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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]

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]

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]

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]

Gottingen minipigs mirror the physiological radiation response observed in humans and hence make an ideal candidate model for studying radiation biodosimetry for both limited-sized and mass casualty incidents. We examined the whole blood gene expression profiles starting one day after total-body irradiation with increasing doses of gamma-rays. The minipigs were monitored for up to 45 days or time to euthanasia necessitated by radiation effects. We successfully identified dose- and time-agnostic (over a 1-7 day period after radiation), survival-predictive gene expression signatures derived using machine-learning algorithms with high sensitivity and specificity. These survival-predictive signatures fare better than an optimally performing dose-differentiating signature or blood cellular profiles. These findings suggest that prediction of survival is a much more useful parameter for making triage, resource-utilization and treatment decisions in a resource-constrained environment compared to predictions of total dose received. It should hopefully be possible to build such classifiers for humans in the future., (© 2021. The Author(s).)

Dielectrophoresis technology is applied to microfluidic chips to achieve microscopic control of cells. Currently, microfluidic chips based on dielectrophoresis have certain limitations in terms of cell sorting species, in order to explore a microfluidic chip with excellent performance and high versatility. In this paper, we designed a microfluidic chip that can be used for continuous cell sorting, with the structural design of a curved channel and curved double side electrodes. CM factors were calculated for eight human healthy blood cells and cancerous cells using the software MyDEP, the simulation of various blood cells sorting and the simulation of the joule heat effect of the microfluidic chip were completed using the software COMSOL Multiphysics. The effect of voltage and inlet flow velocity on the simulation results was discussed using the control variables method. We found feasible parameters from simulation results under different voltages and inlet flow velocities, and the feasibility of the design was verified from multiple perspectives by measuring cell movement trajectories, cell recovery rate and separation purity. This paper provides a universal method for cell, particle and even protein sorting. [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]

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]

The analysis of cells frozen within the International Space Station (ISS) will provide crucial insights into the impact of the space environment on cellular functions and properties. The objective of this study was to develop a method for cryopreserving blood cells under the specific constraints of the ISS. In a ground experiment, mouse blood was directly mixed with a cryoprotectant and gradually frozen at −80 °C. Thawing the frozen blood sample resulted in the successful recovery of viable mononuclear cells when using a mixed solution of dimethylsulfoxide and hydroxyethyl starch as a cryoprotectant. In addition, we developed new freezing cases to minimize storage space utilization within the ISS freezer. Finally, we confirmed the recovery of major mononuclear immune cell subsets from the cryopreserved blood cells through a high dimensional analysis of flow cytometric data using 13 cell surface markers. Consequently, this ground study lays the foundation for the cryopreservation of viable blood cells on the ISS, enabling their analysis upon return to Earth. The application of this method in ISS studies will contribute to understanding the impact of space environments on human cells. Moreover, this method may find application in the cryopreservation of blood cells in situations where research facilities are inadequate. [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]

Leukocytes are mainly composed of neutrophils, basophils, eosinophils, monocytes, and lymphocytes. The number and proportion of different types of leukocytes correspond to different diseases, so an accurate segmentation of each type of leukocyte is important for the diagnosis of disease. However, the acquisition of blood cell images can be affected by external environmental factors, which can lead to variable light and darkness, complex backgrounds, and poorly characterized leukocytes. To address the problem of complex blood cell images collected under different environments and the lack of obvious leukocyte features, a leukocyte segmentation method based on improved U-net is proposed. First, adaptive histogram equalization-retinex correction was introduced for data enhancement to make the leukocyte features in the blood cell images clearer. Then, to address the problem of similarity between different types of leukocytes, convolutional block attention module is added to the four skip connections of U-net to focus the features from spatial and channel aspects, so that the network can quickly locate the high-value information of features in different channels and spaces. It avoids the problem of large amount of repeated computation of low-value information, prevents overfitting, and improves the training efficiency and generalization ability of the network. Finally, to solve the problem of class imbalance in blood cell images and to better segment the cytoplasm of leukocytes, a loss function combining focal loss and Dice loss is proposed. We use the BCISC public dataset to verify the effectiveness of the proposed method. The segmentation of multiple leukocytes using the method of this paper can achieve 99.53% accuracy and 91.89% mIoU. The experimental results show that the method achieves good segmentation results for lymphocytes, basophils, neutrophils, eosinophils, and monocytes. [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]

Despite proven scientific quality of menstrual blood mesenchymal cells, research and science output using those cells is still incipient, which suggests there is a resistance to the study of this type of cell by scientists, and a lack of attention to its potential for cell therapy, regenerative medicine and bioengineering. This study analyzes the literature about the menstrual blood mesenchymal stromal/stem cells (mbMSC) on the PubMed database between 2008–2020 and the social attention it received on Twitter. A comparative analysis showed that mbMSC accounts for a very small portion of mesenchymal cell research (0.25%). Most first authors are women (53.2%), whereas most last authors are men (63.74%), reinforcing an already known, and still significant, gender gap between last and corresponding authors. Menstrual blood tends to be less used in experiments and its scientific value tends to be underestimated, which brings gender bias to a technical and molecular level. Although women are more positive in the mbMSC debate on Twitter, communication efforts toward visibility and public interest in menstrual cells has room to grow. [ABSTRACT FROM AUTHOR]

Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images. [ABSTRACT FROM AUTHOR]

Blood is essential to life. The number of blood cells plays a significant role in observing an individual's health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitates early disease diagnosis, presenting advantages over traditional medical imaging techniques. Addressing challenges such as the computational burden of existing convolutional neural networks (CNNs) and imbalances in sample data, this paper introduces a lightweight GhostMRNet for the classification of microscopic hyperspectral images of human blood cells. The proposed model employs Ghost Modules to replace conventional convolutional layers and a cascading approach with small convolutional kernels for multiscale feature extraction, aiming to enhance feature extraction capabilities while reducing computational complexity. Additionally, an SE (Squeeze-and-Excitation) module is introduced to selectively allocate weights to features in each channel, emphasizing informative features and efficiently achieving spatial–spectral feature extraction in microscopic hyperspectral imaging. We evaluated the performance of the proposed GhostMRNet and compared it with other state-of-the-art models using two real medical hyperspectral image datasets. The experimental results demonstrate that GhostMRNet exhibits a superior performance, with an overall accuracy (OA), average accuracy (AA), and Kappa coefficient reaching 99.965%, 99.565%, and 0.9925, respectively. In conclusion, the proposed GhostMRNet achieves a superior classification performance at a smaller computational cost, thereby providing a novel approach for blood cell detection. [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]

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient–provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019–2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019–2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence. [ABSTRACT FROM AUTHOR]

The paper considers the problem of automating the creation of an annotated dataset for further use in a system for detecting, localizing and classifying blood cells in an image using deep learning. The subject of the research is the processes of digital image processing for object detection and localization. Objective. The aim of this study is to create a pipeline of digital image processing methods that can automatically generate an annotated set of blood smear images. This set will then be used to train and validate deep learning models, significantly reducing the time required by machine learning specialists. Method. The proposed approach for object detection and localization is based on digital image processing methods such as filtering, thresholding, binarization, contour detection, and filling. The pipeline for detection and localization includes the following steps: The given fragment of text describes a process that involves noise reduction, conversion to the HSV color model, defining a mask for white blood cells and platelets, detecting the contours of white blood cells and platelets, determining the coordinates of the upper left and lower right corners of white blood cells and platelets, calculating the area of the region inside the bounding box, saving the obtained data, and determining the most common color in the image; filling the contours of leukocytes and platelets with said color; defining a mask for red blood cells; defining the contours of red blood cells; determining the coordinates of the upper left and lower right corners of red blood cells; calculating the area of the region within the bounding box; entering data about the found objects into the dataframe; saving to a .csv file for future use. With an unlabeled image dataset and a generated .csv file using image processing libraries, any researcher should be able to recreate a labeled dataset. Results. The developed approach was implemented in software for creating an annotated dataset of blood smear images Conclusions. The study proposes and justifies an approach to automatically create a set of annotated data. The pipeline is tested on a set of unlabelled data and a set of labelled data is obtained, consisting of cell images and a .csv file with the attributes “file name”, “type”, “xmin”, “ymin”, “xmax”, “ymax”, “area”, which are the coordinates of the bounding box for each object. The number of correctly, incorrectly, and unrecognised objects is calculated manually, and metrics are calculated to assess the accuracy and quality of object detection and localisation. [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]

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]

In modern clinical medicine, the important information of red blood cells, such as shape and number, is applied to detect blood diseases. However, the automatic recognition problem of single cells and adherent cells always exists in a densely distributed medical scene, which is difficult to solve for both the traditional detection algorithms with lower recognition rates and the conventional networks with weaker feature extraction capabilities. In this paper, an automatic recognition method of adherent blood cells with dense distribution is proposed. Based on the Faster R-CNN, the balanced feature pyramid structure, deformable convolution network, and efficient pyramid split attention mechanism are adopted to automatically recognize the blood cells under the conditions of dense distribution, extrusion deformation, adhesion and overlap. In addition, the Align algorithm for region of interest also contributes to improving the accuracy of recognition results. The experimental results show that the mean average precision of cell detection is 0.895, which is 24.5% higher than that of the original network model. Compared with the one-stage mainstream networks, the presented network has a stronger feature extraction capability. The proposed method is suitable for identifying single cells and adherent cells with dense distribution in the actual medical scene. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiplicity of positive periodic solutions of a discrete blood cell production model with impulse effects. This model is described by periodic coefficients and time delays, as well as nonlinear feedback with exponential terms. By employing the Krasnosel'skii fixed point theorem, we establish a sufficient condition for the existence of at least two positive periodic solutions. To this end, we construct solution transformation between an impulsive delay difference equation and the corresponding nonimpulsive delay difference equation. Aditionally, a solution representation of the positive periodic solution of the blood cell production model is presented. Moreover, a numerical example and its simulations are given to illustrate the main result. [ABSTRACT FROM AUTHOR]

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]