMMA) mold, which has a uniform microstructure. This device utilized a filtration membrane to separate plasma from whole blood samples and used hydrophilic OSTE pillar forest as the capillary pump to propel the plasma. The device can be used to separate blood plasma with high purity for later use in lateral flow tests. The device can process 45 μL of whole blood in 72 s and achieves a plasma separation yield as high as 60.0%. The protein recovery rate of separated plasma is 85.5%, which is on par with state-of-the-art technologies. This device can be further developed into lateral flow tests for biomarker detection in whole blood.

Point-of-Care Diagnoses and Assays Based on Lateral Flow Test

Analytical devices for point-of-care diagnoses are highly desired and would improve quality of life when first diagnoses are made early and pathologies are recognized soon. Lateral flow tests (LFTs) are such tools that can be easily performed without specific equipment, skills, or experiences. This review is focused on the use of LFT in point-of-care diagnoses. The principle of the assay is explained, and new materials like nanoparticles for labeling, new recognition molecules for interaction with an analyte, and new additional instrumentation like signal scaling by a smartphone camera are described and discussed. Advantages of the LFT devices as well as their limitations are described and discussed here considering actual papers that are properly cited.

Touchable cell biophysics property recognition platforms enable multifunctional blood smart health care

As a crucial biophysical property, red blood cell (RBC) deformability is pathologically altered in numerous disease states, and biochemical and structural changes occur over time in stored samples of otherwise normal RBCs. However, there is still a gap in applying it further to point-of-care blood devices due to the large external equipment (high-resolution microscope and microfluidic pump), associated operational difficulties, and professional analysis. Herein, we revolutionarily propose a smart optofluidic system to provide a differential diagnosis for blood testing via precise cell biophysics property recognition both mechanically and morphologically. Deformation of the RBC population is caused by pressing the hydrogel via an integrated mechanical transfer device. The biophysical properties of the cell population are obtained by the designed smartphone algorithm. Artificial intelligence-based modeling of cell biophysics properties related to blood diseases and quality was developed for online testing. We currently achieve 100% diagnostic accuracy for five typical clinical blood diseases (90 megaloblastic anemia, 78 myelofibrosis, 84 iron deficiency anemia, 48 thrombotic thrombocytopenic purpura, and 48 thalassemias) via real-world prospective implementation; furthermore, personalized blood quality (for transfusion in cardiac surgery) monitoring is achieved with an accuracy of 96.9%. This work suggests a potential basis for next-generation blood smart health care devices.