On-chip flow cytometer using integrated photonics for the detection of human leukocytes.

Differentiation between leukocyte subtypes like monocytes and lymphocytes is essential for cell therapy and research applications. To guarantee the cost-effective delivery of functional cells in cell therapies, billions of cells must be processed in a limited time. Yet, the sorting rates of commercial cell sorters are not high enough to reach the required yield. Process parallelization by using multiple instruments increases variability and production cost. A compact solution with higher throughput can be provided by multichannel flow cytometers combining fluidics and optics on-chip. In this work, we present a micro-flow cytometer with monolithically integrated photonics and fluidics and demonstrate that both the illumination of cells, as well as the collection of scattered light, can be realized using photonic integrated circuits. Our device is the first with sufficient resolution for the discrimination of lymphocytes and monocytes. Innovations in microfabrication have enabled complete integration of miniaturized photonic components and fluidics in a CMOS-compatible wafer stack. In combination with external optics, the device is ready for the collection of fluorescence using the on-chip excitation.

Micropump integrated white blood cell separation platform for detection of chronic granulomatous disease.

White blood cells (WBCs) are robust defenders during antigenic challenges and prime immune cell functioning indicators. High-purity WBC separation is vital for various clinical assays and disease diagnosis. Red blood cells (RBCs) are a major hindrance in WBC separation, constituting 1000 times the WBC population. The study showcases a low-cost micropump integrated microfluidic platform to provide highly purified WBCs for point-of-care testing. An integrated user-friendly microfluidic platform was designed to separate WBCs from finger-prick blood (⁓5 µL), employing an inertial focusing technique. We achieved an efficient WBC separation with 86% WBC purity and 99.99% RBC removal rate in less than 1 min. In addition, the microdevice allows lab-on-chip colorimetric evaluation of chronic granulomatous disease (CGD), a rare genetic disorder affecting globally. The assay duration, straight from separation to disease detection, requires only 20 min. Hence, the proposed microfluidic platform can further be implemented to streamline various clinical procedures involving WBCs in healthcare industries.

Leukocytes within Autologous Blood Concentrates Have No Impact on the Growth and Proliferation of Human Primary Osteoblasts: An In Vitro Study.

Platelet-rich fibrin (PRF) is a widely used autologous blood concentrate in regenerative medicine. This study aimed to characterize the cellular composition and distribution of different PRF matrices generated by high (710 g) and low (44 g) relative centrifugal forces (RCFs) and to analyze their bioactivity on human primary osteoblasts (pOBs). PRF was separated into upper layer (UL) and buffy coat (BC) fractions, and their cellular contents were assessed using histological and immunohistochemical staining. The release of platelet-derived growth factor (PDGF) and transforming growth factor (TGF-ß) was quantified using an ELISA. Indirect PRF treatment on pOBs was performed to evaluate cell viability and morphology. A histological analysis revealed higher quantities of leukocytes and platelets in the low-RCF PRF. TGF-ß release was significantly higher in the low-RCF PRF compared to the high-RCF PRF. All PRF fractions promoted pOB proliferation regardless of the centrifugation protocol used. The low-RCF PRF showed higher TGF-ß levels than the high-RCF PRF. These findings contribute to understanding the cellular mechanisms of PRF and provide insights into optimizing PRF protocols for bone regeneration, advancing regenerative medicine, and improving patient outcomes.

Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.

Highly Sensitive and Linear Resonator-Based Biosensor for White Blood Cell Counting: Feasible Measurement Method and Intrinsic Mechanism Exploration.

Since different quantities of white blood cells (WBCs) in solution possess an adaptive osmotic pressure of cells, the WBCs themselves and in solution have similar concentrations, resulting in them having similar dielectric properties. Therefore, a microwave sensor could have difficulty in sensing the quantity variation when WBCs are in solution. This paper presents a highly sensitive, linear permittivity-inspired microwave biosensor for WBCs, counting through the evaporation method. Such a measurement method is proposed to record measurements after the cell solution is dripped onto the chip and is completely evaporated naturally. The proposed biosensor consists of an air-bridged asymmetric differential inductor and a centrally located circular fork-finger capacitor fabricated on a GaAs substrate using integrated passive fabrication technology. It is optimized to feature a larger sensitive area and improved Q-factor, which increases the effective area of interaction between cells and the electromagnetic field and facilitates the detection of their changes in number. The sensing relies on the dielectric properties of the cells and the change in the dielectric constant for different concentrations, and the change in resonance properties, which mainly represents the frequency shift, corresponds to the macroscopic change in the concentration of the cells. The microwave biosensors are used to measure biological samples with concentrations ranging from 0.25 × 106 to 8 × 106 cells per mL in a temperature (26.00 ± 0.40 °C) and humidity (54.40 ± 3.90 RH%) environment. The measurement results show a high sensitivity of 25.06 Hz/cells·mL-1 with a highly linear response of r2 = 0.99748. In addition, a mathematical modeling of individual cells in suspension is performed to estimate the dielectric constant of individual cells and further explain the working mechanism of the proposed microwave biosensor.

Portable platform for leukocyte extraction from blood using sheath-free microfluidic DLD.

Leukocyte count is routinely performed for diagnostic purposes and is rapidly emerging as a significant biomarker for a wide array of diseases. Additionally, leukocytes have demonstrated considerable promise in novel cell-based immunotherapies. However, the direct retrieval of leukocytes from whole blood is a significant challenge due to their low abundance compared to erythrocytes. Here, we introduce a microfluidic-based platform that isolates and recovers leukocytes from diluted whole blood in a single step. Our platform utilizes a novel, sheathless method to initially sediment and focus blood cells into a dense stream while flowing through a tubing before entering the microfluidic device. A hexagonal-shaped structure, patterned at the device's inlet, directs all the blood cells against the channel's outer walls. The focused cells are then separated based on their size using the deterministic lateral displacement (DLD) microfluidic technique. We evaluated various parameters that could influence leukocyte separation, including different focusing structures (assessed both computationally and experimentally), the orientation of the tubing-chip interface, the effects of blood sample hematocrit (dilution), and flow rate. Our device demonstrated the ability to isolate leukocytes from diluted blood with a separation efficiency of 100%, a recovery rate of 76%, and a purity of 80%, while maintaining a cell viability of 98%. The device operates for over 30 min at a flow rate of 2 µL min-1. Furthermore, we developed a handheld pressure controller to drive fluid flow, enhancing the operability of our platform outside of central laboratories and enabling near-patient testing. Our platform can be integrated with downstream cell-based assays and analytical methods that require high leukocyte purity (80%), ranging from cell counting to diagnostics and cell culture applications.

The dilution evaluation as a corrective measure for interference in the white blood cell scattergram in Beckman Coulter DxH 900.

BACKGROUND: The Beckman Coulter DxH 900 is a haematological analyser capable of counting and sizing blood cells, and obtaining a complete blood cell count (CBC). This analyses different parameters of red blood cells (RBC), platelets and white blood cells/leukocytes. Some automated CBC counters present limitations due to specimen characteristics, abnormal cells or both factors. In the presence of abnormalities, the DxH 900 has a flagging system, warning the laboratory technician that something needs to be verified. In the present work, we evaluated samples from oncologic patients, presenting a population erroneously perceived as being lymphocytes. The most common explanations for this situation are RBC resistant to lysis or serum hyperbilirubinaemia. METHODS: In an attempt to solve and understand what the cause of this problem might be, we diluted our samples (1:3) and analysed the serum total bilirubin. To identify cells' abnormalities, the samples were also analysed by manual DLC counts. During the study, we also checked the different flags presented by the equipment. RESULTS: The results evidenced that the major interference was due to RBC lysis resistance, corresponding to 94.7% of the cases, while hyperbilirubinaemia was only present in 73.4%. Besides, we determined that some samples with normal bilirubin levels also presented interference, suggesting that hyperbilirubinaemia was not the main cause of the error. The most recurrent flag observed was "High event rate". CONCLUSION: The dilution solved all of the observed interferences. The results between diluted and manual counts showed a strong correlation, leading us to introduce dilution in our laboratory routine.

Performance comparison of two automated digital morphology analyzers for leukocyte differential in patients with malignant hematological diseases: Mindray MC-80 and Sysmex DI-60.

BACKGROUND: The MC-80 (Mindray, Shenzhen, China), a newly available artificial intelligence (AI)-based digital morphology analyzer, is the focus of this study. We aim to compare the leukocyte differential performance of the Mindray MC-80 with that of the Sysmex DI-60 and the gold standard, manual microscopy. METHODS: A total of 100 abnormal peripheral blood (PB) smears were compared across the MC-80, DI-60, and manual microscopy. Sensitivity, specificity, predictive value, and efficiency were calculated according to the Clinical and Laboratory Standards Institute (CLSI) EP12-A2 guidelines. Comparisons were made using Bland-Altman analysis and Passing-Bablok regression analysis. Additionally, within-run imprecision was evaluated using five samples, each with varying percentages of mature leukocytes and blasts, in accordance with CLSI EP05-A3 guidelines. RESULTS: The within-run coefficient of variation (%CV) of the MC-80 for most cell classes in the five samples was lower than that of the DI-60. Sensitivities for the MC-80 ranged from 98.2% for nucleated red blood cells (NRBC) to 28.6% for reactive lymphocytes. The DI-60's sensitivities varied between 100% for basophils and reactive lymphocytes, and 11.1% for metamyelocytes. Both analyzers demonstrated high specificity, negative predictive value, and efficiency, with over 90% for most cell classes. However, the DI-60 showed relatively lower specificity for lymphocytes (73.2%) and lower efficiency for blasts and lymphocytes (80.1% and 78.6%, respectively) compared with the MC-80. Bland-Altman analysis indicated that the absolute mean differences (%) ranged from 0.01 to 4.57 in MC-80 versus manual differential and 0.01 to 3.39 in DI-60 versus manual differential. After verification by technicians, both analyzers exhibited a very high correlation (r = 0.90-1.00) with the manual differential results in neutrophils, lymphocytes, and blasts. CONCLUSIONS: The Mindray MC-80 demonstrated good performance for leukocyte differential in PB smears, notably exhibiting higher sensitivity for blasts identification than the DI-60.

Leukocyte differential based on an imaging and impedance flow cytometry of microfluidics coupled with deep neural networks.

The differential of leukocytes functions as the first indicator in clinical examinations. However, microscopic examinations suffered from key limitations of low throughputs in classifying leukocytes while commercially available hematology analyzers failed to provide quantitative accuracies in leukocyte differentials. A home-developed imaging and impedance flow cytometry of microfluidics was used to capture fluorescent images and impedance variations of single cells traveling through constrictional microchannels. Convolutional and recurrent neural networks were adopted for data processing and feature extractions, which were then fused by a support vector machine to realize the four-part differential of leukocytes. The classification accuracies of the four-part leukocyte differential were quantified as 95.4% based on fluorescent images plus the convolutional neural network, 90.3% based on impedance variations plus the recurrent neural network, and 99.3% on the basis of fluorescent images, impedance variations, and deep neural networks. Based on single-cell fluorescent imaging and impedance variations coupled with deep neural networks, the four-part leukocyte differential can be realized with almost 100% accuracy.

A high-resolution large-scale dataset of pathological and normal white blood cells.

Microscopic examination plays a significant role in the initial screening for a variety of hematological, as well as non-hematological, diagnoses. Microscopic blood smear examination that is considered a key diagnostic technique, is in recent clinical practice still performed manually, which is not only time consuming, but can lead to human errors. Although automated and semi-automated systems have been developed in recent years, their high purchasing and maintenance costs make them unaffordable for many medical institutions. Even though much research has been conducted lately to explore more accurate and feasible solutions, most researchers had to deal with a lack of medical data. To address the lack of large-scale databases in this field, we created a high-resolution dataset containing a total of 16027 annotated white blood cells. Moreover, the dataset covers overall 9 types of white blood cells, including clinically significant pathological findings. Since we used high-quality acquisition equipment, the dataset provides one of the highest quality images of blood cells, achieving an approximate resolution of 42 pixels per 1 µm.

A two-step search and run response to gradients shapes leukocyte navigation in vivo.

Migrating cells must interpret chemical gradients to guide themselves within tissues. A long-held principle is that gradients guide cells via reorientation of leading-edge protrusions. However, recent evidence indicates that protrusions can be dispensable for locomotion in some contexts, raising questions about how cells interpret endogenous gradients in vivo and whether other mechanisms are involved. Using laser wound assays in zebrafish to elicit acute endogenous gradients and quantitative analyses, we demonstrate a two-stage process for leukocyte chemotaxis in vivo: first a "search" phase, with stimulation of actin networks at the leading edge, cell deceleration, and turning. This is followed by a "run" phase, with fast actin flows, cell acceleration, and persistence. When actin dynamics are perturbed, cells fail to resolve the gradient, suggesting that pure spatial sensing of the gradient is insufficient for navigation. Our data suggest that cell contractility and actin flows provide memory for temporal sensing, while expansion of the leading edge serves to enhance gradient sampling.

Brain motor and fear circuits regulate leukocytes during acute stress.

The nervous and immune systems are intricately linked1. Although psychological stress is known to modulate immune function, mechanistic pathways linking stress networks in the brain to peripheral leukocytes remain poorly understood2. Here we show that distinct brain regions shape leukocyte distribution and function throughout the body during acute stress in mice. Using optogenetics and chemogenetics, we demonstrate that motor circuits induce rapid neutrophil mobilization from the bone marrow to peripheral tissues through skeletal-muscle-derived neutrophil-attracting chemokines. Conversely, the paraventricular hypothalamus controls monocyte and lymphocyte egress from secondary lymphoid organs and blood to the bone marrow through direct, cell-intrinsic glucocorticoid signalling. These stress-induced, counter-directional, population-wide leukocyte shifts are associated with altered disease susceptibility. On the one hand, acute stress changes innate immunity by reprogramming neutrophils and directing their recruitment to sites of injury. On the other hand, corticotropin-releasing hormone neuron-mediated leukocyte shifts protect against the acquisition of autoimmunity, but impair immunity to SARS-CoV-2 and influenza infection. Collectively, these data show that distinct brain regions differentially and rapidly tailor the leukocyte landscape during psychological stress, therefore calibrating the ability of the immune system to respond to physical threats.

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique.

The complete blood count (CBC) is one of the most important clinical steps in clinical diagnosis. The instruments used for CBC are usually expensive and bulky and require well-trained operators. Therefore, it is difficult for medical institutions below the tertiary level to provide these instruments, especially in underprivileged countries. Several reported on-chip blood cell tests are still in their infancy and do not deviate from conventional microscopic or impedance measurement methods. In this study, we (i) combined magnetically activated cell sorting and the differential density method to develop a method to selectively isolate three types of leukocytes from blood and obtain samples with high purity and concentration for portable leukocyte classification using the lens-free shadow imaging technique (LSIT), and (ii) established several shadow parameters to identify the type of leukocytes in a complete leukocyte shadow image by shadow image analysis. The purity of the separated leukocytes was confirmed by flow cytometry. Several shadow parameters such as the "order ratio" and "minimum ratio" were developed to classify the three types of leukocytes. A shadow image library corresponding to each type of leukocyte was created from the tested samples. Compared with clinical reference data, a correlation index of 0.98 was obtained with an average error of 6% and a confidence level of 95%. This technique offers great potential for biological, pharmaceutical, environmental, and clinical applications, especially where point-of-care detection of rare cells is required.

Increasing a microscope's effective field of view via overlapped imaging and machine learning.

This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.

Oxidative stress induces inflammation of lens cells and triggers immune surveillance of ocular tissues.

Recent reports have challenged the notion that the lens is immune-privileged. However, these studies have not fully identified the molecular mechanism(s) that promote immune surveillance of the lens. Using a mouse model of targeted glutathione (GSH) deficiency in ocular surface tissues, we have investigated the role of oxidative stress in upregulating cytokine expression and promoting immune surveillance of the eye. RNA-sequencing of lenses from postnatal day (P) 1-aged Gclcf/f;Le-CreTg/- (KO) and Gclcf/f;Le-Cre-/- control (CON) mice revealed upregulation of many cytokines (e.g., CCL4, GDF15, CSF1) and immune response genes in the lenses of KO mice. The eyes of KO mice had a greater number of cells in the aqueous and vitreous humors at P1, P20 and P50 than age-matched CON and Gclcw/w;Le-CreTg/- (CRE) mice. Histological analyses revealed the presence of innate immune cells (i.e., macrophages, leukocytes) in ocular structures of the KO mice. At P20, the expression of cytokines and ROS content was higher in the lenses of KO mice than in those from age-matched CRE and CON mice, suggesting that oxidative stress may induce cytokine expression. In vitro administration of the oxidant, hydrogen peroxide, and the depletion of GSH (using buthionine sulfoximine (BSO)) in 21EM15 lens epithelial cells induced cytokine expression, an effect that was prevented by co-treatment of the cells with N-acetyl-l-cysteine (NAC), a antioxidant. The in vivo and ex vivo induction of cytokine expression by oxidative stress was associated with the expression of markers of epithelial-to-mesenchymal transition (EMT), α-SMA, in lens cells. Given that EMT of lens epithelial cells causes posterior capsule opacification (PCO), we propose that oxidative stress induces cytokine expression, EMT and the development of PCO in a positive feedback loop. Collectively these data indicate that oxidative stress induces inflammation of lens cells which promotes immune surveillance of ocular structures.

Effects of Formyl Peptide Receptor Agonists Ac9-12 and WKYMV in In Vivo and In Vitro Acute Inflammatory Experimental Models.

Formyl peptide receptors (Fprs) are a G-protein-coupled receptor family mainly expressed on leukocytes. The activation of Fpr1 and Fpr2 triggers a cascade of signaling events, leading to leukocyte migration, cytokine release, and increased phagocytosis. In this study, we evaluate the effects of the Fpr1 and Fpr2 agonists Ac9-12 and WKYMV, respectively, in carrageenan-induced acute peritonitis and LPS-stimulated macrophages. Peritonitis was induced in male C57BL/6 mice through the intraperitoneal injection of 1 mL of 3% carrageenan solution or saline (control). Pre-treatments with Ac9-12 and WKYMV reduced leukocyte influx to the peritoneal cavity, particularly neutrophils and monocytes, and the release of IL-1ß. The addition of the Fpr2 antagonist WRW4 reversed only the anti-inflammatory actions of WKYMV. In vitro, the administration of Boc2 and WRW4 reversed the effects of Ac9-12 and WKYMV, respectively, in the production of IL-6 by LPS-stimulated macrophages. These biological effects of peptides were differently regulated by ERK and p38 signaling pathways. Lipidomic analysis evidenced that Ac9-12 and WKYMV altered the intracellular lipid profile of LPS-stimulated macrophages, revealing an increased concentration of several glycerophospholipids, suggesting regulation of inflammatory pathways triggered by LPS. Overall, our data indicate the therapeutic potential of Ac9-12 and WKYMV via Fpr1 or Fpr2-activation in the inflammatory response and macrophage activation.

A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm.

Accurate and early detection of anomalies in peripheral white blood cells plays a crucial role in the evaluation of well-being in individuals and the diagnosis and prognosis of hematologic diseases. For example, some blood disorders and immune system-related diseases are diagnosed by the differential count of white blood cells, which is one of the common laboratory tests. Data is one of the most important ingredients in the development and testing of many commercial and successful automatic or semi-automatic systems. To this end, this study introduces a free access dataset of normal peripheral white blood cells called Raabin-WBC containing about 40,000 images of white blood cells and color spots. For ensuring the validity of the data, a significant number of cells were labeled by two experts. Also, the ground truths of the nuclei and cytoplasm are extracted for 1145 selected cells. To provide the necessary diversity, various smears have been imaged, and two different cameras and two different microscopes were used. We did some preliminary deep learning experiments on Raabin-WBC to demonstrate how the generalization power of machine learning methods, especially deep neural networks, can be affected by the mentioned diversity. Raabin-WBC as a public data in the field of health can be used for the model development and testing in different machine learning tasks including classification, detection, segmentation, and localization.

Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT.

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.

WBC-AMNet: Automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism.

The recognition and classification of White Blood Cell (WBC) play a remarkable role in blood-related diseases (i.e., leukemia, infections) diagnosis. For the highly similar morphology of different WBC subtypes, it is too confused to classify the WBC effectively and accurately for visual observation of blood cell smears. This paper proposes a Deep Convolutional Neural Network (DCNN) with feature fusion strategies, named WBC-AMNet, for automatically classifying WBC subtypes based on focalized attention mechanism. To obtain more localized attention of CNN, the fusion features of the first and the last convolutional layer are extracted by focalized attention mechanism combining Squeeze-and-Excitation (SE) and Gather-Excite (GE) modules. The new method performs successfully in classifying monocytes, neutrophils, lymphocytes, and eosinophils on the complex background with an overall accuracy of 95.66%, better than that of general CNNs. The multi-classification accuracy of WBC-AMNet with the background segmentation is over 98% in all cases. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize the attention heatmaps of different feature maps.

The regulatory role of insulin in energy metabolism and leukocyte functions.

Insulin is the hormone responsible for maintaining glucose homeostasis in the body, in addition to participating in lipid metabolism, protein synthesis, and the inhibition of gluconeogenesis. These functions are well characterized in the classic organ target cells that are responsible for general energy regulation: the liver, skeletal muscle, and adipose tissue. However, these actions are not restricted to these tissues because insulin has been shown to affect most cells in the body. This review describes the role of insulin in leukocyte signaling pathways, metabolism and functions, and how insulin resistance could affect this signaling and deteriorate leukocyte metabolism and function, in addition to showing evidence that suggests leukocytes may substantially contribute to the development of systemic insulin resistance.