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[Display omitted] The complex spreading dynamics of blood on paper matrix is likely to be quantitatively altered with variations in the fractional occupancy of red blood cells in the whole blood (haematocrit). Here, we presented an apparently surprising observation that a finite volume blood drop undergoes a universal time-dependent spreading on a filter paper strip that is virtually invariant with its hematocrit level within physiologically healthy regime, though distinctively distinguishable from the spreading laws of blood plasma and water. Our hypothesis was ascertained by performing controlled wicking experiments on filter papers of different grades. Spreading of human blood samples of different haematocrit levels ranging between 15% and 51% and the plasma separated from therein were traced by combined high-speed imaging and microscopy. These experiments were complemented with a semi-analytical theory to decipher the key physics of interest. Our results unveiled the exclusive influence of the obstructing cellular aggregates in the randomly distributed hierarchically structured porous pathways and deciphered the role of the networked structures of the various plasma proteins that induced hindered diffusion. The resulting universal signatures of spontaneous dynamic spreading, delving centrally on the fractional reduction in the interlaced porous passages, provide novel design basis for paper-microfluidic kits in medical diagnostics and beyond. [ABSTRACT FROM AUTHOR]

In this work, fast isoelectric focusing (IEF) was successfully implemented on an open paper fluidic channel for simultaneous concentration and separation of proteins from complex matrix. With this simple device, IEF can be finished in 10 min with a resolution of 0.03 pH units and concentration factor of 10, as estimated by color model proteins by smartphone-based colorimetric detection. Fast detection of albumin from human serum and glycated hemoglobin (HBA1c) from blood cell was demonstrated. In addition, off-line identification of the model proteins from the IEF fractions with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was also shown. This PAD IEF is potentially useful either for point of care test (POCT) or biomarker analysis as a cost-effective sample pretreatment method. [ABSTRACT FROM AUTHOR]

Optical microscopy videos enable experts to analyze the motion of several biological elements. Particularly in blood samples infected with Trypanosoma cruzi (T. cruzi), microscopy videos reveal a dynamic scenario where the parasites' motions are conspicuous. While parasites have self-motion, cells are inert and may assume some displacement under dynamic events, such as fluids and microscope focus adjustments. This paper analyzes the trajectory of T. cruzi and blood cells to discriminate between these elements by identifying the following motion patterns: collateral, fluctuating, and pan–tilt–zoom (PTZ). We consider two approaches: i) classification experiments for discrimination between parasites and cells; and ii) clustering experiments to identify the cell motion. We propose the trajectory step dispersion (TSD) descriptor based on standard deviation to characterize these elements, outperforming state-of-the-art descriptors. Our results confirm motion is valuable in discriminating T. cruzi of the cells. Since the parasites perform the collateral motion, their trajectory steps tend to randomness. The cells may assume fluctuating motion following a homogeneous and directional path or PTZ motion with trajectory steps in a restricted area. Thus, our findings may contribute to developing new computational tools focused on trajectory analysis, which can advance the study and medical diagnosis of Chagas disease. [ABSTRACT FROM AUTHOR]

A platelet (thrombocyte) can be in two states, activated and inactivated. The paper analyzes the interaction of an inactive platelet cell with the wall of a blood vessel. The goal is to analyze and represent the dynamics of platelet cell interaction when a thrombus has not yet formed. The discrete element method (DEM) can be used for the presented model. The paper presents an analysis of the dependence of force and displacement. This test is an introduction to more advanced tests when a blood clot forms. [ABSTRACT FROM AUTHOR]

We propose a new, paper-based analytical device (PAD) for blood typing that allows for the simultaneous determination of ABO and Rh blood groups on the same device. The device was successfully fabricated by using a combination of wax printing and wax dipping methods. A 1:2 blood dilution was used for forward grouping, whereas whole blood could be used for reverse grouping. A 30% cell suspension of A-cells or B-cells was used for haemagglutination on the reverse grouping side. The total assay time was 10 min. The ratio between the distance of red blood cell movement and plasma separation is the criterion for agglutination and indicates the presence of the corresponding antigen or antibody. The proposed PAD has excellent reproducibility in that the same blood groups, namely A, AB, and O, were reported by using different PADs that were fabricated on the same day ( n =10). The accuracy for detecting blood group A ( n =12), B ( n =13), AB ( n =9), O ( n =14), and Rh ( n =48) typing were 92%, 85%, 89%, 93%, and 96%, respectively, in comparison with the conventional slide test method. The haematocrit of the sample affects the accuracy of the results, and appropriate dilution is suggested before typing. In conclusion, this study proposes a novel method that is straightforward, time-saving, and inexpensive for the simultaneous determination of ABO and Rh blood groups, which is promising for use in developing countries. [ABSTRACT FROM AUTHOR]

Gastrointestinal fistula, a complication of gastrointestinal cancer surgery, has a high mortality rate. Detection of both C-reactive protein (CRP) and prealbumin (PAB) is advantageous in the auxiliary diagnosis of postoperative complications. However, traditional detection methods are not capable of on-site rapid detection. In an attempt to overcome these challenges, a multifunctional origami-paper-based device (ePADs) was developed to simultaneously detect CRP and PAB in whole blood. After integration, functionalization, and modification, the electrochemical dual-parameter device was capable of separating blood cells and detecting target analytes. The plasma separation performance revealed a sample diffusion time of 75 s for a whole blood sample volume of 73.3 μL. The efficiency of the device in separating blood cells was 99.91%. Electrochemical results showed that the multifunctional device exhibited linearity between 5 pg mL−1 and 1 μg mL−1 for CRP (R2 = 0.990), and between 10 pg mL−1 and 1 μg mL−1 for PAB (R2 = 0.998). The limits of detection for CRP and PAB were 5 and 10 pg mL−1, respectively (S/N = 3). We also successfully evaluated the accuracy of the dual-parameter device with clinical whole blood samples. Based on these results, the multifunctional device can facilitate clinical detection and provide a new platform for domestic point-of-care testing. [ABSTRACT FROM AUTHOR]

Simple Summary: Oncolytic herpes simplex viruses (oHSVs) employ as natural entry receptors nectin1 or HVEM. In contrast, retargeted oHSVs (ReHVs) employ as an entry receptor a tumor-associated antigen that no longer interacts with nectin1/HVEM and, thus, spares normal cells. Prior to entry, both oHSVs and ReHVs attach to cells by interaction of gC and gB glycoproteins with heparan sulphates and chondroitin sulphates (glycosaminoglycans–GAGs). The systemic delivery of oncolytic viruses would be the ideal route for hard-to-reach or metastatic cancers that constitute unmet clinical needs. In an accompanying paper, we reported that the blood complement and the antiviral neutralizing antibodies represent the major blood factors that inactivate systemically administered ReHVs. Here, we asked whether ReHV adsorption to GAGs acts as a sink and subtracts the virus from circulation. We report that a genetic modification in gC, which reduced its interaction with GAGs, resulted in a longer half-life of circulating ReHV and a higher anticancer efficacy of systemically (but not intratumorally) administered ReHV. The role of the interaction with cell-surface glycosaminoglycans (GAGs) during in vivo HSV infection is currently unknown. The rationale of the current investigation was to improve the anticancer efficacy of systemically administered retargeted oHSVs (ReHVs) by decreasing their binding to GAGs, including those of endothelial cells, blood cells, and off-tumor tissues. As a proof-of-principle approach, we deleted seven amino acids critical for interacting with GAGs from the glycoprotein C (gC) of R-337 ReHV. The modification in the resulting R-399 recombinant prolonged the half-life in the blood of systemically administered R-399 and enhanced its biodistribution to tumor-positive lungs and to the tumor-negative liver. Ultimately, it greatly increased the R-399 efficacy against metastatic-like lung tumors upon IV administration but not against subcutaneous tumors upon IT administration. These results provide evidence that the increased efficacy seen upon R-399 systemic administration correlated with the slower clearance from the circulation. To our knowledge, this is the first in vivo evidence that the partial impairment of the gC interaction with GAGs resulted in a prolonged half-life of circulating ReHV, an increase in the amount of ReHV taken up by tissues and tumors, and, ultimately, an enhanced anticancer efficacy of systemically administered ReHV. [ABSTRACT FROM AUTHOR]

Simple Summary: Primordial germ cells (PGCs) play a critical role in the preservation of poultry genetic resources and transgenic research, but there are significant efficiency issues with isolating PGCs from chicken embryonic blood, especially from a single embryo. In this study, we developed a simple and rapid method to isolate PGCs from single chicken embryonic blood. This method is based on the characteristics of PGCs found in the study. That is, when co-cultured with chicken embryonic fibroblasts (CEFs) of passages two to three, PGCs can migrate to the lower layer of CEF through pores smaller than their diameter, which is called cell culture inserts/CEF adhesion method. The PGCs isolated using this method retain their stem cell characteristics and migration ability, and exhibit good proliferation efficiency. These cells can be directly utilized for subsequent transgenic experiments or for the preservation of germplasm resources. Primordial germ cells (PGCs) play a crucial role in preserving poultry genetic resources and conducting transgenic research. A system for the rapid isolation of PGCs from single chicken embryonic blood was established in this paper. We found that PGCs can migrate to the lower layer of chicken embryonic fibroblasts (CEFs) through pores smaller than their diameter, while blood cells cannot, when co-cultured with CEFs of passages two to three. Based on the characteristics of PGCs, we developed a new PGC isolation method (cell culture insert/CEF adhesion method) that utilizes a 3 μm cell culture insert and CEFs of passages two to three. Using this method, approximately 700 PGCs can be isolated from the blood of a single chicken embryo at Hamburger and Hamilton (H&H) stage 17 of development. The separation rate achieved was 87.5%, with a separation purity of 95%. The separation rate of this method was 41.4% higher than the common Percoll density gradient centrifugation method and 33.6% higher than lysis with ACK buffer. PGCs isolated from embryonic blood could proliferate 37-fold within 2 weeks when cultured in a feeder-free culture system. They also continued to express the SSEA-1 and DAZL proteins and retained the ability to migrate in vivo. Overall, PGCs separated using cell culture inserts/CEF adhesion method retain their stem cell characteristics and migration ability. PGCs also exhibit good proliferation efficiency, making them suitable for subsequent transgenic experiments or genetic resource preservation. [ABSTRACT FROM AUTHOR]

The pathophysiology of the severe course of COVID-19 is multifactorial and not entirely elucidated. However, it is well known that the hyperinflammatory response and cytokine storm are paramount events leading to further complications. In this paper, we investigated the vascular response in the pathophysiology of severe COVID-19 and aimed to identify novel biomarkers predictive of ICU admission. The study group consisted of 210 patients diagnosed with COVID-19 (age range: 18–93; mean ± SD: 57.78 ± 14.16), while the control group consisted of 80 healthy individuals. We assessed the plasma concentrations of various vascular factors using the Luminex technique. Then, we isolated RNA from blood mononuclear cells and performed a bioinformatics analysis investigating various processes related to vascular response, inflammation and angiogenesis. Our results confirmed that severe COVID-19 is associated with vWF/ADAMTS 13 imbalance. High plasma concentrations of VEGFR and low DPP-IV may be potential predictors of ICU admission. SARS-CoV-2 infection impairs angiogenesis, hinders the generation of nitric oxide, and thus impedes vasodilation. The hypercoagulable state develops mainly in the early stages of the disease, which may contribute to the well-established complications of COVID-19. [ABSTRACT FROM AUTHOR]

This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-the-art performance on both digital staining and classification tasks. [ABSTRACT FROM AUTHOR]

This paper proposes an improved target detection algorithm, SDE-YOLO, based on the YOLOv5s framework, to address the low detection accuracy, misdetection, and leakage in blood cell detection caused by existing single-stage and two-stage detection algorithms. Initially, the Swin Transformer is integrated into the back-end of the backbone to extract the features in a better way. Then, the 32 × 32 network layer in the path-aggregation network (PANet) is removed to decrease the number of parameters in the network while increasing its accuracy in detecting small targets. Moreover, PANet substitutes traditional convolution with depth-separable convolution to accurately recognize small targets while maintaining a fast speed. Finally, replacing the complete intersection over union (CIOU) loss function with the Euclidean intersection over union (EIOU) loss function can help address the imbalance of positive and negative samples and speed up the convergence rate. The SDE-YOLO algorithm achieves a mAP of 99.5%, 95.3%, and 93.3% on the BCCD blood cell dataset for white blood cells, red blood cells, and platelets, respectively, which is an improvement over other single-stage and two-stage algorithms such as SSD, YOLOv4, and YOLOv5s. The experiment yields excellent results, and the algorithm detects blood cells very well. The SDE-YOLO algorithm also has advantages in accuracy and real-time blood cell detection performance compared to the YOLOv7 and YOLOv8 technologies. [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]

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]

Extracellular vesicles (EVs) are important for intercellular signalling in multi-cellular organisms. However, the role of mature transfer RNAs (tRNAs) and tRNA fragments in EVs has yet to be characterised. This systematic review aimed to identify up-to-date literature on tRNAs present within human EVs and explores their potential clinical significance in health and disease. A comprehensive and systematic literature search was performed, and the study was conducted in accordance with PRISMA guidelines. Electronic databases MEDLINE and EMBASE were searched up until 1 January 2022. From 685 papers, 60 studies were identified for analysis. The majority of papers reviewed focussed on the role of EV tRNAs in cancers (31.7%), with numerous other conditions represented. Blood and cell lines were the most common EV sources, representing 85.9% of protocols used. EV isolation methods included most known methods, precipitation being the most common (49.3%). The proportion of EV tRNAs was highly variable, ranging between 0.04% to >95% depending on tissue source. EV tRNAs are present in a multitude of sources and show promise as disease markers in breast cancer, gastrointestinal cancers, and other diseases. EV tRNA research is an emerging field, with increasing numbers of papers highlighting novel methodologies for tRNA and tRNA fragment discovery. [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]

Background/Aim. Measles is a contagious disease with a good prognosis; however, severe complications may sometimes develop. C-reactive protein (CRP) and blood cells count-derived inflammatory indices -- granulocytelymphocyte ratio (GLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), mean platelet volume (MPV)/platelet count ratio (MPR), red blood cell distribution width (RDW), and MPV are the indicators related to the clinical outcome in various inflammatory diseases. The aim of the study was to analyze the values of CRP, blood cell count, GLR, PLR, MLR, MPR, RDW, and MPV in measles-affected children compared to healthy controls and between measles-affected children with complicated and severely complicated measles form. A particular aim of the paper was to assess the suitability of inflammatory-derived markers for predicting the severity of the disease. Methods. The study included 55 measlesaffected children who developed complications (examination group), while the control group included 30 healthy children. The first peripheral blood count, obtained on the first hospitalization day (before treatment), was used for further analyses. Results. The white blood cells, lymphocytes, monocytes, and platelets count were significantly lower, while GLR, PLR, MPR, and CRP were significantly higher in measles-affected children (p < 0.05). In severely complicated measles form, significantly higher values of granulocytes, CRP, GLR, and PLR were documented, including lower lymphocytes (p < 0.05). A linear regression analysis showed that CRP was the only indicator with predictive significance for the severity of the course of measles. Conclusion. The blood cell count-derived inflammatory indices should not be crucial in assessing the severity of measles in children. CRP was the most valuable predictive factor for the development of the severe course of measles in measles-affected children. [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]

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]

Leukemia is type of cancer in blood which impacts the lymphatic framework and the bone marrow and also impacts white blood cells. Leukemia, in contrast to other types of cancer, does not produce solid tumors; instead, it produces a huge number of aberrant white blood cells that crowd out the healthy blood cells. Machine learning algorithms that are widely utilized in the treatment of leukemia, whether it is to classify the various forms of leukemia or to determine whether a patient has the disease. It is a malignant kind of cancer that results in a number of medical issues. Expert hematologists and pathologists manually examine blood samples under the microscope to make a diagnosis. Techniques like image processing and pattern recognition can be utilized to help these experts. In order to attain excellent performance in the categorization of malignant leukocytes challenge, this paper suggests straightforward modifications to conventional neural network topologies. Consequently, there is considerable interest in the trustworthy and precise recognition of nonmalignant and malignant cells. Leukemia can be automatically detected using computer-aided diagnostic (CAD) models, which can help doctors and be useful for leukemia early identification. In this single-center study, we attempted to develop a deep learning model for classification of leukemic B-lymphoblasts. Data augmentation methods were utilized to manage the little dataset size and an exchange learning technique was utilized to accelerate learning and upgrade the presentation of the recommended network in order to create a trustworthy and accurate deep learner. The outcomes demonstrate that our suggested approach surpassed seperate networks with a test accuracy of 95.59% in the Leukemic B-lymphoblast examination, and was caable to merge characteristics extracted from the top deep learning models. [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]

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]

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]

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images. [ABSTRACT FROM AUTHOR]

Highlights • Hematopoiesis model with multiple unimodal production functions is proposed to elucidate the dynamics of blood cells. • Periodic behavior of the density of mature blood cells is investigated. • It is easy to check whether the obtained condition is satisfied or not. • Existence region of positive periodic solutions is clarified by using the continuation theorem of coincidence degree theory. • Positive parameters and variable coefficients of our model can be estimated from clinical data on health and blood disease. Abstract A discrete model describing the increase and decrease of blood cells is considered in this paper. This hematopoiesis model is a discretization of a delay differential equation with unimodal production function whose coefficients and delays are periodic discrete functions with ω -period. This paper is concerned with the existence of positive ω -periodic solutions. Our results are proved by using the well-known continuation theorem of coincidence degree theory. The existence range of the positive ω -periodic solutions is also clarified. A concrete example and its simulation are also given to illustrate our result. Finally, we examine how positive numbers and coefficients making up our model influence the upper and lower limits of blood cell counts. [ABSTRACT FROM AUTHOR]

Malaria is known as an acute febrile disease caused by the bite of female Anopheles mosquitoes, and it manifests itself with symptoms such as headache, fever, chills, vomiting, and fatigue. The diagnosis of malaria is still based on manual identification of Plasmodium parasitized cells in microscopic examinations of blood cells known as parasite-based microscopy diagnostic testing. The accuracy of this manual diagnosis method is clearly affected by the level of microscopist's experience, which makes this diagnosis method susceptible to manual error and time consuming. Diagnoses of diseases made using deep-learning methods have had great repercussions in the medical world, especially in recent years; and this indicates that the diagnosis of malaria can also be achieved by deep-learning methods. On the basis of this fact, this paper presents a novel deep-learning-based malaria disease detection technique. A convolutional neural network (CNN) architecture, which has 20 weighted layers is designed and proposed to identify parasitized microscopic images from uninfected microscopic images. A total of 27,558 thin blood cell images were used to train and test the CNN model, and 95.28% overall accuracy was obtained. The experimental results on large clinical dataset show the effectiveness of the proposed deep-learning method for malaria disease detection. [ABSTRACT FROM AUTHOR]

The partial success of tumor immunotherapy induced by checkpoint blockade, which is not antigen-specific, suggests that the immune system of some patients contain antigen receptors able to specifically identify tumor cells. Here we focused on T-cell receptor (TCR) repertoires associated with spontaneous breast cancer. We studied the alpha and beta chain CDR3 domains of TCR repertoires of CD4 T cells using deep sequencing of cell populations in mice and applied the results to published TCR sequence data obtained from human patients. We screened peripheral blood T cells obtained monthly from individual mice spontaneously developing breast tumors by 5 months. We then looked at identical TCR sequences in published human studies; we used TCGA data from tumors and healthy tissues of 1,256 breast cancer resections and from 4 focused studies including sequences from tumors, lymph nodes, blood and healthy tissues, and from single cell dataset of 3 breast cancer subjects. We now report that mice spontaneously developing breast cancer manifest shared, Public CDR3 regions in both their alpha and beta and that a significant number of women with early breast cancer manifest identical CDR3 sequences. These findings suggest that the development of breast cancer is associated, across species, with biomarker, exclusive TCR repertoires. Author summary: More than 1,100 cancer treatments are currently in clinical testing. Of these, 295 are immune-oncology medicines and vaccines. For Checkpoint Immunotherapy alone, 52,539 patients were enrolled in 2017. Of these treatments, 108 are for breast cancer, the leading cancer diagnosed in women. Although checkpoint immunotherapy has yet to be proven beneficial in most types of breast cancer, current observations of which specific T cell go into the tumor, and how these cells are connected to the response to treatment, have lead to a new understating of the role of the immune system. Yet, and in spite of this intense research into the role of T cells in tumors, very little is known about how clones of T cells behave during tumor formation. The paper we present here studies this clonal behavior over time. We study it during mammary tumor development in mice. We see that a set of T cell clones, which comes from a diversity of different nucleotide sequences, stands out in its behavior compared with other T cell clones. Importantly, we show that these mouse T cell clones have a strong connection to human breast cancer. The specific identification of a subset of T cell clones, together with the behavior of these clones over time, is critical to our understanding of the effect of modifications to the immune system, such as the modifications made by immunotherapy treatments, and to our understanding of how to better control the behavior of the immune system. [ABSTRACT FROM AUTHOR]

Background: Hematopoietic stem cell (HSC) transplantation is an effective treatment strategy for many types of diseases. Peripheral blood (PB) is the most commonly used source of bone marrow (BM)-derived stem cells for current HSC transplantation. However, PB usually contains very few HSCs under normal conditions, as these cells are normally retained within the BM. This retention depends on the interaction between the CXC chemokine receptor 4 (CXCR4) expressed on the HSCs and its natural chemokine ligand, stromal cell-derived factor (SDF)-1α (also named CXCL12) present in the BM stromal microenvironment. In clinical practice, blocking this interaction with a CXCR4 antagonist can induce the rapid mobilization of HSCs from the BM into the PB. Methods: C3H/HEJ, DBA/2, CD45.1+, and CD45.2+ mice and monkeys were employed in colony-forming unit (CFU) assays, flow cytometry assays, and competitive/noncompetitive transplantation assays, to assess the short-term mobilization efficacy of HF51116 and the long-term repopulating (LTR) ability of HSCs. Kinetics of different blood cells and the concentration of HF51116 in PB were also explored by blood routine examinations and pharmacokinetic assays. Results: In this paper, we report that a novel small molecule CXCR4 antagonist, HF51116, which was designed and synthesized by our laboratory, can rapidly and potently mobilize HSCs from BM to PB in mice and monkeys. HF51116 not only mobilized HSCs when used alone but also synergized with the mobilizing effects of granulocyte colony-stimulating factor (G-CSF) after co-administration. Following mobilization by HF51116 and G-CSF, the long-term repopulating (LTR) and self-renewing HSCs were sufficiently engrafted in primary and secondary lethally irradiated mice and were able to rescue and support long-term mouse survival. In monkeys, HF51116 exhibited strong HSC mobilization activity and quickly reached the highest in vivo blood drug concentration. Conclusions: These results demonstrate that HF51116 is a new promising stem cell mobilizer which specifically targets CXCR4 and merits further preclinical and clinical studies. [ABSTRACT FROM AUTHOR]

Established cell lines are widely used in research, however an appealing question is the comparability of the cells between various laboratories, their characteristics and stability in time. Problematic is also the cell line misidentification, genetic and phenotypic shift or Mycoplasma contamination which are often forgotten in research papers. The monocyte/macrophage-like cell line RAW 264.7 has been one of the most commonly used myeloid cell line for more than 40 years. Despite its phenotypic and functional stability is often discussed in literature or at various scientific discussion panels, their stability during the consecutive passages has not been confirmed in any solid study. So far, only a few functional features of these cells have been studied, for example their ability to differentiate into osteoclasts. Therefore, in the present paper we have investigated the phenotype and functional stability of the RAW 264.7 cell line from passage no. 5 till passage no. 50. We found out that the phenotype (expression of particular macrophage-characteristic genes and surface markers) and functional characteristics (phagocytosis and NO production) of RAW 264.7 cell line remains stable through passages: from passage no. 10 up to passage no. 30. Overall, our results indicated that the RAW 264.7 cell line should not be used after the passage no. 30 otherwise it may influence the data reliability. [ABSTRACT FROM AUTHOR]

Kaempferol (kae) and its glycosides are widely distributed in nature and show multiple bioactivities, yet few reports have compared them. In this paper, we report the antitumor, antioxidant and anti-inflammatory activity differences of kae, kae-7-O-glucoside (kae-7-O-glu), kae-3-O-rhamnoside (kae-3-O-rha) and kae-3-O-rutinoside (kae-3-O-rut). Kae showed the highest antiproliferation effect on the human hepatoma cell line HepG2, mouse colon cancer cell line CT26 and mouse melanoma cell line B16F1. Kae also significantly inhibited AKT phosphorylation and cleaved caspase-9, caspase-7, caspase-3 and PARP in HepG2 cells. A kae-induced increase in DPPH and ABTS radical scavenging activity, inhibition of concanavalin A (Con A)-induced activation of T cell proliferation and NO or ROS production in LPS-induced RAW 264.7 macrophage cells were also seen. Kae glycosides were used to produce kae via environment-friendly enzymatic hydrolysis. Kae-7-O-glu and kae-3-O-rut were hydrolyzed to kae by β-glucosidase and/or α-L-rhamnosidase. This paper demonstrates the application of enzymatic catalysis to obtain highly biologically active kae. This work provides a novel and efficient preparation of high-value flavone-related products. [ABSTRACT FROM AUTHOR]

Leukocytes are blood cells involved in the immune response. Leukocytes can be classified into three main groups (granulocytes, lymphocytes, and monocytes), and the measurement of the relative prevalence of each subgroup, termed differential leukocyte count, is an important clinical parameter in the diagnosis and prognosis of various health anomalies. Currently, differential leukocyte counts are obtained either manually through labor-intensive blood smears or by employing bulky hematology analyzers. Both of these measurements require a laboratory setup and hence are not amenable for decentralized and point-of-care settings. In this paper, we introduce a microfluidic assay with an electronic readout for differential leukocyte count assays. Our device classifies leukocyte subgroups based on the differential CD33 expression on cell membranes. In the device, each subgroup is differentially immunocaptured in different microfluidic chambers, while the cell capture rates are quantified by an integrated electronic sensor network. When benchmarked against a commercial hematology analyzer, our device could identify leukocyte subpopulations with differences of <6% for granulocytes and lymphocytes, and a <3% for monocytes. These results demonstrate the potential of our technique as an accurate method to perform the widely-employed hematology assay for point-of-care applications without the need for sample labeling. [2020-0197] [ABSTRACT FROM AUTHOR]

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows. Author summary: Combining data from many different studies is an attractive way of capturing new aspects of the biology being studied. Biological variance attributable to cell type, cellular niche, origin, disease status or environmental stimuli is the basis of most small-n transcriptome studies. In aggregation, these promise to capture emergent dimensions of a biology that is not possible to view from any individual study. However biological signal is easily swamped by technical artifact, especially when data is generated on platforms with profoundly different data structures. This is the case when comparing microarray data to RNAseq, or RNAseq to single cell profiling. Consequently, transcriptome atlases are generally comprised from a small number of donors/conditions surveyed using one technology platform. In this paper we present a simple and scalable data integration method that is platform agnostic. We provide a proof-of-principle by constructing an atlas of blood cells that combines many data sets measured on different platforms, and that in combination, recapitulates the known blood hierarchy. The atlas provides a reference to compare external samples to, allowing users to benchmark new derivation or isolation methods. It also provides a reference point for new data types, such as the classification of single cells. The approach allows for FAIR data reuse and robust identification of molecular signatures across multiple studies and experimental conditions. [ABSTRACT FROM AUTHOR]

Introduction: False-negative malaria rapid diagnostic test (RDT) results amongst symptomatic malaria patients are detrimental as they could lead to ineffective malaria case management. This study determined the nationwide contribution of parasites with Pfhrp2 and Pfhrp 3 gene deletions to false negative malaria RDT results in Ghana. Methods: This was a cross sectional study where whole blood (~2 ml) was collected from patients presenting with malaria symptoms at 100 health facilities in all the regions in Ghana from May to August 2018. An aliquot of the blood was used to prepare thin and thick blood smears, filter paper blood spots (DBS) and spot a PfHRP 2 RDT kit. The remaining blood was separated into plasma and blood cells and stored at -20°C. Plasmodium parasite density and species identity was estimated from the blood smears. Plasmodium falciparum specific 18S rRNA PCR, merozoite surface protein (msp 1) and glutamate rich protein (glurp) gene PCR were used to identify P. falciparum positive samples, which were subjected to Pfhrp 2/3 exon1-2 and exon2 genotyping. Results: Of the 2,860 microscopically P. falciparum positive patients analyzed, 134 (4.69%) had false negative P. falciparum specific RDT results. Samples for PCR analysis was available for 127 of the false negative patients, and the analysis identified 116 (91.3%) as positive for P. falciparum. Only 58.1% (79/116) of the false negative RDT samples tested positive by msp 1 and glurp PCR. Genotyping of exon 1–2 and exon 2 of the Pfhrp 2 gene identified 12.9% (10/79) and 39.5% (31/79) of samples respectively to have deletions. Genotyping exon 1–2 and exon 2 of the Pfhrp 3 gene identified 15.2% (12/79) and 40.5% (32/79) of samples respectively to have deletions. Only 5% (4/79) of the false negative samples had deletions in both exon 1–2 and exon 2 of the Pfhrp 2 gene. Out of the 49 samples that tested positive for aldolase by luminex, 32.6% (16/49) and) had deletions in Pfhrp 2 exon 2 and 2% (1/49) had deletions in both exon 2 and exon 1–2 of the Pfhrp 2 gene. Conclusions: The low prevalence of false negative RDT test results provides assurance that PfHRP 2 based malaria RDT kits remain effective in diagnosing symptomatic malaria patients across all the Regions of Ghana. Although there was a low prevalence of parasites with deletions in exon 2 and exon 1–2 of the Pfhrp 2 gene the prevalence of parasites with deletions in Pfhrp 2 exon 2 was about a third of the false negative RDT results. The need to ensure rapid, accurate and reliable malaria diagnosis requires continuous surveillance of parasites with Pfhrp 2 gene deletions. [ABSTRACT FROM AUTHOR]

The main reason for the weakening of the body of rabbits is a deficiency of nutrients and biologically ac􀆟ve substances in their diet. The search for non-toxic and highly effec􀆟ve preven􀆟ve drugs of complex ac􀆟on that have a posi􀆟ve effect on the metabolism of minerals in rabbits is of keen interest. Here we present the results of the study of morphological and biochemical parameters of the blood as well as the content of chemical elements in blood plasma in New Zealand White rabbits on the farm in Kyiv region. The paper presents the materials for determining the biochemical status of the body in fa􀆩ening rabbits for the preven􀆟on of microelementoses using a new experimental environmentally friendly tool. The content of total protein, albumin, glucose, total calcium, inorganic phosphorus, total bilirubin, urea, crea􀆟nine, TBA-ac􀆟ve products, Fe, Mn, Cu, Zn, Co and the ac􀆟vity of ALT, AST, LF, and HGT in the blood of New Zealand White rabbits in the first and 50th days of the experiment (before and a􀅌er the use of biologically ac􀆟ve addi􀆟ve "Huminorm plus") are presented here for the first 􀆟me. The use of this addi􀆟ve had a posi􀆟ve effect on haematopoiesis, protein and mineral metabolism, and func􀆟onal status of the liver, which indicates its high efficiency for the preven􀆟on of micronutrient deficiency. "Huminorm Plus" s􀆟mulates poten􀆟al body growth in rabbits up to the age of 90 days. [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]

Image-processing methods are applied in many environments to solve various practical problems. Recently, considerable research has been conducted on analysing retinal images, which show the blood vessels in the human eye, to detect diseases in patients. Various diseases related to the eyes can be located by detecting changes in the retinal blood cells. This paper proposes two analysis methods, namely the fuzzy c-means and neuro-fuzzy methods, which analyse optical coherence tomography images of retinal blood cells. The system was implemented in a MATLAB experimental setup, while the efficiency was evaluated with respect to time and accuracy. The performance analysis of the fuzzy c-means and neuro-fuzzy systems indicates that the neuro-fuzzy system has a higher accuracy (93.16%) and a lower processing time (2 s) than the k-means clustering, expectation maximisation, fuzzy clustering and neural network methods. [ABSTRACT FROM AUTHOR]

Negative magnetophoresis is a novel and attractive method for continuous microparticle sorting inside a magnetic medium. In this method, diamagnetic particles are sorted based on their sizes using magnetic buoyancy force and without any labeling process. Although this method provides some attractive features, such as low-cost fabrication and ease of operation, there are some obstacles that adversely affect its performance, especially for biological applications. Most types of magnetic media, such as ferrofluids, are not biocompatible, and the time-consuming process of sample preparation can be threatening to the viability of the cells within the sample. Furthermore, in this method, both the target and non-target particles are affected by the magnetic field, and therefore, a high separation efficiency cannot be achieved. In this paper, to isolate the abnormal cells from the other blood cells, a microfluidic device was designed using numerical simulation. This model utilizes negative magnetophoresis on a rotating disk, and to reduce the exposure time of the cells inside the magnetic medium, a micromixer is embedded in the upstream of the separator which rapidly prepares the sample. In this part, diluted blood sample and ferrofluid are mixed together utilizing magnetic force. Afterward, the separator sorts the cells into multiple outlets using magnetic buoyancy force as well as centrifugal force. The numerical procedure employed in this study shows that the proposed model is able to recover ∼100% of the abnormal cells from a particular outlet for binary and ternary separation of the cells with high throughputs. Although this percentage of separation may be lower in reality, the optimization of the proposed design by the numerical method can avoid trial-and-error during costly and time-consuming experiments. Also, the device proposed in this study reduces the exposure time of the cells inside the ferrofluid to just a few seconds, which can improve cell viability. [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 Epstein-Barr virus (EBV) induces B-cell proliferation with high efficiency through expression of latent proteins and microRNAs. This process takes place in vivo soon after infection, presumably to expand the virus reservoir, but can also induce pathologies, e.g. an infectious mononucleosis (IM) syndrome after primary infection or a B-cell lymphoproliferation in immunosuppressed individuals. In this paper, we investigated the growth characteristics of EBV-infected B-cells isolated from transplant recipients or patients with IM. We found that these cells grew and withstood apoptosis at highly variable rates, suggesting that the expansion rate of the infected B-cells widely varies between individuals, thereby influencing the size of the B-cell reservoir and the ability to form tumors in infected individuals. All viruses investigated were type 1 and genetically close to western strains. EBV-infected B-cells expressed the transforming EBV latent genes and microRNAs (miRNAs) at variable levels. We found that the B-cell growth rates positively correlated with the BHRF1 miRNA levels. Comparative studies showed that infected B-cells derived from transplant recipients with iEBVL on average expressed higher levels of EBV miR-BHRF1 miRNAs and grew more rapidly than B-cells from IM patients, suggesting infection by more transforming viruses. Altogether, these findings suggest that EBV infection has a highly variable impact on the B-cell compartment that probably reflects the genetic diversity of both the virus and the host. It also demonstrates the unexpected finding that B-cells from different individuals can grow at different speed under the influence of the same virus infection. [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]

Introduction: Platelets are smallest blood cells of discoid or round shape and are cytoplasmic fragments of megakaryocytes. Platelets consist of 3 types of granules: alpha granules, dense granules and lysosomes. Granule secretion releases coagulation factors, growth factors, cytokines, and a number of proteolytic enzymes.Platelets contain a number of receptors known as platelet agonists. Basic and most studied role of platelets is in hemostasis process. The aim of this paper is to point on platelet function unrelated to hemostasis. Matherials and methoods: Papers on other recognized functions of platelets were searched for in biomedical journals idexed in MEDLINE form 2004 to 2016. Topic: This paper studies less known platelet functions becoming subject of interest with the development of applied science. Platelets participate in infammation by releasing proinflammatory mediators (CD154, CD40L). Complement activation via Pselectin, platelet-generates immunomodulatory effect. CD40L accelerates releasing RANTES protein leading to intensified activation of T-lymphocytes. During embryonic development, platelets allow blood and lymph vessels separation by activating CLE-2 receptor and ligand PDPN. Platelets alleviate migration and invasiveness of tumor cells, contribute to disease progression and development of metastases. Platelets affect maturation of follicles and oocytes and have important role in embryo implantation process and placentation. Conclusion: Based on these findings, conclusion imposes platelets as not only active participants in the hemostasis process but as having significant role in inflammation, unspecified and specified body defending, tumor biology, embryonic development and in female reproductive system regulation. Numerous roles of platelets open wide range for the new drugs' operation. Their specific characteristic is the basis for the personalized Clinical pharmacology development and possibility of applying specific drug as polyindicative therapeutic agent. [ABSTRACT FROM AUTHOR]

The subset of pro-inflammatory B cells, called late memory, tissue-like or double negative (DN), accumulates in the blood of elderly individuals. Here we show that DN B cells do not proliferate and do not make antibodies to influenza antigens, but they secrete antibodies with autoimmune reactivity, in agreement with their membrane phenotype (CD95+CD21-CD11c+) and their spontaneous expression of the transcription factor T-bet. These cells also increase in the blood of individuals with obesity and autoimmune diseases, but causative mechanisms and signaling pathways involved are known only in part. In the present paper we compare frequencies and metabolic requirements of these cells in the blood of healthy individuals of different ages and in the blood and the subcutaneous adipose tissue (SAT) of individuals with obesity. Results show that DN B cells from young individuals have minimal metabolic requirements, DN B cells from elderly and obese individuals utilize higher amounts of glucose to perform autoimmune antibody production and enroll in aerobic glycolysis to support their function. DN B cells from the SAT have the highest metabolic requirements as they activate oxidative phosphorylation, aerobic glycolysis and fatty acid oxidation. DN B cells from the SAT also show the highest levels of ROS and the highest levels of phosphorylated AMPK (5’-AMP activated kinase) and Sestrin 1, both able to mitigate stress and cell death. This metabolic advantage drives DN B cell survival and function (secretion of autoimmune antibodies). [ABSTRACT FROM AUTHOR]