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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]

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]

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]

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]

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.)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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