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Particle counting serves as a pivotal constituent in diverse analytical domains, encompassing a broad spectrum of entities, ranging from blood cells and bacteria to viruses, droplets, bubbles, wear debris, and magnetic beads. Recent epochs have witnessed remarkable progressions in microfluidic chip technology, culminating in the proliferation and maturation of microfluidic chip-based particle counting methodologies. This paper undertakes a taxonomical elucidation of microfluidic chip-based particle counters based on the physical parameters they detect. These particle counters are classified into three categories: optical-based counters, electrical-based particle counters, and other counters. Within each category, subcategories are established to consider structural differences. Each type of counter is described not only in terms of its working principle but also the methods employed to enhance sensitivity and throughput. Additionally, an analysis of future trends related to each counter type is provided. [ABSTRACT FROM AUTHOR]

Pathology reports are necessary for specialists to make an appropriate diagnosis of diseases in general and blood diseases in particular. Therefore, specialists check blood cells and other blood details. Thus, to diagnose a disease, specialists must analyze the factors of the patient's blood and medical history. Generally, doctors have tended to use intelligent agents to help them with CBC analysis. However, these agents need analytical tools to extract the parameters (CBC parameters) employed in the prediction of the development of life-threatening bacteremia and offer prognostic data. Therefore, this paper proposes an enhancement to the Rabin–Karp algorithm and then mixes it with the fuzzy ratio to make this algorithm suitable for working with CBC test data. The selection of these algorithms was performed after evaluating the utility of various string matching algorithms in order to choose the best ones to establish an accurate text collection tool to be a baseline for building a general report on patient information. The proposed method includes several basic steps: Firstly, the CBC-driven parameters are extracted using an efficient method for retrieving data information from pdf files or images of the CBC tests. This will be performed by implementing 12 traditional string matching algorithms, then finding the most effective ways based on the implementation results, and, subsequently, introducing a hybrid approach to address the shortcomings or issues in those methods to discover a more effective and faster algorithm to perform the analysis of the pathological tests. The proposed algorithm (Razy) was implemented using the Rabin algorithm and the fuzzy ratio method. The results show that the proposed algorithm is fast and efficient, with an average accuracy of 99.94% when retrieving the results. Moreover, we can conclude that the string matching algorithm is a crucial tool in the report analysis process that directly affects the efficiency of the analytical system. [ABSTRACT FROM AUTHOR]

White blood cells (WBCs) play a main role in identifying the health condition and disease characteristics of a normal person. An automated classification system is capable of recognizing white blood cells that may help doctors to diagnose several diseases like malaria, anemia, leukemia, etc. Automated blood cell analysis allows fast and accurate outcomes and often involves broad data without performance negotiation. The state-of-the-art systems use a lot of different stages (feature extraction, segmentation, pre-processing, etc.) to provide the automated blood cell analysis using blood smear images which is a lengthy process. To overcome these problems, this paper presents an efficient peripheral blood cell image recognition and classification using a combination of the salp swarm algorithm and the cat swarm optimization (SSPSO) algorithm-based optimized convolutional neural networks (SSPSO-CNN) method. This paper uses the CNN approach to classify five peripheral blood cells such as eosinophil, basophil, lymphocytes, monocytes, and neutrophils without any human intervention. The other objective of this paper is to propose an improved version of salp swarm optimizer (SSO) using particle swarm optimization (PSO) to attain competitive classification performance over the database of the blood cell images. In this paper, the CNN uses VGG19 architecture for training purposes. The accuracy of the classification achieved with VGG19 models is 98%. The proposed model based on the CNN approach optimized by SSPSO achieves high classification accuracy and provides automatic peripheral blood cell classification. This method establishes the fine-tuning process to develop a classifier trained using 10 674 images obtained from medical practice. The proposed method augmented the performance in terms of high precision and F 1-score and obtained an overall classification accuracy of 99%. [ABSTRACT FROM AUTHOR]

The combination of a blood cell analyzer and artificial microscopy to detect white blood cells is used in hospitals. Blood cell analyzers not only have large throughput, but they also cannot detect cell morphology; although artificial microscopy has high accuracy, it is inefficient and prone to missed detections. In view of the above problems, a method based on Fourier ptychographic microscopy (FPM) and deep learning to detect peripheral blood leukocytes is proposed in this paper. Firstly, high-resolution and wide-field microscopic images of human peripheral blood cells are obtained using the FPM system, and the cell image data are enhanced with DCGANs (deep convolution generative adversarial networks) to construct datasets for performance evaluation. Then, an improved DETR (detection transformer) algorithm is proposed to improve the detection accuracy of small white blood cell targets; that is, the residual module Conv Block in the feature extraction part of the DETR network is improved to reduce the problem of information loss caused by downsampling. Finally, CIOU (complete intersection over union) is introduced as the bounding box loss function, which avoids the problem that GIOU (generalized intersection over union) is difficult to optimize when the two boxes are far away and the convergence speed is faster. The experimental results show that the mAP of the improved DETR algorithm in the detection of human peripheral white blood cells is 0.936. In addition, this algorithm is compared with other convolutional neural networks in terms of average accuracy, parameters, and number of inference frames per second, which verifies the feasibility of this method in microscopic medical image detection. [ABSTRACT FROM AUTHOR]

Blood cells play an important role in the metabolism of the human body, and the status of blood cells can be used for clinical diagnoses, such as the ratio of different blood cells. Therefore, blood cell classification is a primary task, which requires much time for manual analysis. The recent advances in computer vision can be beneficial to free doctors from tedious tasks. In this paper, a novel automated blood cell classification model based on the shifted window vision transformer (SW-ViT) is proposed. The SW-ViT architecture is firstly pre-trained on the ImageNet dataset and fine-tuned on the blood cell images for classification. Two transfer strategies are employed to generate better classification results. One is to fine-tune the entire SW-ViT, and the other is to only fine-tune the linear output layer of the SW-ViT while all the other parameters are frozen. A public dataset named BCCD_Dataset (Blood Cell Count and Detection) is utilized in the experiments. The results show that the SW-ViT outperforms several state-of-the-art methods in terms of classification accuracy. The proposed SW-ViT can be applied in daily clinical diagnosis. [ABSTRACT FROM AUTHOR]

In the human body, white blood cells (WBCs) are crucial immune cells that help in the early detection of a variety of illnesses. Determination of the number of WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, as well as AIDS and leukemia. However, the conventional method of classifying and counting WBCs is time-consuming, laborious, and potentially erroneous. Therefore, this paper presents a computer-assisted automated method for recognizing and detecting WBC categories from blood images. Initially, the blood cell image is preprocessed and then segmented using an effective deep learning architecture called SegNet. Then, the important features are devised and extracted using the EfficientNet architecture. Finally, the WBCs are categorized into four different types using the XGBoost classifier: neutrophils, eosinophils, monocytes, and lymphocytes. The advantages of SegNet, EfficientNet, and XGBoost make the proposed model more robust and achieve a more efficient classification of the WBCs. The BCCD dataset is used to evaluate the performance of the proposed methodology, and the findings are compared to existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, and F1-score. Evaluation results show that the proposed approach has a higher rank-1 accuracy of 99.02% and outperformed other existing techniques. [ABSTRACT FROM AUTHOR]

In this paper we present an automatic method for leukocyte classification using support vector machines in multiple stages that results in higher accuracy and less overfitting than other automatic methods. White blood cells (WBC), also called leukocytes, play an important role in the immune system to help protect the body from virus and bacterial diseases. Leukocyte tests can help diagnose a number of diseases related to blood disorders such as acute leukemia, chronic myeloid leukemia. Manual blood cell classification is commonly used in clinics and hospitals, automated cell classification systems may assist clinicians to increase efficiency and accuracy of diagnosis. In recent years, along with the development of artificial intelligence, there have been many studies using automatic methods to classify and count leukocytes. In this paper, we propose a Multi-stage support vector machine method (Multistage SVM) to classify leukocytes into 4 classes with 93% accuracy and less overfitting than other automatic methods. [ABSTRACT FROM AUTHOR]

Long non-coding RNA (LncRNA) is a class of non-coding RNA comprising more than 200 nucleotides. Several studies report that LncRNAs are widely involved in biological processes such as DNA methylation, histone modification, and chromatin remodeling. LncRNA interacts with DNA, RNA, and protein molecules thus regulating expression of target genes. LncRNAs are associated with occurrence and development of tumors, nervous systems, metabolic diseases, reproductive development, and cardiovascular diseases. Therefore, LncRNAs are implicated in progression of several diseases. Studies report that LncRNA plays a crucial role in regulation of gene expression during proliferation and differentiation stages of red blood cell development. LncRNA promotes development of hematopoietic stem cells and plays an important role in myeloid and lymphatic function. In this paper, studies on regulatory mechanism of LncRNA in blood cells and pathogenesis in hematological malignancies were reviewed., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In this paper, a microfluidic device is introduced based on direct current (dc) dielectrophoresis for the separation of circulating tumor cells from normal blood cells. Differential sidewall electrodes are designed inside the channel and two middle-channel outlets are located after the end of the electrodes, which are assigned to collect the normal blood cells. A maximum dc potential of 3 V is applied to the electrodes, resulting in a differential dielectrophoretic forces acting on the cells. This paper involves isolating MCF-7 breast cancer cells from white blood cells (WBCs) and red blood cells (RBCs), eliminating the demand for primary RBC lysis. Numerical simulation results revealed that the differential electrodes not only suppressed successfully the need for high applied voltages by dc dielectrophoresis but also diminished the joule heating effects. Besides that, the arrangement of the differential electrodes was observed to play a critical role in the separation performance. In particular, designing the outlets, as well as adjusting the electrode potentials, has effectively assisted the device to be tunable for different cancer cells even with different radii. More strikingly, the ability of the device to deflect the waste cells (WBCs and RBCs) to out of the microchannel prior to the cancer cells was proven to positively affect the separation efficiency and purity. The proposed device was capable of separating different cells leveraging various diameters with capture purity and efficiency of higher than 83% and 100%, respectively. [ABSTRACT FROM AUTHOR]

Hematopoietic stem cells are a group of heterogeneity cells with the potential to differentiate into various types of mature blood cells. Their basic biological properties include quiescence, self-renewal, multilineage differentiation, and homing ability, with the homing of exogenous hematopoietic stem cells after transplantation becoming a new focus, while the first three properties share some similarity in mechanism due to connectivity. In various complex mechanisms, the role of E3 ubiquitin ligases in hematopoietic homeostasis and malignant transformation is receiving increasing attention. As a unique part, E3 ubiquitin ligases play an important role in physiological regulation mechanism of posttranslational modification. In this review, we focus on the recent progress of the crucial role of E3 ubiquitin ligases that target specific proteins for ubiquitination to regulate biological properties of hematopoietic stem cells. Additionally, this paper deals with E3 ubiquitin ligases that affect the biological properties through aging and summarizes the relevant applications of targeting E3 ligases in hematopoietic malignancies. We present some ideas on the clinical application of E3 ubiquitin ligase to regulate hematopoietic stem cells and also believe that it is meaningful to study the upstream signal of these E3 ubiquitin ligases because hematopoietic stem cell dysfunction is caused by deficiency of some E3 ligases. [ABSTRACT FROM AUTHOR]

The phenomenon of bifurcation in disease transmission models has been observed in a number of epidemiological models. The consequence of bifurcation is that the classical requirement of the reproduction number being less than unity becomes only a necessary, but not sufficient, for disease elimination. This paper addresses the problem of finding the causes of bifurcation in standard deterministic models for the spread of HBV diseases with non-Cytolytic cure processes on infected liver and blood cells. The model contains logistic growth of healthy liver and blood cells and non -Cytolytic cure processes of infected cells. I have got that the model exhibits back ward and forward bifurcations with some conditions. The existence of a backward bifurcation is an interesting artifact since this means that the disease cannot be eradicated by simply reducing the value of the basic reproduction number R 0 below 1.This can have important implications on drug therapy protocols, since it sheds light on possible control mechanisms for disease eradication. [ABSTRACT FROM AUTHOR]

(1) Hematological malignancies are characterized by an immortalization, uncontrolled proliferation of blood cells and their differentiation block, followed by the loss of function. The primary goal in the treatment of leukemias is the elimination of rapidly proliferating leukemic cells (named blasts). However, chemotherapy, which removes proliferating blasts, also prevents the remaining immune cells from being activated. Acute leukemias affect elderly people, who are often not fit to survive aggressive chemotherapy. Therefore, there is a need of milder treatment, named differentiation therapy, which might simulate the immune system of the patient. 1,25-Dihydroxyvitamin D, or low-calcemic analogs of this compound, were proposed as supporting therapy in acute leukemias. (2) Bone marrow blasts from patients with hematological malignancies, and leukocytes from healthy volunteers were ex vivo exposed to 1,25-dihydroxyvitamin D, and then their genomes and transcriptomes were investigated. (3) Our analysis indicates that 1,25-dihydroxyvitamin D regulates in blood cells predominantly genes involved in immune response, such as CAMP (cathelicidin antimicrobial peptide), CP (ceruloplasmin), CXCL9 (C-X-C motif chemokine ligand 9), CD14 (CD14 molecule) or VMO1 (vitelline membrane outer layer 1 homolog). This concerns blood cells from healthy people, as well as blasts from patients with hematological malignancies. In addition, in one patient, 1,25-dihydroxyvitamin D significantly downregulated transcription of genes responsible for cell division and immortalization. (4) In conclusion, the data presented in this paper suggest that addition of 1,25-dihydroxyvitamin D to the currently available treatments would stimulate immune system, inhibit proliferation and reduce immortal potential of blasts. [ABSTRACT FROM AUTHOR]

We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the 'three Es of cancer immunoediting': Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a 'human readable' statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct 'PCF signature' that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples. Author summary: Multiplex images provide exquisitely detailed information about the spatial distribution and intensity of up to 40 biomarkers within two-dimensional tissue regions, creating challenges and opportunities for quantitative analysis. Although stain intensities are measured on a continuous scale, they are typically converted into discrete labels to simplify subsequent spatial analysis. In this paper we propose a new spatial statistic, the weighted pair correlation function (wPCF), which exploits, rather than neglects, the continuous variation in stain intensity contained in multiplex images, and can characterise both spatial and phenotypic heterogeneity. As proof-of-principle, we apply the wPCF to synthetic data that resemble multiplex images of solid tumours. We generate data from an agent-based model (ABM) that simulates macrophage-tumour interactions. The wPCF shows how the continuous label describing macrophage phenotype is spatially related to categorical labels associated with tumour cells and blood vessels. We demonstrate that correlation functions can categorise spatial relationships in a manner which is interpretable and quantitative. The methods we present can be used to analyse both ABM simulations and multiplex imaging data, with applications that go beyond macrophage phenotype to include other biological processes that exhibit continuous variation (e.g., cancer cell stemness, biomarkers for T-cell exhaustion, and levels of oxygenation). [ABSTRACT FROM AUTHOR]

Micro-ribonucleic acids are control gene expression in cells. They represent the changed cellular states that occur can be employed as biomarkers. Red blood cells alter biochemically and morphologically while they are being stored, which could be detrimental to transfusion. The effect of storage on the erythrocyte transcriptome is not mostly investigated. Because adult erythrocytes lack a nucleus, it has long been assumed that they lack deoxyribonucleic acid and ribonucleic acid. On the other hand, erythrocytes contain a diverse range of ribonucleic acids, of which micro-ribonucleic acids are key component. Changes in this micro-ribonucleic acid protect cells from death and adenine triphosphate depletion, and they are linked to specific storage lesions. As a result, changes in micro-ribonucleic acid in stored erythrocytes may be used as a marker to assess the quality and safety of stored erythrocytes. Therefore, this review ams to review the role of microRNA in stored packed red blood cells as quality indicator. Google Scholar, PubMed, Scopus, and Z-libraries are used for searching articles and books. The article included in this paper was written in the English language and had the full article. During long storage of RBCs, miR-16-2-3p, miR-1260a, miR-1260b, miR-4443, miR-4695-3p, miR-5100, let-7b, miR-16, miRNA-1246, MiR-31-5p, miR-203a, miR-654-3p, miR-769-3p, miR-4454, miR-451a and miR-125b- 5p are up regulated. However, miR-96, miR-150, miR-196a, miR-197, miR-381 and miR-1245a are down regulated after long storage of RBCs. The changes of this microRNAs are linked to red blood cell lesions. Therefore, micro-ribonucleic acids are the potential quality indicator in stored packed red blood cells in the blood bank. Particularly, micro-ribonucleic acid-96 is the most suitable biomarker for monitoring red blood cell quality in stored packed red blood units. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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.)