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The aim of this study was to evaluate how the inclusion of a blend of essential oils in milk replacer (MR) affects different outcomes of dairy heifers. The outcomes evaluated: feed intake, performance, body development, blood cells and metabolites, insulin-like growth factor-1 (IGF-1), rumen fermentation, fecal scores, and respiratory scores. All outcomes were evaluated during pre-weaning (4-60 d of age), and carry-over effects during post-weaning (61-90 d of age) periods. The experimental units utilized were 29 newborn Holstein × Gyr crossbred dairy heifers, with genetic composition of 5/8 or more Holstein and 3/8 or less Gyr and body weight (BW) at birth of 32.2 ± 5.2 kg. Experimental units were assigned to either a control (CON, n = 15) or a blend of essential oil supplementation (BEO, n = 14) treatment, maintaining a balance of genetic composition. The BEO was supplemented in the MR with 1 g/d/calf of a blend of essential oils (Apex Calf, Adisseo, China) composed by plant extracts derived from anise, cinnamon, garlic, rosemary, and thyme. During the pre-weaning phase, all heifers were fed 5 L of MR/d reconstituted to 15% (dry matter basis), divided into two equal meals. Water and starter were provided ad libitum. During the post-weaning, animals received a maximum of 3 kg of starter/d, and ad libitum corn silage, divided into two meals. Feed intake, fecal and respiratory scores were evaluated daily. The BW was measured every three days, while body development was recorded weekly. Blood samples were collected on 0, 30, and 60 d of age for total blood cell count, weekly and on the weaning day to determinate ß-hydroxybutyrate, urea and glucose, and biweekly for IGF-1. Ruminal parameters (pH, volatile fatty acids, ammonia-N, and acetate:propionate proportion-C2:C3) were measured on days 14, 28, 42, 60, 74 and 90. A randomized complete block design with an interaction between treatment and week was the experimental method of choice to test the hypothesis of the BEO's effect on all outcomes. An ANOVA procedure was used for continuous outcomes, and a non-parametric test was used for the ordered categorical outcomes, both adopting a CI = 95%. Results indicated that there was not enough evidence to accept the alternative hypothesis of the effect of BEO in MR on feed intake, performance, body development, and blood metabolites during both pre-weaning and post-weaning periods. However, results indicated that the inclusion of BEO in MR significantly affects the proportion of C2:C3 during pre- and post-weaning (P = 0.05). Similarly, the effect was significant for basophil (P ≤ 0.001), and platelet (P = 0.04) counts pre-weaning. The interaction between week and treatment was also significant for lymphocytes (P ≤ 0.001), revealing a cumulative effect. Lastly, fecal scores were also significant (P = 0.04) during pre-weaning, with lower values for BEO. The BEO contributed to ruminal manipulation in pre-weaning and carry-over effects in post-weaning, immunity improvement, and decreased morbidity of neonatal diarrhea in the pre-weaning phase., Competing Interests: This paper was financed by Brazilian Agricultural Research Corporation (EMBRAPA), Embrapa Dairy Cattle for funding this research and providing its facilities, and Adisseo Company for funding this research (project number: 20500.18/0005-2). Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brasília, Brazil), Instituto Nacional de Ciência e Tecnologia Ciência Animal (INCT, Viçosa, Brazil) for granting the scholarship. Alessandra L. Voorsluys and David V. Jacob received salary from Adisseo Company. There are no patents, products in development or marketed products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person's blood sample under a microscope. They identify and categorize leukemia by counting various blood cells and morphological features. This technique is time-consuming for the prediction of leukemia. The pathologist's professional skills and experiences may be affecting this procedure, too. In computer vision, traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images such as microscopic blood cells. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells. First, we have divided the previous works into six categories based on the output of the models. Then, we describe various steps of detection and classification of acute leukemia and WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction), Classification, and focus on classification step in the methods. Finally, we divide automated detection and classification of acute leukemia and WBCs into three categories, including traditional, Deep Neural Network (DNN), and mixture (traditional and DNN) methods based on the type of classifier in the classification step and analyze them. The results of this study show that in the diagnosis and classification of acute leukemia and WBCs, the Support Vector Machine (SVM) classifier in traditional machine learning models and Convolutional Neural Network (CNN) classifier in deep learning models have widely employed. The performance metrics of the models that use these classifiers compared to the others model are higher. We propose providing models in detecting and classify acute leukemia and WBCs that use a combination of SVM and CNN classifiers in their classification step to achieve optimum performance metrics. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to compare the biological research carried out in North Korea with the modern outcomes in the same field, from a historical perspective. In the 1960s, the group led by Bong-Han Kim asserted the existence of a fundamental circulatory system in the human body. Their findings were introduced in five papers as well as in the newspaper Rodong Sinmun. The size of the group, the range of equipment, and their findings expanded gradually, and the final paper on blood cells would be only published one of over sixty papers reported at that time. In the five papers, some unclear and inconsistent contents were found. Meanwhile, in the 2000s, PVS (primo vascular system) researchers have published over one hundred papers to prove the existence of this novel system. Some trends have emerged in approximately sixty papers as follows: The research on the identification of the anatomical and histological characteristics has gradually expanded to encompass the system's own functioning. Also, new visual results (e.g. before and after dyeing, and its comparison with blood clots) and the exploration of possible medical applications (e.g. cancer metastasis channels or stem cells location) have been proposed. However, it is difficult to identify papers which comprehensively represented the elements claimed by Bong-Han Kim's group. In addition, papers have been published more in alternative or Oriental medicine journals than in Western biomedical journals. It seems necessary to report more existence of the system in one laboratory animal and in more influential journals. [ABSTRACT FROM AUTHOR]

In this paper, a sensitive graphene-based terahertz sensor is proposed. The proposed sensor consists of a patterned graphene geometry on top of a SiO2 layer. To eliminate the transmission of proposed structure, a bottom gold layer is utilized. Moreover, a Polysillicon layer is located at a near distance under the graphene layer to provide all practical issues encountered with biasing the embedded graphene layer. According to utilized optimization procedure and resulting sensing performance, the designed sensor is sensitive to surrounding medium and can be used as a refractive index sensor with linear performance. The presented structure is based on enhancing electromagnetic fields confinement. This sensing mechanism can be used in biomedical applications for detecting different components of body fluids including blood glucose. Moreover, the proposed sensor can be used to distinguish between normal and cancerous cells. The obtained performance is investigated considering three different cells, namely Hela, basal, and MDA-MB-231 (breast cells). The simulation results show sensitivities of 3 and 3.23 THz/RIU together with figure of merits of 15 and 16.8 RIU-1 for cancer cells and blood components, respectively. The proposed structure results in an almost linear relation between resonance frequency shift and refractive index changes, which is an important property demanded by refractive index sensors. The proposed sensor benefits from simple structure resulting to an easy fabrication process. [ABSTRACT FROM AUTHOR]

The recognition and classification of White Blood Cell (WBC) play a remarkable role in blood-related diseases (i.e., leukemia, infections) diagnosis. For the highly similar morphology of different WBC subtypes, it is too confused to classify the WBC effectively and accurately for visual observation of blood cell smears. This paper proposes a Deep Convolutional Neural Network (DCNN) with feature fusion strategies, named WBC-AMNet, for automatically classifying WBC subtypes based on focalized attention mechanism. To obtain more localized attention of CNN, the fusion features of the first and the last convolutional layer are extracted by focalized attention mechanism combining Squeeze-and-Excitation (SE) and Gather-Excite (GE) modules. The new method performs successfully in classifying monocytes, neutrophils, lymphocytes, and eosinophils on the complex background with an overall accuracy of 95.66%, better than that of general CNNs. The multi-classification accuracy of WBC-AMNet with the background segmentation is over 98% in all cases. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize the attention heatmaps of different feature maps. [ABSTRACT FROM AUTHOR]

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

Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN models each one trained on the Malaria RBC dataset with different architectures for handling the classification tasks. Furthermore, disadvantage of the traditional method of using transfer learning, and how to control model complexity to achieve better performance was discussed. The dropout regularization technique was used to avoid overfitting problems and minimize validation loss. Applying Data Augmentation technique to avoid the problem of small data in training of proposed models, which is a very common problem in medical dataset. Finally, removing noise in Malaria images using a Median blur filter, and studying how effects of that on training CNN models. According to the classification results, the proposed model achieved better classification results at accuracy 99.22 on the original Malaria RBCs dataset, and it has the best performance comparing with related work. [ABSTRACT FROM AUTHOR]

Primary plasma cell leukemia (PCL) has a consistently ominous prognosis, even after progress in the last decades. PCL deserves a prompt identification to start the most effective treatment for this ultra-high-risk disease. The aim of this position paper is to revisit the diagnosis of PCL according to the presence of circulating plasma cells in patients otherwise meeting diagnostic criteria of multiple myeloma. We could identify two retrospective series where the question about what number of circulating plasma cells in peripheral blood should be used for defining PCL. The presence of ≥5% circulating plasma cells in patients with MM had a similar adverse prognostic impact as the previously defined PCL. Therefore, PCL should be defined by the presence of 5% or more circulating plasma cells in peripheral blood smears in patients otherwise diagnosed with symptomatic multiple myeloma. [ABSTRACT FROM AUTHOR]

In this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones - the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes). The separated blood cells are analysed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists. The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%. [ABSTRACT FROM AUTHOR]

The key complications associated with bare metal stents and drug eluting stents are in-stent restenosis and late stent thrombosis, respectively. Thus, improving the biocompatibility of metal stents remains a significant challenge. The goal of this protocol is to describe a robust technique of metal surface modification by biologically active peptides to increase biocompatibility of blood contacting medical implants, including endovascular stents. CD47 is an immunological species-specific marker of self and has anti-inflammatory properties. Studies have shown that a 22 amino acid peptide corresponding to the Ig domain of CD47 in the extracellular region (pepCD47), has anti-inflammatory properties like the full-length protein. In vivo studies in rats, and ex vivo studies in rabbit and human blood experimental systems from our lab have demonstrated that pepCD47 immobilization on metals improves their biocompatibility by preventing inflammatory cell attachment and activation. This paper describes the step-by step protocol for the functionalization of metal surfaces and peptide attachment. The metal surfaces are modified using polyallylamine bisphosphate with latent thiol groups (PABT) followed by deprotection of thiols and amplification of thiol-reactive sites via reaction with polyethyleneimine installed with pyridyldithio groups (PEI-PDT). Finally, pepCD47, incorporating terminal cysteine residues connected to the core peptide sequence through a dual 8-amino-3,6-dioxa-octanoyl spacer, are attached to the metal surface via disulfide bonds. This methodology of peptide attachment to metal surface is efficient and relatively inexpensive and thus can be applied to improve biocompatibility of several metallic biomaterials.

Objectives: In response to increasing numbers of states in the US that test newborn babies for cystic fibrosis (CF), the Newborn Screening Quality Assurance Programme initiated a pilot proficiency testing programme for immunoreactive trypsinogen (IRT), the biomarker for CF. Dried blood spot specimens (DBS) were used to evaluate the performance of laboratories that screen babies for CF. Methods: DBS were prepared from human whole blood enriched with physiologically relevant levels of IRT. Various methods of making IRT-enriched DBS were used to optimize the recovery and stability of the biomarker, including preparation of DBS from either intact or lysed red blood cells, varying the timing of IRT addition to blood before dispensing onto filter paper, adding a protease inhibitor cocktail, and treating serum with charcoal before IRT enrichment. The recovery and stability of IRT in DBS were assessed. Newborn screening laboratories were offered the opportunity to test blind-coded DBS in the pilot PT programme. Results: IRT was stable in the filter paper matrix when stored for one year at either -20°C or 4°C. Fifty percent more IRT was recovered from DBS prepared with lysed red blood cells where the IRT was added to blood just before dispensing; however, protease inhibitors did not improve IRT recovery. Conclusions: IRT in the DBS matrix is stable and can be shipped worldwide under ambient conditions. Optimal IRT recovery was achieved by adjustment of DBS production practices. Laboratories receiving specimens accurately measured IRT by a variety of commercial and in-house methods. [ABSTRACT FROM AUTHOR]

One of the most common cancers identified worldwide among women is breast cancer, which has become the foremost reason for death. Due to the complexity of breast tissues, it is essential to accurately detect and classify breast cancer in medical imaging. In this paper, an optimisationbased deep learning technique is created to detect breast cancer using histopathology pictures. At first, the input image taken from the figshare dataset is sent to the preprocessing stage. The median filter is employed at the preprocessing stage to eliminate noise from the input image. Then, the pre-processed images are sent to the segmentation procedure, where the Fuzzy Local Information C-Means (FLICM) technique is used to segment the blood cells. After the segmentation process, feature extraction is carried out. In the feature extraction process, various features, such as area, shape, diameter, and Speeded-Up Robust Features (SURF), are extracted from histopathological images. At last, Deep Q Network (DQN) trained using the developed Adaptive Jelly Fish Search Optimization (AJSO) algorithm is used to identify and classify benign and malignant breast cancer. The devised approach has good accuracy, TPR, and TNR values of 91.1%, 92.1%, and 92%, correspondingly. [ABSTRACT FROM AUTHOR]

This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional techniques. The work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells, namely, eosinophils, neutrophils, monocytes, and lymphocytes, known for their relationship with human body damage, inflammatory regions, and organ illnesses, in particular, and with the health of the immune system and other hazards, such as cardiovascular disease or infections, more in general. The results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%, 97%, and 89%, respectively, with regard to the training, verification, and testing of the corresponding datasets. [ABSTRACT FROM AUTHOR]

The classification of leukocytes in peripheral blood images is an important milestone to be achieved because it can greatly assist pathologists to diagnose diseases such as leukemia, anemia, and other blood disorders. To a certain extent, a good segmentation method for identifying leukocytes from their background is the first step to the efficient functioning of the leukocytes classification system. However, the morphological structure of leukocytes, poor contrast, and the variations in their shape and size lead to the degradation of the segmentation accuracy. In this paper, we propose a new leukocyte segmentation framework that first locates and then segments leukocytes from peripheral blood images. Here, the locations of the leukocytes are first identified using a novel edge strength cue (ESc), and later, the Grabcut model is deployed to obtain the segmentation of the leukocytes. The novelty lies in the way the location of the leukocytes is detected, and this improves the leukocyte segmentation accuracy. The experimental evaluation is performed on ALL-IDB1, Cellavision, and LISC datasets for leukocyte segmentation based on the detection of the ESc location. Experimental results are evaluated using precision, recall, and F-score measures. The proposed method outperforms the state-of-the-art techniques. Additionally, the computation time of the proposed method is analyzed and presented in the study. Graphical Abstract Leukocytes Location Detection and Segmentation.

The sorting of target particles from heterogeneous samples is challenging yet crucial for cell biology research and clinical diagnosis. Among various microfluidic methods, the use of cavity-based laminar vortex combined with inertial focusing is a powerful label-free passive technique for the selective sorting of large rare cells with high purity and concentration from billions of blood cells. However, this technology faces the challenge of improving the cavity holding capacity of trapped particles. This paper describes a round cavity-based vortex sorting method and presents a novel judgment criterion. The proposed round cavity achieves a holding capacity of entrapped target particles that is 2.2–7.8 times higher than that of rectangular cavities. By comparing the particle recirculating orbits and the simulated vortex morphology in round and rectangular cavities, a mechanism whereby particles/cells are held within the cavities is investigated. It is found that the area ratios (S = Ap/Ac) of the particle orbit area (Ap) to the cavity area (Ac) are 0.56 and 0.95 for the rectangular and round cavities, respectively. The results show that the round cavity provides more efficient space for recirculating particles and has better sorting performance. This round cavity-based vortex sorting method will be useful for clinical applications. [ABSTRACT FROM AUTHOR]

This paper reports a miniature microfluidic system based on centrifugal and gravity actuations for separation of blood cells. The fluidic platform is driven with a motor and controlled using a single-chip micyoco (SCM). Centrifugal force was used both for delivering the blood sample and realizing density gradient centrifugation for separation of red blood cells from plasma. By utilizing the centrifugal force, Coriolis force, Euler's force, and gravity force in actuation of blood sample, a compact design of three-dimensional fluidic system for flow control was achieved. The centrifugal microfluidic platform was fabricated using 3D printing technology with polymers as structural materials. Because of the strong adhesion of leukocyte and the larger sizes of the blood cells, silica electrospun fiber was used as filter for white cells. In the experiments, the average removal rate of red blood cells is controllable by changing the working parameters. The instrument can separate 20–50 μl plasma at a time. No white cells were found in the plasma after separation. [ABSTRACT FROM AUTHOR]

Malaria is a deadly disease caused by parasites of the genus Plasmodium, spread by mosquitoes, mostly in tropical and subtropical regions. Plasmodium falciparum is dangerous among the five species of this parasite that causes malaria. Correct classification of malaria parasite species is important for diagnosis and treatment. The proposed project aims to classify five different malaria parasite species from the blood sample image of the patient using three different classifiers implemented using MATLAB. Classifiers are compared for accuracy, precision, sensitivity, and specificity. Three algorithms are used as classifiers in this project: SVM (Support Vector Machine), Random Forest (Tree assembly) and KNN (K-Nearest Neighbor). An IoT device is integrated with the results from MATLAB with Arduino controller. The test result from the classifier is transmitted to the patient and the physician using IoT and GSM module. According to the design of this project, when the victim is a carrier of Plasmodium falciparum, upon detection using MATLAB, an audible signal sounds to indicate the severity of the parasite infection to the laboratory assistant. [ABSTRACT FROM AUTHOR]

This paper reports a novel approach for the efficient detection of blood and skin cancerous cells using PCF-based sensor. The proposed sensor contains an asymmetrical arrangement of rectangular holes where the core region is composed of a single rectangle. Zeonex is used as the fiber substance. The model is structured and numerically analyzed using the finite element method (FEM)-based software. The simulation of the proposed model validates its efficiency in cancer cell detection. Several performance parameters suggest that the proposed sensor attains optimum results at 2.0 THz. At this point, the sensitivities achieved in detecting blood cancer cells, normal cell (Jurkat), skin cancer cell, normal cell (basal), and water are 96.74, 96.56, 96.61, 96.34, and 95.69%, respectively. Besides, simulation results also suggest that the material and confinement loss are very low for this proposed sensor. Additionally, the simple rectangle-based model provides the ease of fabrication introducing prevailing strategies. [ABSTRACT FROM AUTHOR]

Cell separation technique is essential for the detection of circulating tumor cells in blood and for enrichment of stem cell in regenerative medicine. However, conventional methods require cell labeling such as antibodies, which may damage the cells during the operation. Therefore, dielectrophoresis (DEP), a "label‐free" separation technique that eliminates the effects on cells, has been attracting attention. In this paper, we investigated the dielectric properties of Jurkat cells derived from human T‐cell leukemia cells by dielectrophoretic velocimetry. An equivalent circuit model of the cell was constructed and the frequency characteristics of the voltage of each layer of the cell were calculated. As a result, it was shown that the live and dead cells showed opposite dielectrophoretic forces in the region below 10 kHz, indicating the possibility of separating them. The agreement between the theoretical calculations and the experimental results shows the validity of the equivalent circuit model. [ABSTRACT FROM AUTHOR]

Two samples of living blood cells and of cleared blood plasma from the Phlebobranch tunicate Ascidia ceratodes from Bodega Bay, California, and one of fresh Henze solution from A. ceratodes of Monterey Bay, California, have been examined using sulfur K-edge x-ray absorption spectroscopy (XAS). Biological sulfur included sulfate esters, sulfate and bisulfate ions, benzothiazole, thianthrene, epi-sulfide, thiol and disulfide. Glutathione dominated reduced sulfur, from which an average intracellular Voltage of -0.21 V was calculated. Sulfate-bisulfate ratios yielded blood cell pH values of 2.0 and 2.8. Total blood cell [sulfur] was 373±9 mM or 296±73 mM from BaSO 4 gravimetry. Two plasma samples (pH 6.9 or 7.0; [S] = 33±6 mM or 26±4 mM) were dominated by sulfate and disulfide. Fresh Henze solution evidenced a sulfur inventory similar to blood cells, with calculated pH = 2.7. A V(III)-sulfonate fraction varied systematically with intracellular pH across six independent blood cell samples, implying a vanadium mobilization pathway. Bodega Bay and Monterey Bay A. ceratodes appear to maintain alternative suites of low-valent sulfur. The significance of the vanabins to vanadium metabolism is critically examined in terms of known protein - V(IV) biochemistry. Finally, a detailed hypothesis for the reduction of [VO 4 ] 3 - to V(III) in ascidians is introduced. A vanadium oxido-reductase is proposed to span the signet ring membrane and to release V(III) into the inner acidic vacuole. The V(V) to V(III) reduction is predicted require an inner-sphere mechanism, a thiol reductant, 7-coordinate V(III), a biologically accessible Voltage, and proton-facilitated release of V(III)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The simple green method for synthesis of stable L-Methionine (L-Met) capped silver (Ag@LM NPs) and gold (Au@LM NPs) nanoparticles (NPs) without adding any additional reduction agent or stabilizer was developed. Colloidal dispersions were characterized by UV-Vis spectrophotometry. The size and spherical shape of NPs were evaluated by transmission electron microscopy. Their surface covering was confirmed by atomic force microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, and zeta potential measurements. Density functional theory calculations pointed that the preferential adsorption mode of L-Met on both Ag and Au surfaces was a vertical binding geometry via -NH 2 group, while horizontal binding mode via S and -NH 2 groups is also possible. The genotoxicity (evaluated by the micronucleus assay) of NPs, as well as their effects on some oxidative stress parameters (catalase activity, malondialdehyde level), were assessed in vitro using human peripheral blood cells as a model system. The influence of NPs on the morphology of lymphocyte cells studied using atomic force microscopy revealed that the membrane of cells remained unaffected after the treatment with NPs. When considering the effects of NPs on catalase activity and malondialdehyde level, neither particle type promoted oxidative stress. However, the treatment of lymphocytes with Ag@LM NPs induced a concentration-dependent enhancement of the micronuclei incidence and suppression of the cell proliferation while Au@LM NPs promoted cell proliferation, with no significant effects on micronuclei formation. The Ag@LM NPs were more prone to induce DNA damage than Au@LM NPs, which makes the latter type more suitable for further studies in nano-medicine., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and spectral information, provides more wealthy information for recognizing cells. In this paper, a novel blood cell classification framework, which combines a modulated Gabor wavelet and deep convolutional neural network (CNN) kernels, named as MGCNN, is proposed based on medical hyperspectral imaging. For each convolutional layer, multi-scale and orientation Gabor operators are taken dot product with initial CNN kernels. The essence is to transform the convolutional kernels into the frequency domain to learn features. By combining characteristics of Gabor wavelets, the features learned by modulated kernels at different frequencies and orientations are more representative and discriminative. Experimental results demonstrate that the proposed model can achieve better classification performance than traditional CNNs and widely used support vector machine approaches, especially as training small-sample-size situations.

The health benefits of natural products have long been recognized. Consumption of dietary compounds such as supplements provides an alternative source of natural products to those obtained from the diet. There is a growing concern regarding the possible side effects of using different food supplements simultaneously, since their possible interactions are less known. For the first time, we have tested genotoxic and antigenotoxic effects of Biochaga, in combination with dihydroquercetin. No genotoxic effect on whole blood cells was observed within individual treatment of Biochaga (250  μ g/mL, 500  μ g/mL and 1000  μ g/mL) and dihydroquercetin (100  μ g/mL, 250  μ g/mL and 500  μ g/mL), nor in combination. Afterwards, antigenotoxic potency of both supplements against hydrogen peroxide- (H 2 O 2 -) induced DNA damage to whole blood cells (WBC) was assessed, using the comet assay. Biochaga and dihydroquercetin displayed a strong potential to attenuate H 2 O 2 -induced damage on DNA in cells at all tested concentrations, with a statistical significance ( p < 0.05), whereas Biochaga at the dose of 500  μ g/mL in combination with dihydroquercetin 500  μ g/mL was most prominent. Biochaga in combination with dihydroquercetin is able to protect genomic material from oxidative damage induced by hydrogen peroxide in vitro ., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2019 Lada Živković et al.)

Fast 3D imaging and measurement of cells are of great importance in many fields associated with the life science. In this paper, only using two phase images in orthogonal directions, a geometric rotation method for 3D fast morphology reconstruction is proposed in view of more spheroid cells in blood cells. In this method, at first, the edges of a cell in two orthogonal projection directions are extracted from the corresponding phase images, and theirs centers and axes are determined. Then, with the geometric rotation method, the 3D surfaces of the cell membrane and its nuclei are reconstructed respectively. After all, the 3D complete morphology of the cell is reconstructed according to their space geometry relation. Simulation and experimental results demonstrate the validity and accuracy of this method for spheroid cells. Compared with other reconstruction methods, the image speed is improved since the multiple measurements or iterative procedures are not required. It provides a new approach to image the 3D morphological structure of spheroid blood cells.

Background and Objectives: Morphological analysis is the starting point for the diagnostic approach of more than 80% of hematological diseases. However, the morphological differentiation among different types of normal and abnormal peripheral blood cells is a difficult task that requires experience and skills. Therefore, the paper proposes a system for the automatic classification of eight groups of peripheral blood cells with high accuracy by means of a transfer learning approach using convolutional neural networks. With this new approach, it is not necessary to implement image segmentation, the feature extraction becomes automatic and existing models can be fine-tuned to obtain specific classifiers., Methods: A dataset of 17,092 images of eight classes of normal peripheral blood cells was acquired using the CellaVision DM96 analyzer. All images were identified by pathologists as the ground truth to train a model to classify different cell types: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (myelocytes, metamyelocytes and promyelocytes), erythroblasts and platelets. Two designs were performed based on two architectures of convolutional neural networks, Vgg-16 and Inceptionv3. In the first case, the networks were used as feature extractors and these features were used to train a support vector machine classifier. In the second case, the same networks were fine-tuned with our dataset to obtain two end-to-end models for classification of the eight classes of blood cells., Results: In the first case, the experimental test accuracies obtained were 86% and 90% when extracting features with Vgg-16 and Inceptionv3, respectively. On the other hand, in the fine-tuning experiment, global accuracy values of 96% and 95% were obtained using Vgg-16 and Inceptionv3, respectively. All the models were trained and tested using Keras and Tensorflow with a Nvidia Titan XP Graphics Processing Unit., Conclusions: The main contribution of this paper is a classification scheme involving a convolutional neural network trained to discriminate among eight classes of cells circulating in peripheral blood. Starting from a state-of-the-art general architecture, we have established a fine-tuning procedure to develop an end-to-end classifier trained using a dataset with over 17,000 cell images obtained from clinical practice. The performance obtained when testing the system has been truly satisfactory, the values of precision, sensitivity, and specificity being excellent. To summarize, the best overall classification accuracy has been 96.2%., (Copyright © 2019. Published by Elsevier B.V.)

This paper analysed Sickle cell anaemia literature on Nigeria listed in PubMed to understand the research performance of medical researchers on sickle cell anaemia literature on Nigeria. Data covering the period of 2006-2016 were collected from the PubMed, a citation database developed by the National Centre for Biotechnology Information (NCBI).The search strategy used was ((sickle cell anaemia) OR (sickle cell anemia)) AND Nigeria AND (("2006/01/01"[PDat] : "2016/12/31"[PDat])), bearing in mind variance in spelling (anaemia and anemia), which retrieved 326 sickle cell literature on Nigeria between 2006 and 2016. Article productivity increased between 2006 and 2010. However, there was a drastic drop in article publication between 2010 and 2011 as well as between 2013 and 2015. The degree of collaboration ranged between 0.85 and 1. University of Nigeria Teaching Hospital, Enugu, Nigeria, had the highest number of contributing authors. Authors published more in journals located in Nigeria, with Nigeria Journal of Clinical Practice being the most prolific publication outlet for authors publishing sickle cell anaemia literature on Nigeria. [ABSTRACT FROM AUTHOR]

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

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

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

The paper considers the possibilities of using in medical practice various cryomethods and techniques of cryopreservation of biological objects to treat the patients with purulent chronic wounds (PCW). The variety of methods and approaches to apply cold in medical practice: general and local cryotherapy, cryosurgery etc. has been emphasized. As a result of using each of these methods, regenerative processes begin, which promote the healing of PCW. Particular attention is paid to cryotechniques, ensuring the preservation of cord blood at low temperatures. Cord blood cells and serum have been shown to have immune- and trophicstimulating therapeutic effects, that is extremely important when treating the patients with PCW. The issue of combined use of cord blood serum and innovative vacuum therapy for the patients with PCW has been considered. [ABSTRACT FROM AUTHOR]

1. Sequence analysis of the NADPH domain (residues 158 - 293) and of the interface domain (365 - 478) was based on 12 CNBr fragments, which were isolated using ion-exchange chromatography and paper methods. Fragments with more than 15 residues were digested further with trypsin and chymotrypsin. The isolated peptides were sequenced by automated solid-phase Edman degradation. All sequenced peptides were ordered and overlapped by computerized comparisons with a complete sequence guessed from the electron density map of the protein. In the case of short CNBr fragments, this alignment was confirmed by the sequence analysis of protein fragments resulting from incomplete CNBr cleavage. 2. In the NADPH domain, residue 197, which is involved in an induced-fit mechanism, was identified as a tyrosine. The structure of the NADPH domain is probably homologous with the NAD domain of lipoamide dehydrogenase and with the FAD domain of several proteins, but not with NADPH domains of known chainfold in other proteins. 3. The paper completes the sequence analysis of glutathione reductase so that the enzyme is now known in atomic detail. The numbering scheme of the chemically determined sequence will be used henceforth in crystallographic studies also. As inferred from the sequence data each of the two identical chains contains 478 amino acid residues, the composition being Cys10, Asp21, Asn17, Thr31, Ser31, Glu29, Gln11, Pro24, Gly43, Ala42, Val44, Met15, Ile29, Leu34, Tyr13, Phe14, Lys34. His16, Arg17, and Trp3. From these data an mr of 2 × 51 600 was calculated for the FAD-free apoenzyme and an Mr of 2 × 52400 for the holoenzyme. [ABSTRACT FROM AUTHOR]

Aims: Blood cell classification helps detect various diseases. However, the current classification model of blood cells cannot always get great results. A network that automatically classifies blood cells can provide doctors with data as one of the criteria for diagnosing patients' disease types and severity. If doctors diagnose blood cells, doctors could spend lots of time on the diagnosis. The diagnosis progress is very tedious. Doctors can make some mistakes when they feel tired. On the other hand, different doctors may have different points on the same patient. Methods: We propose a ResNet50-based ensemble of randomized neural networks (ReRNet) for blood cell classification. ResNet50 is used as the backbone model for feature extraction. The extracted features are fed to 3 randomized neural networks (RNNs): Schmidt neural network, extreme learning machine, and dRVFL. The outputs of the ReRNet are the ensemble of these 3 RNNs based on the majority voting mechanism. The 5 × 5-fold cross-validation is applied to validate the proposed network. Results: The average-accuracy, average-sensitivity, average-precision, and average-F1-score are 99.97%, 99.96%, 99.98%, and 99.97%, respectively. Conclusions: The ReRNet is compared with 4 state-of-the-art methods and achieves the best classification performance. The ReRNet is an effective method for blood cell classification based on these results. [ABSTRACT FROM AUTHOR]

Biodegradable stent coatings have shown great potential in terms of delivering drugs to a damaged vessel wall, and their release profiles are key elements governing the overall performance of drug-eluting stents (DESs). However, release and degradation kinetics are usually not tested under simulated physiological conditions or in dynamic environments, both essential aspects in the design of novel DESs. To bridge this gap, fused silica-based microfluidic systems, with either round or square channel cross-sections, were designed to mimic the microenvironment of a stented vessel. In particular, we fabricated and characterized microfluidic chips based on customizable channels, which were spray-coated with a naturally-derived, rutin-loaded zein solution, to perform a comprehensive study under flow conditions. Dynamic assays after 6 hours showed how the degradation of the zein matrix was affected by the cross-sectional conformation (∼69% vs. ∼61%, square and round channel, respectively) and the simulated blood fluid components (∼55%, round channel with a more viscous solution). The released amount of rutin was ∼81% vs. ∼77% and ∼78% vs. ∼74% from the square and round channels, using the less and more viscous blood-simulated fluids, respectively. Fitting the drug release data to Korsmeyer–Peppas and first-order mathematical models provided further insight into the mechanism of rutin release and coating behavior under flowing conditions. More importantly, whole blood tests with our newly developed microfluidic platforms confirmed the hemocompatibility of our zein-based coating. In detail, in-flow and static studies on the blood cell behavior showed a significant reduction of platelet adhesion (∼73%) and activation (∼93%) compared to the stainless-steel substrate, confirming the benefits of using such naturally-derived coatings to avoid clogging. Overall, our microfluidic designs can provide a key practical tool for assessing polymer degradation and drug release from degradable matrices under flowing conditions, thus aiding future studies on the development of hemocompatible, controlled-release coatings for DESs. [ABSTRACT FROM AUTHOR]

We have previously shown that obesity is associated with increased secretion of IgG antibodies with anti-self-reactivity. In this paper, we confirm and extend our previous findings. We show that the plasma of individuals with obesity is enriched in autoimmune antibodies whose levels are positively associated with blood frequencies of the subset of Double Negative (DN) B cells, which is the most pro-inflammatory B cell subset. We also show that DN B cells, significantly increased in the blood of obese versus lean individuals, are characterized by higher expression of immune activation markers and of the transcription factor T-bet, both associated with autoimmunity. The removal of DN B cells from the peripheral B cell pool significantly decreases in vitro secretion of anti-self IgG antibodies. These results altogether confirm the crucial role of DN B cells in the secretion of anti-self IgG antibodies in individuals with obesity. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs) have substantially improved the prognosis of patients with different types of cancer. Through blockade of cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1), negative feedback mechanisms of the immune system are inhibited, potentially resulting in very durable anti-tumor responses. Despite their promise, ICIs can also elicit auto-immune toxicities. These immune-related adverse events (irAEs) can be severe and sometimes even fatal. Therefore, being able to predict severe irAEs in patients would be of added value in clinical decision making. A search was performed using "adverse events", "immune checkpoint inhibitor", "biomarker", and synonyms in PubMed, yielding 3580 search results. After screening title and abstract on the relevance to the review question, statistical significance of reported potential biomarkers, and evaluation of the remaining full papers, 35 articles were included. Five additional reports were obtained by means of citations and by using the similar article function on PubMed. The current knowledge is presented in comprehensive tables summarizing blood-based, immunogenetic and microbial biomarkers predicting irAEs prior to and during ICI therapy. Until now, no single biomarker has proven to be sufficiently predictive for irAE development. Recommendations for further research on this topic are presented. [ABSTRACT FROM AUTHOR]

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

Efficient trapping of bacteria from whole blood is essential for point-of-care diagnostics of sepsis at an early stage in order to reduce morbidity and mortality. However, low bacteria concentration and the presence of blood cells hinder the trapping efficiency of bacteria from whole blood. As red blood cells comprise 94.9% of total blood cells, lysing the red blood cells using saponin could effectively attenuate the influence of the blood component on the bacteria-trapping process. In this situation, long-range bacteria trapping from whole blood using a hybrid electrokinetic based lab-on-a-chip device becomes promising. In this paper, through developing a multi-physical lattice Boltzmann method with Langevin dynamics, the continuous trapping process of S. aureus in a microfluidic channel with quadrupole electrodes under combined alternating-current electrothermal electrohydrodynamic force and negative dielectrophoresis force is numerically investigated and optimized at various parametric conditions. Based on the statistical data, a stable bacteria recovery rate of 68.4%–74.5% is successfully achieved with respect to different bacteria densities under appropriate operational conditions of the designed lab-on-a-chip device. The current work demonstrates the potential of continuous bacteria trapping from whole blood using hybrid electrokinetic phenomena. [ABSTRACT FROM AUTHOR]

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

Blood cell count provides relevant clinical information about different kinds of disorders. Any deviation in the number of blood cells implies the presence of infection, inflammation, edema, bleeding, and other blood-related issues. Current microscopic methods used for blood cell counting are very tedious and are highly prone to different sources of errors. Besides, these techniques do not provide full information related to blood cells like shape and size, which play important roles in the clinical investigation of serious blood-related diseases. In this paper, deep learning-based automatic classification and quantitative analysis of blood cells are proposed using the YOLOv2 model. The model was trained on 1560 images and 2703-labeled blood cells with different hyper-parameters. It was tested on 26 images containing 1454 red blood cells, 159 platelets, 3 basophils, 12 eosinophils, 24 lymphocytes, 13 monocytes, and 28 neutrophils. The network achieved detection and segmentation of blood cells with an average accuracy of 80.6% and a precision of 88.4%. Quantitative analysis of cells was done following classification, and mean accuracy of 92.96%, 91.96%, 88.736%, and 92.7% has been achieved in the measurement of area, aspect ratio, diameter, and counting of cells respectively. Graphical abstract [ABSTRACT FROM AUTHOR]

Purpose: In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images. Design/methodology/approach: The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach. Findings: The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%. Originality/value: The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them. [ABSTRACT FROM AUTHOR]

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

In conditions of abdominal sepsis with indications of first- or second-stage shock, blood cells undergo significant ultrastructural changes that cause impaired gas exchange, changes in reactivity, and decompensation of organs and systems functions. This paper presents a cross-sectional prospective study aimed at researching the ultrastructure of blood cells in children experiencing abdominal septic shock against the background of generalized purulent peritonitis of appendicular origin. This study was conducted with 15 children aged 6–12 who were undergoing treatment for generalized appendicular purulent peritonitis, with first- or second-stage abdominal septic shock, in emergency care. The changes in the ultrastructure of erythrocytes did not correspond to changes characteristic of eryptosis, which confirms their occurrence under the influence of such pathogenic factors as intoxication, metabolic, water–electrolyte balance, and acid–base disorders. Ultrastructural changes of granulocytes indicate their hyperactivation, which leads to the exhaustion of membrane synthetic resources, membrane destruction, ineffective expenditure of bactericidal factors on substrates that are not subject to destruction. In lymphocytes, disorganization of the nuclear membrane and intracellular membranes, uneven distribution of chromatin, the hypertrophied Golgi apparatus, and a large number of young mitochondria, lysosomes, ribosomes, vesicles manifesting the disruption of metabolism, stress and decompensation of energy supply and protein synthesis systems, have been demonstrated. In conditions of abdominal sepsis with indications of first- or second-stage shock, blood cells undergo substantial ultrastructural changes causing gas exchange disruption, changes in reactivity, as well as decompensation of organs and system functioning. [ABSTRACT FROM AUTHOR]

Medical image processing technics has been a great boon to hematologists for the detection and investigation of disorders such as malarial parasites. Malaria may be an illness caused by parasites, that infect human red blood cells (erythrocytes). The parasites are transmitted by the bite of feminine mosquitoes. protozoal infection continues to be an extremely deadly condition albeit it's treatable. The sphere of investigation in this paper is for automatic malaria detection from blood smear images that has a prodigious significance in healthcare informatics. Widespread experiments are executed using the meticulously thick blood smear images. Our results reveal that the deep-learning-based computing technique with accuracy of 86%. [ABSTRACT FROM AUTHOR]

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

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

In this paper, we report on the development of a microfluidic device, which extracts plasma from low‐volume whole blood (10 μL), and is aimed at integration with microfluidic system for micro analytical systems. The device autonomously separates blood cells and transfers the separated plasma by steady rotation. Based on the original autonomous fluidic control theory, we developed the principle of automatic timed injection of pressurizing medium into the separation chamber. This realizes the transferring of separated plasma from blood separation chamber to the other place, after separating blood cells by centrifugal force. Through the demonstration on a polydimethylsiloxane microfluidic device, we confirmed that the automatic plasma extraction was successfully implemented as designed. The efficiency of separation was evaluated on the basis of the concentrations of blood cells in samples before and after extraction, and the rate of blood cell removal was 98.5%. [ABSTRACT FROM AUTHOR]