A portable spinning disc for complete blood count (CBC)

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.

An integrated centrifugal microfluidic strategy for point-of-care complete blood counting

Centrifugal microfluidics holds the potential to revolutionize point-of-care (POC) testing by simplifying laboratory tests through automating fluid and cell manipulation within microfluidic channels. This technology can facilitate blood testing, the most frequent clinical test, at the POC. However, an integrated centrifugal microfluidic device for complete blood counting (CBC) has not yet been fully realized. To address this, we propose an integrated portable system comprising a centrifuge and a hybrid microfluidic disc specifically designed for CBC analysis at the POC. The disc enables the implementation of various spin profiles in different stages of CBC to facilitate in-situ cell separation, solution metering and mixing, and differential cell counting. Furthermore, our system is coupled with a custom script that automates the process and ensures precise quantification of cells using light and fluorescent images captured from the detection chamber of the disc. We demonstrate a close correlation between the proposed method and the hematology analyzer, considered the gold standard, for quantifying hematocrit (R2 = 0.99), white blood cell count (R2 = 0.98), white blood cell differential count (granulocyte/agranulocyte; R2 = 0.89), red blood cell count (R2 = 0.97), and mean corpuscular volume (R2 = 0.94). The integration of our portable system offers significant advantages, enabling more accessible and affordable CBC testing at the POC. Considering the simplicity, affordability (∼$250 capital cost and <$2 operational cost per test), as well as low power consumption (>100 tests using a typical 24 V/10 Ah battery), this system has the potential to enhance healthcare delivery, particularly in resource-limited settings and remote areas where access to traditional laboratory facilities is limited.

Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features

White blood cells (WBCs) are one of the main components of blood produced by the bone marrow. WBCs are part of the immune system that protects the body from infectious diseases and an increase or decrease in the amount of any type that causes a particular disease. Thus, recognizing the WBC types is essential for diagnosing the patient's health and identifying the disease. Analyzing blood samples to determine the amount and WBC types requires experienced doctors. Artificial intelligence techniques were applied to analyze blood samples and classify their types to help doctors distinguish between types of infectious diseases due to increased or decreased WBC amounts. This study developed strategies for analyzing blood slide images to classify WBC types. The first strategy is to classify WBC types by the SVM-CNN technique. The second strategy for classifying WBC types is by SVM based on hybrid CNN features, which are called VGG19-ResNet101-SVM, ResNet101-MobileNet-SVM, and VGG19-ResNet101-MobileNet-SVM techniques. The third strategy for classifying WBC types by FFNN is based on a hybrid model of CNN and handcrafted features. With MobileNet and handcrafted features, FFNN achieved an AUC of 99.43%, accuracy of 99.80%, precision of 99.75%, specificity of 99.75%, and sensitivity of 99.68%.

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.

A smartphone-integrated portable rotating platform for estimation of concentration level of plasma-creatinine using whole human blood

The measurement of creatinine concentration is performed to monitor the renal health. The devices available in modern clinical laboratories for measuring creatinine concentration are accurate and provide results rapidly but may be prohibitively expensive for resource-poor settings. Therefore, developing an inexpensive yet accurate device for measuring creatinine concentration is needed. Consequently, we developed a simple, affordable, and portable spinning disc for measuring plasma-creatinine concentration with 10 μL of whole human blood. 5 μL of the alkaline picrate solution is loaded into the device and rotated at 1000 rpm to transport this solution to the periphery of the microchannel. Further, 10 μL whole blood is loaded in the same channel and spun at 1300 rpm for 10 min. The creatinine in plasma reacts with alkaline picrate (Jaffe reaction), and the color of the mixture changes to yellow-orange color. The resulting color is captured with a smartphone, and creatinine concentration is estimated using an in-house developed app (CREA-SESE). The value of creatinine measured with the present device and the gold standard device are highly correlated (R² = 0.998). The bias and standard deviation of the difference between the two measurements are 0.134 mg/dL and 0.143 mg/dL. This study demonstrates the feasibility of a simple, inexpensive, and portable rotating device for measuring creatinine concentration using 10 μL of whole human blood, which can easily be deployed to the underserved population in resource-constrained settings to monitor renal diseases.

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.

Microfluidic Systems for Blood and Blood Cell Characterization

A laboratory blood test is vital for assessing a patient's health and disease status. Advances in microfluidic technology have opened the door for on-chip blood analysis. Currently, microfluidic devices can reproduce myriad routine laboratory blood tests. Considerable progress has been made in microfluidic cytometry, blood cell separation, and characterization. Along with the usual clinical parameters, microfluidics makes it possible to determine the physical properties of blood and blood cells. We review recent advances in microfluidic systems for measuring the physical properties and biophysical characteristics of blood and blood cells. Added emphasis is placed on multifunctional platforms that combine several microfluidic technologies for effective cell characterization. The combination of hydrodynamic, optical, electromagnetic, and/or acoustic methods in a microfluidic device facilitates the precise determination of various physical properties of blood and blood cells. We analyzed the physical quantities that are measured by microfluidic devices and the parameters that are determined through these measurements. We discuss unexplored problems and present our perspectives on the long-term challenges and trends associated with the application of microfluidics in clinical laboratories. We expect the characterization of the physical properties of blood and blood cells in a microfluidic environment to be considered a standard blood test in the future.

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.

Use of some cost-effective technologies for a routine clinical pathology laboratory

Classically, the need for highly sophisticated instruments with important economic costs has been a major limiting factor for clinical pathology laboratories, especially in developing countries. With the aim of making clinical pathology more accessible, a wide variety of free or economical technologies have been developed worldwide in the last few years. 3D printing and Arduino approaches can provide up to 94% economical savings in hardware and instrumentation in comparison to commercial alternatives. The vast selection of point-of-care-tests (POCT) currently available also limits the need for specific instruments or personnel, as they can be used almost anywhere and by anyone. Lastly, there are dozens of free and libre digital tools available in health informatics. This review provides an overview of the state-of-the-art on cost-effective alternatives with applications in routine clinical pathology laboratories. In this context, a variety of technologies including 3D printing and Arduino, lateral flow assays, plasmonic biosensors, and microfluidics, as well as laboratory information systems, are discussed. This review aims to serve as an introduction to different technologies that can make clinical pathology more accessible and, therefore, contribute to achieve universal health coverage.

Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization

White blood cells, WBCs for short, are an essential component of the human immune system. These cells are our body's first line of defense against infections and diseases caused by bacteria, viruses, and fungi, as well as abnormal and external substances that may enter the bloodstream. A wrong WBC count can signify dangerous viral infections, autoimmune disorders, cancer, sarcoidosis, aplastic anemia, leukemia, tuberculosis, etc. A lot of these diseases and disorders can be extremely painful and often result in death. Leukemia is among the more common types of blood cancer and when left undetected leads to death. An early diagnosis is necessary which is possible by looking at the shapes and determining the numbers of young and immature WBCs to see if they are normal or not. Performing this task manually is a cumbersome, expensive, and time-consuming process for hematologists, and therefore computer-aided systems have been developed to help with this problem. This paper proposes an improved method of classification of WBCs utilizing a combination of preprocessing, convolutional neural networks (CNNs), feature selection algorithms, and classifiers. In preprocessing, contrast-limited adaptive histogram equalization (CLAHE) is applied to the input images. A CNN is designed and trained to be used for feature extraction along with ResNet50 and EfficientNetB0 networks. Ant colony optimization is used to select the best features which are then serially fused and passed onto classifiers such as support vector machine (SVM) and quadratic discriminant analysis (QDA) for classification. The classification accuracy achieved on the Blood Cell Images dataset is 98.44%, which shows the robustness of the proposed work.

Red blood cell recognition and posture estimation in microfluidic chip based on lensless imaging

On the one hand, lensless imaging technology has become one of the key technologies to achieve point-of-care testing; on the other hand, microfluidic technology has shown great application potential in the field of biological detection. Using mainstream lensless imaging technology to achieve biological cell imaging in microfluidic chips has technical limitations. In particular, it is more difficult to achieve lensless imaging for non-spherical cells in microfluidic chips such as red blood cells. Achieving red blood cell recognition and posture estimation in a microfluidic chip under the lensless imaging, combined with mainstream lensless imaging technology, can provide more effective red blood cell morphological parameters for medical diagnosis. In this paper, the method for red blood cell recognition and posture estimation in microfluidic chips based on lensless imaging is given. First, the relevant theoretical basis is introduced. Then, the models of red blood cell recognition and posture estimation in microfluidic chips based on lensless imaging are given. The effect of red blood cell flipping on lensless imaging is analyzed in the modeling process. Finally, the effectiveness of the proposed method is verified by experiments. Experiments show that the proposed method can well achieve red blood cell recognition and posture estimation through the shape characteristics of red blood cells.

Smartphone-Enabled Paper-Based Hemoglobin Sensor for Extreme Point-of-Care Diagnostics

We report a simple, affordable (∼0.02 US $/test), rapid (within 5 min), and quantitative paper-based sensor integrated with smartphone application for on-spot detection of hemoglobin (Hgb) concentration using approximately 10 μL of finger-pricked blood. Quantitative analytical colorimetry is achieved via an Android-based application (Sens-Hb), integrating key operational steps of image acquisition, real-time analysis, and result dissemination. Further, feedback from the machine learning algorithm for adaptation of calibration data offers consistent dynamic improvement for precise predictions of the test results. Our study reveals a successful deployment of the extreme point-of-care test in rural settings where no infrastructural facilities for diagnostics are available. The Hgb test device is validated both in the controlled laboratory environment (n = 200) and on the field experiments (n = 142) executed in four different Indian villages. Validation results are well correlated with the pathological gold standard results (r = 0.9583) with high sensitivity and specificity for the healthy (n = 136) (>11 g/dL) (specificity: 97.2%), mildly anemic (n = 55) (<11 g/dL) (sensitivity: 87.5%, specificity: 100%), and severely anemic (n = 9) (<7 g/dL) (sensitivity: 100%, specificity: 100%) samples. Results from field trials reveal that only below 5% cases of the results are interpreted erroneously by classifying mildly anemic patients as healthy ones. On-field deployment has unveiled the test kit to be extremely user friendly that can be handled by minimally trained frontline workers for catering the needs of the underserved communities.

Biosensors Based on Mechanical and Electrical Detection Techniques

Biosensors are powerful analytical tools for biology and biomedicine, with applications ranging from drug discovery to medical diagnostics, food safety, and agricultural and environmental monitoring. Typically, biological recognition receptors, such as enzymes, antibodies, and nucleic acids, are immobilized on a surface, and used to interact with one or more specific analytes to produce a physical or chemical change, which can be captured and converted to an optical or electrical signal by a transducer. However, many existing biosensing methods rely on chemical, electrochemical and optical methods of identification and detection of specific targets, and are often: complex, expensive, time consuming, suffer from a lack of portability, or may require centralised testing by qualified personnel. Given the general dependence of most optical and electrochemical techniques on labelling molecules, this review will instead focus on mechanical and electrical detection techniques that can provide information on a broad range of species without the requirement of labelling. These techniques are often able to provide data in real time, with good temporal sensitivity. This review will cover the advances in the development of mechanical and electrical biosensors, highlighting the challenges and opportunities therein.