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PurposeFolate required to achieve desirable red blood cell (RBC) folate concentrations within 4–8 weeks pre-pregnancy is not known. We studied the effect of supplementation with 400 or 800 µg/day folate in achieving RBC-folate ≥906 nmol/L.MethodsNon-pregnant women were randomized to receive multinutrient supplements containing 400 µg/day (n = 100) or 800 µg/day (n = 101) folate [folic acid and (6S)-5-CH3-H4folate-Ca (1:1)]. The changes of folate biomarkers were studied after 4 and 8 weeks in the 198 women who returned at least for visit 2.ResultsAt baseline, 12 of the 198 participants (6.1%) had RBC-folate <340 nmol/L, but 88% had levels <906 nmol/L. The RBC-folate concentrations increased significantly in the 800 µg/day (mean ± SD = 652 ± 295 at baseline; 928 ± 330 at 4 weeks; and 1218 ± 435 nmol/L at 8 weeks) compared with the 400 µg/day [632 ± 285 at baseline (p = 0.578); 805 ± 363 at 4 weeks (p < 0.001); 1021 ± 414 nmol/L at 8 weeks (p < 0.001)]. The changes of RBC-folate were greater in the 800 µg/day than in the 400 µg/day at any time (changes after 8 weeks: 566 ± 260 vs. 389 ± 229 nmol/L; p < 0.001). Significantly more women in the 800 µg group achieved desirable RBC-folate concentrations at 4 weeks (45.5 vs. 31.3%; p = 0.041) or 8 weeks (83.8 vs. 54.5%; p < 0.001) compared with the 400 µg group. RBC-folate levels below the population median (590 nmol/L) were associated with a reduced response to supplements.Conclusions88% of the women had insufficient RBC-folate to prevent birth defects, while 6.1% had deficiency. Women with low RBC-folate were unlikely to achieve desirable levels within 4–8 weeks, unless they receive 800 µg/day. The current supplementation recommendations are not sufficient in countries not applying fortification.Trials registerThe trial was registered at The German Clinical Trials Register: DRKS-ID: DRKS00009770.

Macrophages are a source of both proinflammatory and restorative functions in damaged tissue through complex dynamic phenotypic changes. Here, we sought to determine whether monocyte-derived macrophages (MDMs) contribute to recovery after acute sterile brain injury. By profiling the transcriptional dynamics of MDMs in the murine brain after experimental intracerebral hemorrhage (ICH), we found robust phenotypic changes in the infiltrating MDMs over time and demonstrated that MDMs are essential for optimal hematoma clearance and neurological recovery. Next, we identified the mechanism by which the engulfment of erythrocytes with exposed phosphatidylserine directly modulated the phenotype of both murine and human MDMs. In mice, loss of receptor tyrosine kinases AXL and MERTK reduced efferocytosis of eryptotic erythrocytes and hematoma clearance, worsened neurological recovery, exacerbated iron deposition, and decreased alternative activation of macrophages after ICH. Patients with higher circulating soluble AXL had poor 1-year outcomes after ICH onset, suggesting that therapeutically augmenting efferocytosis may improve functional outcomes by both reducing tissue injury and promoting the development of reparative macrophage responses. Thus, our results identify the efferocytosis of eryptotic erythrocytes through AXL/MERTK as a critical mechanism modulating macrophage phenotype and contributing to recovery from ICH.

Morphologies of red blood cells are normally interpreted by a pathologist. It is time-consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead to false disease diagnosis and improper treatment. Thus, a decent pathologist must truly be an expert in classifying red blood cell morphology. In the past decade, many approaches have been proposed for classifying human red blood cell morphology. However, those approaches have not addressed the class imbalance problem in classification. A class imbalance problem—a problem where the numbers of samples in classes are very different—is one of the problems that can lead to a biased model towards the majority class. Due to the rarity of every type of abnormal blood cell morphology, the data from the collection process are usually imbalanced. In this study, we aimed to solve this problem specifically for classification of dog red blood cell morphology by using a Convolutional Neural Network (CNN)—a well-known deep learning technique—in conjunction with a focal loss function, adept at handling class imbalance problem. The proposed technique was conducted on a well-designed framework: two different CNNs were used to verify the effectiveness of the focal loss function and the optimal hyperparameters were determined by fivefold cross-validation. The experimental results show that both CNNs models augmented with the focal loss function achieved higher F 1 -scores, compared to the models augmented with a conventional cross-entropy loss function that does not address class imbalance problem. In other words, the focal loss function truly enabled the CNNs models to be less biased towards the majority class than the cross-entropy did in the classification task of imbalanced dog red blood cell data.

Background and Objectives: Malaria is a disease that can result in a variety of complications. Diagnosis is carried out by an optical microscope and depends on operator experience. The use of artificial intelligence to identify morphological patterns in erythrocytes would improve our diagnostic capability. The object of this study was therefore to establish computer viewing models able to classify blood cells infected with Plasmodium spp. to support malaria diagnosis by optical microscope. Materials and Methods: A total of 27,558 images of human blood sample extensions were obtained from a public data bank for analysis; half were of parasite-infected red cells (n = 13,779), and the other half were of uninfected erythrocytes (n = 13,779). Six models (five machine learning algorithms and one pre-trained for a convolutional neural network) were assessed, and the performance of each was measured using metrics like accuracy (A), precision (P), recall, F1 score, and area under the curve (AUC). Results: The model with the best performance was VGG-19, with an AUC of 98%, accuracy of 93%, precision of 92%, recall of 94%, and F1 score of 93%. Conclusions: Based on the results, we propose a convolutional neural network model (VGG-19) for malaria diagnosis that can be applied in low-complexity laboratories thanks to its ease of implementation and high predictive performance.

The morphological analysis test item of urine red blood cells is referred to as “extracorporeal renal biopsy,” which holds significant importance for medical department testing. However, the accuracy of existing urine red blood cell morphology analyzers is suboptimal, and they are not widely utilized in medical examinations. Challenges include low image spatial resolution, blurred distinguishing features between cells, difficulty in fine-grained feature extraction, and insufficient data volume. This article aims to improve the classification accuracy of low-resolution urine red blood cells. This paper proposes a super-resolution method based on category-aware loss and an RBC-MIX data enhancement approach. It optimizes the cross-entropy loss to maximize the classification boundary and improve intra-class tightness and inter-class difference, achieving fine-grained classification of low-resolution urine red blood cells. Experimental outcomes demonstrate that with this method, an accuracy rate of 97.8% can be achieved for low-resolution urine red blood cell images. This algorithm attains outstanding classification performance for low-resolution urine red blood cells with only category labels required. This method can serve as a practical reference for urine red blood cell morphology examination items.

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k -folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k -folds, and matched trained personnel in overall characterization of whole channel images with R 2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring.

The analysis of pathophysiological change to erythrocytes is important for early diagnosis of anaemia. The manual assessment of pathology slides is time-consuming and complicated regarding various types of cell identification. This paper proposes an ensemble rule-based decision-making approach for morphological classification of erythrocytes. Firstly, the digital microscopic blood smear images are pre-processed for removal of spurious regions followed by colour normalisation and thresholding. The erythrocytes are segmented from background image using the watershed algorithm. The shape features are then extracted from the segmented image to detect shape abnormality present in microscopic blood smear images. The decision about the abnormality is taken using proposed multiple rule-based expert systems. The deciding factor is majority ensemble voting for abnormally shaped erythrocytes. Here, shape-based features are considered for nine different types of abnormal erythrocytes including normal erythrocytes. Further, the adaptive boosting algorithm is used to generate multiple decision tree models where each model tree generates an individual rule set. The supervised classification method is followed to generate rules using a C4.5 decision tree. The proposed ensemble approach is precise in detecting eight types of abnormal erythrocytes with an overall accuracy of 97.81% and weighted sensitivity of 97.33%, weighted specificity of 99.7%, and weighted precision of 98%. This approach shows the robustness of proposed strategy for erythrocytes classification into abnormal and normal class. The article also clarifies its latent quality to be incorporated in point of care technology solution targeting a rapid clinical assistance.

Erythrocyte shape deformations are related to different important illnesses. In this paper, we focus on one of the most important: the Sickle cell disease. This disease causes the hardening or polymerization of the hemoglobin that contains the erythrocytes. The study of this process using digital images of peripheral blood smears can offer useful results in the clinical diagnosis of these illnesses. In particular, it would be very valuable to find a rapid and reproducible automatic classification method to quantify the number of deformed cells and so gauge the severity of the illness. In this paper, we show the good results obtained in the automatic classification of erythrocytes in normal cells, sickle cells, and cells with other deformations, when we use a set of functions based on integral-geometry methods, an active contour-based segmentation method, and a k-NN classification algorithm. Blood specimens were obtained from patients with Sickle cell disease. Seventeen peripheral blood smears were obtained for the study, and 45 images of different fields were obtained. A specialist selected the cells to use, determining those cells which were normal, elongated, and with other deformations present in the images. A process of automatic classification, with cross-validation of errors with the proposed descriptors and with other two functions used in previous studies, was realized.

We describe a microchannel device which utilizes a novel approach to obtain area and volume measurements on many individual red blood cells. Red cells are aspirated into the microchannels much as a single red blood cell is aspirated into a micropipette. Inasmuch as there are thousands of identical microchannels with defined geometry, data for many individual red cells can be rapidly acquired, and the fundamental heterogeneity of cell membrane biophysics can be analyzed. Fluorescent labels can be used to quantify red cell surface and cytosolic features of interest simultaneously with the measurement of area and volume for a given cell. Experiments that demonstrate and evaluate the microchannel measuring capabilities are presented and potential improvements and extensions are discussed.