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In this paper, we describe an algorithm for accurately segmenting multiple nudfclei from clumps of non-overlapping immuno-histochemically stained histological hepatic (liver) images. This problem is notoriously difficult because of the degree of presence of stains among the multi-nucleated cells, the poor contrast of cell cytoplasm, and the presence of mucus, blood, and inflammatory cells in the images. Hepatocellular carcinoma, characterized by cellular and nuclear enlargement, nuclear pleomorphism, and multi-nucleation, poses a prominent threat. Our proposed method addresses the aforementioned issues for an automated diagnosis system by judging the presence of multiple nuclei in a two-step process: the Quickhull algorithm defines the convex hull of each cell in the image and candidate nuclei regions are located with morphological operations. A combination of features containing local minima and shape-dependent features is extracted for the detection of single or multiple nuclei in each cell with a significant reduction in the number of false positives and false negatives providing an accuracy of 89.76%. [ABSTRACT FROM AUTHOR]

Malaria is a life-threatening parasitic disease spread by infected female Anopheles mosquitoes. After analyzing it, microscopists detect this disease from the sample of microscopic red blood cell images. A professional microscopist is required to conduct the detection process, such an analysis may be time-consuming and provide lowquality results for large-scale diagnoses. This paper develops an ensemble learning-based deep learning model to identify malaria parasites from red blood cell images. VGG16(Retrained), VGG19(Retrained), and DenseNet201(Retrained) are three models that are used in developing the adaptive weighted average ensemble models. To reduce the dispersion of predictions, a max voting ensemble technique is then applied in combination with adaptive weighted average ensemble models. A variety of image processing techniques are utilized including the data augmentation technique to increase the number of data and solve the overfitting problem of the model. Some other approaches of custom CNN, Transfer Learning, and CNN-Machine Learning (ML) classifier techniques are also implemented for comparing their performance with the ensemble learning model. The proposed ensemble learning model provides the best performance among all with an accuracy of 97.92% to classify parasitized and uninfected cells. Therefore, the deep learning model has the potential to diagnose malaria more accurately and automatically. [ABSTRACT FROM AUTHOR]

Blood platelets are crucial to prevent excessive bleeding following injury to blood vessels. Platelets are crucial for the formation of clots and for clot strength. Platelet activation involves aggregation, attachment to fibrin and clot retraction. Most assays that address platelet function measure platelet aggregation, not clot retraction. Here, we describe a 96-well-based clot retraction assay that requires a relatively short runtime and small sample volume. The assay involves continuous optical density monitoring of platelet-rich plasma that is activated with thrombin. The data can be analyzed using time-series analytical tools to generate quantitative information about different phases of clot formation and clot retraction. The assay demonstrated good repeatability and reproducibility and was robust to different calcium concentrations. Impairment of platelet bioenergetics, actin polymerization, fibrin interaction, and signaling significantly affected clot retraction and was detected and showed good agreement with light transmission aggregometry, suggesting that clot retraction is predictive of platelet function. Using this microplate clot retraction assay, we showed a significant difference in platelets stored in autologous plasma compared with platelet additive solution after 7 days of room temperature storage. Platelets are cell fragments in the blood that are necessary for clot formation. They are crucial to preventing excessive bleeding following trauma. To form clots, platelets clump (aggregate) and attach to fibrin protein and cells inside the blood vessels to form strong web-like structures. Platelets also contract to pull the edges of the wound close. Most measurements of platelet function involve aggregation. This paper focuses on platelet contraction. Here, we describe a new assay to measure platelets contraction that is repeatable and reproducible. The assay uses standard and common laboratory equipment and can be performed by most laboratory personnel and has the potential to detect clinical pathologies of clot formation. The assay could be developed for bedside patient care where platelet function could be assessed rapidly and assist in the diagnosis of coagulation and platelet disorders. [ABSTRACT FROM AUTHOR]

The link between inflammation and cancer is still an attractive subject of many studies because systemic inflammatory response has been proven to play a pivotal role in cancer progression and metastasis. The strongest relationship between chronic inflammation and cancer development is observed in colorectal cancer (CRC). The evaluation of ratios derived from the routinely performed inflammatory biomarkers shows limited performances and limited clinical utility when individually used as prognostic factors for patients with CRC. In this review, we would like to summarize the latest knowledge about the diagnostic utility of systemic inflammatory ratios: neutrophil/lymphocyte (NLR), lymphocyte/monocyte (LMR), and platelet/lymphocyte (PLR) in CRC. We focused on the papers that assessed the diagnostic utility of blood cell parameters on the basis of the area under the ROC curve published in the recent 6 years. Identification of biomarkers that are significantly associated with prognostic in cancer would help the selection of patients with a high risk of poor outcomes. [ABSTRACT FROM AUTHOR]

This paper presents a novel medical image processing technique for analyzing different peripheral blood cells such as monocytes, lymphocytes, neutrophils, eosinophils, basophils, and macrophages. However, the existing systems suffered from low accuracy while classifying the different blood cell images and also consume higher processing power. The proposed model consists of two major steps such as segmentation and classification of peripheral blood cells. The modified local-information weighted intuitionistic Fuzzy C-means clustering (MLWIFCM)-based golden eagle optimization algorithm performs the nucleus segmentation. Finally, the peripheral blood cell classes such as Basophil, Lymphocyte, Neutrophil, Monocyte, and Eosinophil are effectively classified using hybrid-parameter RNN-based remora optimization algorithm. The MATLAB R2019b is used as the implementation platform. To analyze the performances of our proposed method, we have taken two datasets; they are BCCD and LISC datasets. Meanwhile, the classification performances were analyzed with the aid of different performance metrics such as mean accuracy, mean intersection over union, mean average precision, and mean BF score values. [ABSTRACT FROM AUTHOR]

Cervical cancer is the most frequent and fatal malignancy among women worldwide. If this tumor is detected and treated early enough, the complications it causes can be minimized. Deep learning demonstrated significant promise when imposed on biomedical difficulties such as medical image processing and disease prognostication. Therefore, in this paper, an automatic cervical cell classification approach named IR-PapNet is developed based on Inception-ResNet which is an optimized version of Inception. The learning model's conventional ReLu activation is replaced with the parametric-rectified linear unit (PReLu) to overcome the nullification of negative values and dying ReLu. Finally, the model loss function is minimized with the SGD optimization model by modifying the attributes of the neural network. Furthermore, we present a simple but efficient noise removal technique called 2D-Discrete Wavelet Transform (2D-DWT) algorithm for enhancing image quality. Experimental results show that this model can achieve a top-1 average identification accuracy of 99.8% on the pap smear cervical Herlev datasets, which verifies its satisfactory performance. The restructured Inception-ResNet network model can obtain significant improvements over most of the state-of-the-art models in 2-class classification, and it achieves a high learning rate without experiencing dead nodes. [ABSTRACT FROM AUTHOR]

Gastroenteritis is a frequent pathology in developing countries. Etiological causes include viral, bacterial or even parasitic pathogens. Implication of pancreas in acute gastroenteritis is not a common event. A female patient arrived at the emergency room of the infectious disease service with fever, nausea/vomiting, profuse diarrhea, fatigue, weakness and severe epigastric pain. From the physical and laboratory examination, the patient was conscious, with epigastric abdominal pain and temperature 39°C. Laboratory examinations resulted in an increase of white blood cells, CRP threefold of normal range, pancreatic amylase and lipase over than threefold too. The abdominal ultrasonography and computed tomography (CT) scan showed pancreatic oedema. Stool culture and antibiogram showed a multi-sensitive Salmonella enterica (S. enterica) subsp enterica. Based on the 2012 Atlanta Classification of Acute Pancreatitis: abdominal pain, serum lipase activity (or amylase) at least 3 times greater than the upper limit of normal and characteristic findings of acute pancreatitis on CT or magnetic resonance imaging the case was classified as an acute pancreatitis by S. enterica subsp enterica. The aim of this paper is to present a case of acute gastroenteritis from S. enterica subsp enterica complicated with acute pancreatitis. [ABSTRACT FROM AUTHOR]

Ferroptosis is an iron-dependent cell death pathway and participates in various diseases. Current evidence suggests that ferroptosis can obviously affect the function of blood cells. This paper aims to elaborate the role of ferroptosis in blood cells and related diseases. First, abnormal ferroptosis damages the developing red blood cells by breaking systemic iron homeostasis, leading to erythropoiesis suppression and anaemia. Ferroptosis mediates neutrophils recruitment and neutrophil extracellular trap formation (NETosis). In T-cells, ferroptosis induces a novel point of synergy between immunotherapy and radiotherapy. Additionally, ferroptosis may mediate B cells differentiation, antibody responses and lymphoma. Nevertheless, increased ferroptosis can ameliorate acute myeloid leukaemia and T-cell leukaemia/lymphoma by inducing iron-dependent cancer cells death. Besides, ferroptosis activates platelets by increasing P-selectin, thus causing thromboembolism. Ferroptosis mediates virus infection and parasite infection by driving T-cell death and preventing T-cell immunity. Interestingly, ferroptosis is also considered as a critical player in COVID-19 infections, while targetting ferroptosis may also improve thromboembolism and prognosis in patients with COVID-19 infection. Overall, the crucial role of ferroptosis in blood cells will show a new therapeutic potential in blood cell-related diseases.HighlightsFerroptosis shows a new therapeutic potential for blood cell-related diseases.Ferroptosis damages erythropoiesis and thus induces anaemia.Ferroptosis induces platelet activation and leads to thromboembolism.Ferroptosis regulates T-cell and B-cell immunity, which participant in infectious diseases.Inversely, ferroptosis ameliorates acute myeloid leukaemia and T-cell leukaemia.

Methods to separate circulating tumor cells (CTCs) from blood samples were intensively researched in order to understand the metastatic process and develop corresponding clinical assays. However current methods faced challenges that stemmed from CTCs' heterogeneity in their biological markers and physical morphologies. To this end, we developed integrated ferrohydrodynamic cell separation (iFCS), a scheme that separated CTCs independent of their surface antigen expression and physical characteristics. iFCS integrated both diamagnetophoresis of CTCs and magnetophoresis of blood cells together via a magnetic liquid medium, ferrofluid, whose magnetization could be tuned by adjusting its magnetic volume concentration. In this paper, we presented the fundamental theory of iFCS and its specific application in CTC separation. Governing equations of iFCS were developed to guide its optimization process. Three critical parameters that affected iFCS's cell separation performance were determined and validated theoretically and experimentally. These parameters included the sample flow rate, the volumetric concentration of magnetic materials in the ferrofluid, and the gradient of the magnetic flux density. We determined these optimized parameters in an iFCS device that led to a high recovery CTC separation in both spiked and clinical samples. [ABSTRACT FROM AUTHOR]

Copper(II) complexes with an α-diimine show a wide variety of biological activities, such as antibacterial, antifungal, antioxidant and anticancer. In this work, we synthesized and structurally characterized two novel Cu(II) complexes with methyl 3-formyl-4-hydroxybenzoate (HL) and α-diimines: 2,2′-bipyridine (bipy) and 1,10-phenanthroline (phen). Crystal structure analysis shows that the formulas of the compounds are [Cu(bipy)(L)(BF4)] (1) and [Cu(phen)(L)(H2O)](BF4)·H2O (2), with BF4− as a ligand in complex 1, which is rarely coordinated to metals. Both complexes have a square pyramidal geometry, while DFT calculations showed that the most stable structures of complexes 1 and 2 in a water/DMSO mixture are square-planar derivatives [Cu(bipy)(L)]+ and [Cu(phen)(L)]+. The antibacterial activity of compounds was evaluated in vitro on four Gram-negative and four Gram-positive bacterial strains. Complex 2 showed greater antibacterial activity towards all bacterial strains comparable to the control compound Amikacin. Complex 2 exerted a strong cytotoxic effect against the tested cancer cell lines (IC50 values ranging from 0.32 to 0.44 μM). Both complexes caused apoptotic cell death in HeLa cells and a noticeable in vitro antiangiogenic effect. In the concentration range of 5 to 100 μM, the complexes showed the absence of a genotoxic effect and displayed a protective effect against oxidative DNA damage induced by H2O2 in human peripheral blood cells. The interaction between the compounds and calf–thymus DNA was evaluated by diverse techniques suggesting a tight binding, which was also confirmed by molecular docking. In addition, it was found that the complexes bind tightly and reversibly to bovine and human serum albumin. [ABSTRACT FROM AUTHOR]

Substantial custodial sentences were handed down from the English Central Criminal Court for organ trafficking. Attempts to exploit vulnerable people for the purposes of organ removal, and for organ, tissue, and cells (OTC) trafficking are ongoing in many jurisdictions. It is also clear from the 2019 EU evaluation of the Union legislation on blood, tissues, and cells that there is increasing commercialization in the procurement of human material for medical use, using global supply chains. Full traceability of all human material, for which there is a shortage to meet global medical needs, is required. The medical profession has drafted the Istanbul Declaration on Organ Trafficking and Transplant Tourism 2008, calling for transplant tourism to be prohibited, and full engagement with transnational law enforcement to address developing transnational organized crime in this area. This paper addresses the global, Council of Europe, EU, and UK provisions in this area. [ABSTRACT FROM AUTHOR]

The currently existing label-based techniques for the detection of circulating tumor cells (CTCs) target natural surface proteins of cells and are therefore applicable to only limited cancer cell types. We report optomicrofluidic detection of cancer cells in the pool of peripheral blood mononuclear cells (PBMCs) by exploiting the difference in their cell metabolism. We employ metabolic glycoengineering as a click chemistry tool for tagging cells that yields several fold-higher fluorescence signals from cancer cells compared to that from PBMCs. The effects of concentrations of the tagging compounds and cell incubation time on the fluorescence signal intensity are studied. The tagged cells were encapsulated in droplets ensuring that cells enter the detection region two-dimensionally focused in single-file and optically detected with a high detection efficiency and low coefficient of variation of the signals. The metabolic tagging approach showed a significantly higher tagging efficiency and average fluorescence signal compared to the well-established and widely adopted anti-EpCAM-FITC-based tagging. We demonstrated the detection of three different cancer cell lines – EpCAM-negative cervical cancer cell, HeLa, weakly EpCAM positive, and triple-negative breast cancer cell, MDA-MB-231, and strongly EpCAM positive breast cancer cell, MCF7, highlighting that the proposed technique is independent of naturally occurring cell surface proteins and widely applicable. The metabolically tagged and optically detected cells were successfully recultured, proving the compatibility of the proposed technique with downstream assays. The proposed technique is then utilised for the detection of CTCs in metastatic cancer patients' blood. The current work provides a new strategy for detecting cancer cells in the blood that can find potential applications in both fundamental research and clinical studies involving CTCs as well as in single-cell sequencing. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Squeeze-and-excitation-based Mask Region Convolutional Neural Network (SEM-RCNN) for Environmental Microorganisms (EM) detection tasks. Mask RCNN, one of the most applied object detection models, uses ResNet for feature extraction. However, ResNet cannot combine the features of different image channels. To further optimize the feature extraction ability of the network, SEM-RCNN is proposed to combine the different features extracted by SENet and ResNet. The addition of SENet can allocate weight information when extracting features and increase the proportion of useful information. SEM-RCNN achieves a mean average precision (mAP) of 0.511 on EMDS-6. We further apply SEM-RCNN for blood-cell detection tasks on an open source database (more than 17,000 microscopic images of blood cells) to verify the robustness and transferability of the proposed model. By comparing with other detectors based on deep learning, we demonstrate the superiority of SEM-RCNN in EM detection tasks. All experimental results show that the proposed SEM-RCNN exhibits excellent performances in EM detection. [ABSTRACT FROM AUTHOR]

The liver is a complex organ that governs many types of metabolisms, including energy metabolism and other cellular processes. The liver also plays a crucial role in important functions in immunity, and the activity of liver tissue-associated immunity affects the outcome of many liver pathologies. A thorough characterization of the liver immune microenvironment may contribute to a better understanding of immune signaling, the mechanisms of specific immune responses, and even to improved predictions about therapy outcomes. In this paper, we present an optimized, simple, and rapid protocol to characterize the liver-associated immune cell milieu. We believe that the most suitable technique for obtaining a complex immune cell suspension and for removing contaminating blood cells is to perform mouse liver perfusion, using only phosphate buffer saline. Combining an enzymatic digestion and a mechanical dissociation of liver tissue, followed by cell purification, improves downstream applications. This combination is an essential prerequisite for immune cell determination and characterization. We then demonstrate a flow cytometry-based multiparametric immunophenotyping along with a gating strategy to detect and quantify liver endothelial cells, T cells (helper and cytotoxic), B cells, NK cells, NKT cells, neutrophils, monocytes (subsets included), dendritic cells (subsets included), macrophages and Kupffer cells. [ABSTRACT FROM AUTHOR]

With the recent developments in deep learning, automatic cell segmentation from images of microscopic examination slides seems to be a solved problem as recent methods have achieved comparable results on existing benchmark datasets. However, most of the existing cell segmentation benchmark datasets either contain a single cell type, few instances of the cells, not publicly available. Therefore, it is unclear whether the performance improvements can generalize on more diverse datasets. In this paper, we present a large and diverse cell segmentation dataset BBBC041Seg 1 , which consists both of uninfected cells (i.e., red blood cells/RBCs, leukocytes) and infected cells (i.e., gametocytes, rings, trophozoites, and schizonts). Additionally, all cell types do not have equal instances, which encourages researchers to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. Furthermore, we conduct a comparative study using both classical rule-based and recent deep learning state-of-the-art (SOTA) methods for automatic cell segmentation and provide them as strong baselines. We believe the introduction of BBBC041Seg will promote future research towards clinically applicable cell segmentation methods from microscopic examinations, which can be later used for downstream tasks such as detecting hematological diseases (i.e., malaria)., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Chromosomal analyses of the trisomy ratio of eight patients, seven with standard trisomy 21 and one translocated trisomy 21 (patient H). Previously, two reports documented a decreased mosaic ratio in mosaic DS patients during early childhood, mainly in peripheral blood cultures.[[4]] We studied longitudinal changes in the mosaic ratio of six mosaic DS patients, including young adults. As noted in a review paper,[1] a large-scale study revealed that approximately 1.1%-3.8% of all children with Down syndrome (DS) are born with mosaic DS.[[2]] DS is diagnosed postnatally by chromosome G-banding analysis of peripheral blood cultures, and most patients do not undergo subsequent repeat analysis. [Extracted from the article]

Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping. [ABSTRACT FROM AUTHOR]

White blood cells (WBCs) are the important constituent of a blood cell. These blood cells are responsible for defending the body against infections. Abnormalities identified in WBC smears lead to the diagnosis of disease types such as leukocytosis, hepatitis, and immune system disorders. Digital image analysis for infection detection at an early stage can help fast and precise diagnosis, as compared to manual inspection. Sometimes, acquired blood cell smear images from an L2-type microscope are of very low quality. The manual handling, haziness, and dark areas of the image become problematic for an efficient and accurate diagnosis. Therefore, WBC image enhancement needs attention for an effective diagnosis of the disease. This paper proposed a novel virtual hexagonal trellis (VHT)-based image filtering method for WBC image enhancement and contrast adjustment. In this method, a filter named the virtual hexagonal filter (VHF), of size 3 × 3, and based on a hexagonal structure, is formulated by using the concept of the interpolation of real and square grid pixels. This filter is convolved with WBC ALL-IBD images for enhancement and contrast adjustment. The proposed filter improves the results both visually and statically. A comparison with existing image enhancement approaches proves the validity of the proposed work. [ABSTRACT FROM AUTHOR]

Object detection technology has been widely used in medical field, such as detecting the images of blood cell to count the changes and distribution for assisting the diagnosis of diseases. However, detecting small objects is one of the most challenging and important problems especially in medical scenarios. Most of the objects in medical images are very small but influential. Improving the detection performance of small objects is a very meaningful topic for medical detection. Current researches mainly focus on the extraction of small object features and data augmentation for small object samples, all of these researches focus on extracting the feature space of small objects better. However, in the training process of a detection model, objects of different sizes are mixed together, which may interfere with each other and affect the performance of small object detection. In this paper, we propose a method called pixel level balancing (PLB), which takes into account the number of pixels contained in the detection box as an impact factor to characterize the size of the inspected objects, and uses this as an impact factor. The training loss of each object of different size is adjusted by a weight dynamically, so as to improve the accuracy of small object detection. Finally, through experiments, we demonstrate that the size of objects in object detection interfere with each other. So that we can improve the accuracy of small object detection through PLB operation. This method can perform well with blood cell detection in our experiments. [ABSTRACT FROM AUTHOR]

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim's blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods. [ABSTRACT FROM AUTHOR]

The paper presents the results of studying the immunological parameters of 369 people who were practically healthy at the time of the survey, 298 women and 71 men, of which 216 people are living in the European North of the Russian Federation (173 women and 43 men) and 153 are residents of the Arctic (125 women and 28 men). The study was carried out in the morning (08:00-10:00 am). The study included the determination of the aggregation of erythrocytes, platelets, neutrophilic granulocytes, lymphocytes, hemogram study, hematological analysis, enzyme immunoassay, and flow cytometry. Statistical processing of the obtained data was carried out using the Statistica 7.0 software package (StatSoft, USA). It was found that the activity of aggregation of cells of peripheral venous blood in Arctic residents is 1.5-1.7 times higher than that in people living in more favourable climatic conditions. The frequency of registration of aggregation of erythrocytes and platelets is actually 2 times higher than the aggregation of leukocytes. Aggregation of erythrocytes is associated with an increase in the concentrations of transferrin and receptors for this transport protein. The frequency of detection of platelet aggregation is accompanied by an increase in transferrin concentrations; in cases of aggregation of nonnuclear blood cells, the content of NO 2 in the blood serum is increased. Aggregation of neutrophilic granulocytes and lymphocytes is associated with an increase in the content of free adhesion molecules. Aggregation of erythrocytes and platelets is in evidence when it is necessary to trigger reactions of changes in the hemodynamics of microcirculation to increase the efficiency of oxygen and trophic supply of tissues. The adhesion of leukocytes to the endothelium determines the secretion of biologically active substances that contribute to a change in microcirculation and an increase in the migration of leukocytes into tissues for the implementation of phagocytic and cytolytic functions., Competing Interests: The authors declare that there are no conflict of interests., (Copyright © 2021 L. K. Dobrodeeva et al.)

This paper proposes microfluidic particle separation by sheath-free deterministic lateral displacement (DLD) with inertial focusing in a single straight input channel. Unlike conventional DLD devices for size-based particle separation, in which sheath streams are used to focus the particles before the solution containing them reaches the DLD arrays, the proposed method uses inertial focusing to align the particles along the middle or the sidewalls of the straight rectangular input channel. The two-stage model of inertial focusing is applied to reduce the length of the side-focusing channel. The proposed method is demonstrated by using it to separate fluorescent polymer particles of diameters 13 and 7 μm, in the process of which the effect of the particle focusing regime on the separation performance is also investigated. Through middle focusing, the method is further used to separate MCF-7 cells (a model of circulating tumor cells (CTCs)) and blood cells, with ∼99.0% capture efficiency achieved. [ABSTRACT FROM AUTHOR]

This paper describes a technique to detect blood cell levels based on the time-period modulation of a relaxation oscillator loaded with an Inter-Digitated Capacitor (IDC). A digital readout circuit has been proposed to measure the time-period difference between the two oscillators loaded with samples of healthy and (potentially) unhealthy blood. A prototype circuit was designed in 65-nm CMOS technology and post-layout simulations show 15.25-aF sensitivity. The total circuit occupies 2,184- μ m2 silicon area and consumes 216 μ A from a 1-V power supply. [ABSTRACT FROM AUTHOR]

The ability to separate specific biological components from cell suspensions is indispensable for liquid biopsies, and for personalized diagnostics and therapy. This paper describes an advanced surface acoustic wave (SAW) based device designed for the enrichment of platelets (PLTs) from a dispersion of PLTs and red blood cells (RBCs) at whole blood concentrations, opening new possibilities for diverse applications involving cell manipulation with high throughput. The device is made of patterned SU-8 photoresist that is lithographically defined on the wafer scale with a new proposed methodology. The blood cells are initially focused and subsequently separated by an acoustic radiation force (ARF) applied through standing SAWs (SSAWs). By means of flow cytometric analysis, the PLT concentration factor was found to be 7.7, and it was proven that the PLTs maintain their initial state. A substantially higher cell throughput and considerably lower applied powers than comparable devices from literature were achieved. In addition, fully coupled 3D numerical simulations based on SAW wave field measurements were carried out to anticipate the coupling of the wave field into the fluid, and to obtain the resulting pressure field. A comparison to the acoustically simpler case of PDMS channel walls is given. The simulated results show an ideal match to the experimental observations and offer the first insights into the acoustic behavior of SU-8 as channel wall material. The proposed device is compatible with current (Lab-on-a-Chip) microfabrication techniques allowing for mass-scale, reproducible chip manufacturing which is crucial to push the technology from lab-based to real-world applications. [ABSTRACT FROM AUTHOR]

Isolation and analysis of circulating tumor cells (CTCs) from blood samples present exciting opportunities for basic cancer research and personalized treatment of the disease. While microchip-based negative CTC enrichment offers both sensitive microfluidic cell screening and unbiased selection, conventional microchips are inherently limited by their capacity to deplete a large number of normal blood cells. In this paper, we use 3D printing to create a monolithic device that combines immunoaffinity-based microfluidic cell capture and a commercial membrane filter for negative enrichment of CTCs directly from whole blood. In our device, stacked layers of chemically-functionalized microfluidic channels capture millions of white blood cells (WBCs) in parallel without getting saturated and the leuko-depleted blood is post-filtered with a 3 μm-pore size membrane filter to eliminate anucleated blood cells. This hybrid negative enrichment approach facilitated direct extraction of viable CTCs off the chip on a membrane filter for downstream analysis. Immunofluorescence imaging of enriched cells showed ∼90% tumor cell recovery rate from simulated samples spiked with prostate, breast or ovarian cancer cells. We also demonstrated the feasibility of our approach for processing clinical samples by isolating prostate cancer CTCs directly from a 10 mL whole blood sample. [ABSTRACT FROM AUTHOR]

An increased level of granulocyte‐macrophage colony‐stimulating factor has a potential role in the development of autoimmune diseases, and the neutralization of its activity by monoclonal antibodies is a promising therapy for some diseases. Here, the crystal structure of the Fab region of EV1007, a fully human antibody expressed in Chinese hamster ovary cells that was developed from human peripheral blood mononuclear cells, is described. The structure closely resembles that of MB007, which is the Fab region of the same antibody expressed in Escherichia coli [Blech et al. (2012), Biochem. J.447, 205–215], except at the hinge regions between the immunoglobulin domains and the H3 loop region. This paper presents evidence for the flexibility of the hinge and H3 loop regions of the antibody based on the comparison of two independently solved crystal structures. [ABSTRACT FROM AUTHOR]

The growing interest in regenerative medicine has opened new avenues for novel cell therapies using stem cells. Bone marrow aspirate (BMA) is an important source of stromal mesenchymal stem cells (MSCs). Conventional MSC harvesting from BMA relies on archaic centrifugation methods, often leading to poor yield due to osmotic stress, high centrifugation force, convoluted workflow, and long experimental time (∼2–3 hours). To address these issues, we have developed a scalable microfluidic technology based on deterministic lateral displacement (DLD) for MSC isolation. This passive, label-free cell sorting method capitalizes on the morphological differences between MSCs and blood cells (platelets and RBCs) for effective separation using an inverted L-shaped pillar array. To improve throughput, we developed a novel multi-chip DLD system that can process 2.5 mL of raw BMA in 20 ± 5 minutes, achieving a 2-fold increase in MSC recovery compared to centrifugation methods. Taken together, we envision that the developed DLD platform will enable fast and efficient isolation of MSCs from BMA for effective downstream cell therapy in clinical settings. [ABSTRACT FROM AUTHOR]

Platelet is a blood cell type that is stored and circulated in the human body. It acts as a blood thickening agent and prevents blood from overflowing whenever bleeding occurs. An excessive or inadequate number of platelets could lead to platelet-related diseases. The current practice of platelet counting involves the manual counting process using a haemocytometer, Wright's Stain which uses the dyes to facilitate the differentiation of blood cell types, and a tally counter. Yet, this process can be time-consuming, demanding, and exhausting for haematologists, and likely to be prone to errors. Thus, this paper presents a study on automated platelet counter and detection using image processing techniques. The K-Means Clustering was employed to count and detect the presence of platelets in microscopic blood smear images. Several processes were performed prior to the K-means clustering, including image enhancement and YCbCr image formatting. Subsequently, image masking, as well as area thresholding were applied to eliminate every unwanted entity and highlight the visibility of the platelets before the number of platelets could be detected and counted. A comparative experiment was designed in which the K-Means Clustering platelet count and detection were compared with the actual number of platelets reported by haematologists. The platelet counts and detection were categorized into three detection categories which are Less Detection (LD), Accurate Detection (AD), and Over Detection (OD). The proposed study was evaluated to 90 testing platelet images. Out of the 90 testing images, 75 platelet images were perfectly counted and detected which returned 91.67% of accuracy. This signifies that the K-Means Clustering algorithm was discovered to be efficient and dependable for automated platelet counter and detection. [ABSTRACT FROM AUTHOR]

Background: Tumor microenvironment plays pivotal roles in carcinogenesis, cancer development and metastasis. Composition of cancer immune cell subsets can be inferred by deconvolution of gene expression profile accurately. Compositions of the cell types in cancer microenvironment including cancer infiltrating immune and stromal cells have been reported to be associated with the cancer outcomes markers for cancer prognosis. However, rare studies have been reported on their association with the response to preoperative radiotherapy for rectal cancer. Methods: In this paper, we deconvoluted the immune/stromal cell composition from the gene expression profiles. We compared the composition of immune/stromal cell types in the RT responsive versus nonresponsive for rectal cancer. We also compared the peripheral blood immune cell subset composition in the stable diseases versus progressive diseases of rectal cancer patients with fluorescence-activated cell sorting from our institution. Results: Compared with the non-responsive group, the responsive group showed higher proportions of CD4+ T cell (0.1378 ± 0.0368 vs. 0.1071 ± 0.0373, p = 0.0215), adipocytes, T cells CD4 memory resting, and lower proportions of CD8+ T cell (0.1798 ± 0.0217 vs. 0.2104 ± 0.0415, p = 0.0239), macrophages M2, and preadipocytes in their cancer tissue. The responsive patients showed a higher ratio of CD4+/CD8+ T cell proportions (mean 0.7869 vs. 0.5564, p = 0.0210). Consistently, the peripheral blood dataset showed higher proportion of CD4+ T cells and higher ratio of CD4+/CD8+ T cells, and lower proportion of CD8+ T cells for favorable prognosis. We validated these results with a pooled dataset of GSE3493 and GSE35452, and more peripheral blood data, respectively. Finally, we imported these eight cell features including eosinophils and macrophage M1 to Support Vector Machines and could predict the pre-radiotherapy responsive versus non-responsive with an accuracy of 76%, ROC AUC 0.77, 95% confidential interval of 0.632–0.857, better than the gene signatures. Conclusions: Our results showed that the proportions of tumor-infiltrating subsets and peripheral blood immune cell subsets can be important immune cell markers and treatment targets for outcomes of radiotherapy for rectal cancer. [ABSTRACT FROM AUTHOR]

Thalassemia is a blood disorder inherited as an autosomal single gene characterized by a reduced or absent amount of hemoglobin chains. It is considered to be the most common genetic disorder with defect in either alpha or beta chains which produce abnormal blood cells. The present report describes an inflammatory enlargement over gingiva in the region of an erupting maxillary right premolar in a 10-year-old male who had minor thalassemia. The case was surgically handled using LASER, being a conservative management option, especially in such patients. The histopathological examination confirmed it to be a pyogenic granuloma. The paper describes the management of such lesions using LASERS as minimally invasive surgical techniques. It also tries to establish a hypothesis of a possible pathogenic association between intraoral pyogenic granuloma and thalassemia. [ABSTRACT FROM AUTHOR]

For the analysis of medical images, one of the most basic methods is to diagnose diseases by examining blood smears through a microscope to check the morphology, number, and ratio of red blood cells and white blood cells. Therefore, accurate segmentation of blood cell images is essential for cell counting and identification. The aim of this paper is to perform blood smear image segmentation by combining neural ordinary differential equations (NODEs) with U-Net networks to improve the accuracy of image segmentation. In order to study the effect of ODE-solve on the speed and accuracy of the network, the ODE-block module was added to the nine convolutional layers in the U-Net network. Firstly, blood cell images are preprocessed to enhance the contrast between the regions to be segmented; secondly, the same dataset was used for the training set and testing set to test segmentation results. According to the experimental results, we select the location where the ordinary differential equation block (ODE-block) module is added, select the appropriate error tolerance, and balance the calculation time and the segmentation accuracy, in order to exert the best performance; finally, the error tolerance of the ODE-block is adjusted to increase the network depth, and the training NODEs-UNet network model is used for cell image segmentation. Using our proposed network model to segment blood cell images in the testing set, it can achieve 95.3% pixel accuracy and 90.61% mean intersection over union. By comparing the U-Net and ResNet networks, the pixel accuracy of our network model is increased by 0.88% and 0.46%, respectively, and the mean intersection over union is increased by 2.18% and 1.13%, respectively. Our proposed network model improves the accuracy of blood cell image segmentation and reduces the computational cost of the network., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Dongming Li et al.)

The journal Blood Cells was initiated in 1975 by Marcel Bessis, a French hematologist and cell biologist, as a vehicle for the publication of papers and discussions presented at an international blood club meeting he convened at L' Institut de Pathologie Cellulaire on the campus of Hôpital Bicétre in Kremlin Bicétre, France, a commune on the southern border of Paris. The group met at the Institute for the first time in October 1972. After the first meeting, Bessis published the articles describing the presentations in the Nouvelle Revue d'Hématologie Française , France's principal journal for articles on the science and practice of hematology of which he was the editor. The refusal of the Nouvelle Revue d'Hématologie Française to continue publishing the papers from the meeting of the blood club in English prompted Bessis to start a new journal, Blood Cells , in 1975. Blood Cells , also, began to accept individual submitted papers unrelated to the blood club meeting and, thus, it evolved into a standard journal. A decade later, when Bessis became ill, he asked Brian Bull, a hematopathologist and professor at Loma Linda University School of Medicine in California to assume the position as the second editor-in-chief. He and Bessis had become scientific collaborators and good friends in the preceding years. In 1995, Ernest Beutler, Chair of Molecular and Experimental Medicine at Scripps Research Institute, assumed the editor-in-chief position and transformed the Journal by making three consequential changes. He expanded its title to Blood Cells, Molecules and Diseases , converted its editorial board to past presidents of the American Society of Hematology plus a few additional experimental hematologists of note, a few from abroad, and he converted the Journal to a digital format, hosted on the Scripps Research Institute server. The Journal was the first published solely in a digital format. It, subsequently, was bought by Academic Press, then Harcourt and, then, by Elsevier. The next three editors-in-chief were (i) Marshall A. Lichtman, then Professor of Medicine (Hematology) and of Biochemistry and Biophysics and former Dean of the School of Medicine and Dentistry at the University of Rochester Medical Center, editor from 2000 to 2013, (ii) Mohandas Narla, then Vice President for Research and Director of The Laboratory of Red Cell Physiology at the New York Blood Center, editor from 2014 to 2021 and (iii) Lionel Blanc, Professor of Molecular Medicine and Pediatrics, Center for Autoimmune, Musculoskeletal and Hematopoietic Diseases, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research and the Les Nelkin Professor of Pediatric Oncology Donald and Barbara Zucker School of Medicine at Hofstra-Northwell from 2022 to the present. Although the Journal publishes papers on any aspect of hematology, it has developed a focus on disorders of red cells, erythropoiesis and hematopoiesis. In October 2024, it celebrates its 50th anniversary as a vehicle for the publication of papers in the discipline of hematology. [ABSTRACT FROM AUTHOR]

Objective: Schizophrenia is a complex widespread neuropsychiatric disorder. This illness encompasses a complex debilitating mental disorder causing illusion, delusion, disturbed relationship, low motivation and decline of emotion. Viral infection of the brain including Borna Disease Virus (BDV) may play a role in transient or permanent neurological and behavioral abnormalities. This role of Borna virus has not been resolved outright yet, and based on published papers investigation examining the role of this virus in schizophrenia is in progress worldwide. Method: In this study, Nested Reverse Transcription-Polymerase Chain Reaction (Nested RT-PCR) was used for detection of BDV Ribonucleic Acid (RNA) in Peripheral Blood Mononuclear Cells (PBMCs) of a group of patients experiencing the first episode of schizophrenia. The results were compared with a normal group. Results: In our study, no BDV-positive was found in PBMCs of the case group. Out of 40 participants of control group one was positive for P24 gene of BDV. This result are similar to several published papers about this topic. Conclusion: An etiological relationship between Bornavirus and schizophrenia was not found in this study. More investigations are warranted to illustrate the probable relationship between bornavirus infection and schizophrenia. [ABSTRACT FROM AUTHOR]