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.

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.

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.

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.

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.

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.

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.

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.

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.

Inverse Association of Telomere Length With Liver Disease and Mortality in the US Population.

Physiologic aging leads to attrition of telomeres and replicative senescence. An acceleration of this process has been hypothesized in the progression of chronic liver disease. We sought to examine the association of telomere length (TL) with liver disease and its impact on mortality risk. A cohort of 7,072 adults with leukocyte TL measurements from the National Health and Nutrition Examination Survey 1999-2002 with mortality follow-up through 2015 was analyzed. Liver disease was defined by aminotransferase levels and classified into etiology-based and advanced fibrosis categories. Multivariable-adjusted linear regression models estimated effect sizes, with 95% confidence intervals (CIs), of the presence of liver disease on TL. Cox regression models evaluated associations between TL and all-cause mortality risk using adjusted hazard ratios (HRs). The cohort was representative of the US population with mean age 46.1 years and mean TL 5.79 kilobase pairs. No overall association between TL and liver disease was found; however, there was a significant negative association of TL and advanced liver fibrosis in individuals aged 65 and above. The liver disease cohort (HR 1.22, 95% CI 0.99-1.51) and those with metabolic syndrome (HR 1.26, 95% CI 0.96-1.67) had increased mortality risk with shorter TL. The relationship between TL and all-cause mortality was stronger in women (HR 1.51, 95% CI 1.02-2.23) and in non-Hispanic Whites (HR 1.37, 95% CI 1.02-1.84). Conclusion: Shortened leukocyte TL is independently associated with advanced liver disease at older ages, and with a higher risk of all-cause mortality in those with liver disease. These associations reaffirm the need to better understand the role of telomeres in the progression of liver disease.

In silico-labeled ghost cytometry.

Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive 'imaging' data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.

PCSK9 Imperceptibly Affects Chemokine Receptor Expression In Vitro and In Vivo.

Proprotein convertase subtilin/kexin type 9 (PCSK9) is a protease secreted mainly by hepatocytes and in lesser quantities by intestines, pancreas, and vascular cells. Over the years, this protease has gained importance in the field of cardiovascular biology due to its regulatory action on the low-density lipoprotein receptor (LDLR). However, recently, it has also been shown that PCSK9 acts independent of LDLR to cause vascular inflammation and increase the severity of several cardiovascular disorders. We hypothesized that PCSK9 affects the expression of chemokine receptors, major mediators of inflammation, to influence cardiovascular health. However, using overexpression of PCSK9 in murine models in vivo and PCSK9 stimulation of myeloid and vascular cells in vitro did not reveal influences of PCSK9 on the expression of certain chemokine receptors that are known to be involved in the development and progression of atherosclerosis and vascular inflammation. Hence, we conclude that the inflammatory effects of PCSK9 are not associated with the here investigated chemokine receptors and additional research is required to elucidate which mechanisms mediate PCSK9 effects independent of LDLR.

Meningeal Lymphatics: An Immune Gateway for the Central Nervous System.

The recent (re)discovery of the meningeal lymphatic system has opened new theories as to how immune cells traffic and interact with the central nervous system (CNS). While evidence is accumulating on the contribution of the meningeal lymphatic system in both homeostatic and disease conditions, a lot remains unknown about the mechanisms that allow for interaction between the meningeal lymphatic system and immune cells. In this review, we synthesize the knowledge about the lymphatic immune interaction in the CNS and highlight the important questions that remain to be answered.

Development of Stem Cell-Derived Immune Cells for Off-the-Shelf Cancer Immunotherapies.

Cell-based cancer immunotherapy has revolutionized the treatment of hematological malignancies. Specifically, autologous chimeric antigen receptor-engineered T (CAR-T) cell therapies have received approvals for treating leukemias, lymphomas, and multiple myeloma following unprecedented clinical response rates. A critical barrier to the widespread usage of current CAR-T cell products is their autologous nature, which renders these cellular products patient-selective, costly, and challenging to manufacture. Allogeneic cell products can be scalable and readily administrable but face critical concerns of graft-versus-host disease (GvHD), a life-threatening adverse event in which therapeutic cells attack host tissues, and allorejection, in which host immune cells eliminate therapeutic cells, thereby limiting their antitumor efficacy. In this review, we discuss recent advances in developing stem cell-engineered allogeneic cell therapies that aim to overcome the limitations of current autologous and allogeneic cell therapies, with a special focus on stem cell-engineered conventional αß T cells, unconventional T (iNKT, MAIT, and γδ T) cells, and natural killer (NK) cells.

The viral expression and immune status in human cancers and insights into novel biomarkers of immunotherapy.

BACKGROUND: Viral infections are prevalent in human cancers and they have great diagnostic and theranostic values in clinical practice. Recently, their potential of shaping the tumor immune microenvironment (TIME) has been related to the immunotherapy of human cancers. However, the landscape of viral expressions and immune status in human cancers remains incompletely understood. METHODS: We developed a next-generation sequencing (NGS)-based pipeline to detect viral sequences from the whole transcriptome and used machine learning algorithms to classify different TIME subtypes. RESULTS: We revealed a pan-cancer landscape of viral expressions in human cancers where 9 types of viruses were detected in 744 tumors of 25 cancer types. Viral infections showed different tissue tendencies and expression levels. Multi-omics analyses further revealed their distinct impacts on genomic, transcriptomic and immune responses. Epstein-Barr virus (EBV)-infected stomach adenocarcinoma (STAD) and Human Papillomavirus (HPV)-infected head and neck squamous cell carcinoma (HNSC) showed decreased genomic variations, significantly altered gene expressions, and effectively triggered anti-viral immune responses. We identified three TIME subtypes, in which the "Immune-Stimulation" subtype might be the promising candidate for immunotherapy. EBV-infected STAD and HPV-infected HNSC showed a higher frequency of the "Immune-Stimulation" subtype. Finally, we constructed the eVIIS pipeline to simultaneously evaluate viral infection and immune status in external datasets. CONCLUSIONS: Viral infections are prevalent in human cancers and have distinct influences on hosts. EBV and HPV infections combined with the TIME subtype could be promising biomarkers of immunotherapy in STAD and HNSC, respectively. The eVIIS pipeline could be a practical tool to facilitate clinical practice and relevant studies.

Diarrhea in pediatric recipients of solid organ or bone marrow transplants.

ABSTRACT: Diarrhea is common in adults after solid organ transplantation (SOT) and bone marrow transplantation (BMT), but data in children are limited. Therefore, we aimed to determine the incidence and etiology of pediatric early-onset diarrhea in post SOT and BMT.We reviewed children aged 6 months to 18 years who underwent liver transplantation, kidney transplantation or BMT between January 2015 and December 2019 with duration of diarrhea > 72 hours within the first 6 months after transplantation. Clinical data and diarrheal course were collected. Regression analyses were performed to define factors associated with the interested outcomes.Among 252 transplanted patients, 168 patients (66.6%) had 289 documented episodes of diarrhea. A diagnosis of 68.2% of post-transplant diarrhea remained 'indefinite'. Enteric infection in SOT and gastrointestinal acute graft-versus-host disease (GI-aGVHD) in BMT were the commonly identified etiologies. Among 182 episodes among BMT children, skin rash was more pronounced when compared the ones with diarrhea > 7 days vs ≤ 7 days (odds ratio [OR] 13.9; 95% CI 1.8, 107.6). Males were more likely to develop GI-aGVHD as compared to females (OR 8.9). We found that GI-aGVHD was more common in the ones with skin rash and the presence of white blood cells in stool examination (OR 8.4 and 3.1, respectively). Deaths occurred in 7.7%.Two-thirds of post-transplant children experienced at least one episode of early-onset diarrhea, of which the etiology mainly remains undefined. Various clinical factors of prolonged/chronic diarrhea and GI-aGVHD may help clinicians when managing these children.