Hasil untuk "Diseases of the blood and blood-forming organs"

Fatih E. Bilgen, Ozgur B. Akan

Although the circulatory system functions as a continuous source of physiological data, contemporary diagnostics remain bound to intermittent, time-delayed assessments. To resolve this, we present a framework for ubiquitous hematological profiling driven by Integrated Sensing and Communication (ISAC). We demonstrate how electromagnetic signals can be exploited to monitor blood in real-time, effectively converting them into diagnostic tools. We analyze the biological foundations of blood, review existing Complete Blood Count (CBC) and sensing technologies, and detail a novel pipeline for continuous blood monitoring. Furthermore, we discuss the potential applications of deploying these devices to enable real-time CBC and biomarker detection, ultimately revolutionizing how we predict, detect, and manage individual and public health.

Frederik Herzberg, Michael Korenkov, Konstandina Isaakidis et al.

Not available.

Suqiang Ma, Subhadeep Sengupta, Yao Lee et al.

Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells(WBCs), and platelets are significant biomarkers linked to conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have advanced the automatic analysis of single-cell flow cytometry images, there is a lack of effort to build tools to automatically analyze images containing CCCs. Unlike single cells, cell clusters often exhibit irregular shapes and sizes. In addition, these cell clusters often consist of heterogeneous cell types, which require multi-channel staining to identify the specific cell types within the clusters. This study introduces a new computational framework for analyzing CCC images and identifying cell types within clusters. Our framework uses a two-step analysis strategy. First, it categorizes images into cell cluster and non-cluster groups by fine-tuning the You Only Look Once(YOLOv11) model, which outperforms traditional convolutional neural networks (CNNs), Vision Transformers (ViT). Then, it identifies cell types by overlaying cluster contours with regions from multi-channel fluorescence stains, enhancing accuracy despite cell debris and staining artifacts. This approach achieved over 95% accuracy in both cluster classification and phenotype identification. In summary, our automated framework effectively analyzes CCC images from flow cytometry, leveraging both bright-field and fluorescence data. Initially tested on blood cells, it holds potential for broader applications, such as analyzing immune and tumor cell clusters, supporting cellular research across various diseases.

Nimra Muqaddass, Alessandra Jannelli, Francesco Oliveri

Among cardiovascular diseases, atherosclerosis is a primary cause of stenosis, involving the accumulation of plaques in the inner lining of an artery. Inspired by drug delivery applications, the proposed study aims to examine the numerical modeling of a two-dimensional, axisymmetric, and time-dependent hybrid nanofluid composed of copper $(Cu)$, alumina $(Al_{2}O_{3})$ nanoparticles, and blood as base fluid. Blood, modeled by the non-Newtonian Casson model, flows through an elliptical stenotic artery. The pulsatile nature of the pressure gradient and magnetic field impact with the Hall current parameter are also taken into account in this study. A finite difference technique, forward in time and central in space (FTCS), is deployed to numerically discretize the transformed dimensionless model using MATLAB. Comprehensive visualization of the effects of hemodynamic, geometric, and nanoscale parameters on transport characteristics, and extensive graphical results for blood flow characteristics are provided. A comparison is made among blood, regular nanofluid, and hybrid nanofluid to analyze their properties in relation to fluid flow and heat transfer. An augmentation in the non-Newtonian parameter results in an amplification of velocity and in a reduction of the temperature profile. Incorporating $Cu$ and $Al_2O_3$ nanoparticles into the fluid results in a decrease of velocity and an increase of temperature. These findings possess significant practical implications for applications where efficient heat transfer is essential, such as in drug delivery systems and the thermal management of biomedical devices. However, the observed reduction in velocity may necessitate modifications to flow conditions to ensure optimal operational performance in these contexts.

Arne Van der Vreken, Karin Vanderkerken, Elke De Bruyne et al.

Abstract CAR T cells are widely applied for relapsed hematological cancer patients. With six approved cell therapies, for Multiple Myeloma and other B-cell malignancies, new insights emerge. Profound evidence shows that patients who fail CAR T-cell therapy have, aside from antigen escape, a more glycolytic and weakened metabolism in their CAR T cells, accompanied by a short lifespan. Recent advances show that CAR T cells can be metabolically engineered towards oxidative phosphorylation, which increases their longevity via epigenetic and phenotypical changes. In this review we elucidate various strategies to rewire their metabolism, including the design of the CAR construct, co-stimulus choice, genetic modifications of metabolic genes, and pharmacological interventions. We discuss their potential to enhance CAR T-cell functioning and persistence through memory imprinting, thereby improving outcomes. Furthermore, we link the pharmacological treatments with their anti-cancer properties in hematological malignancies to ultimately suggest novel combination strategies.

JFO Santos, JTS Tokunaga, APR Rosa et al.

Introdução: O sangue é composto por glóbulos vermelhos (eritrócitos), glóbulos brancos (leucócitos), plaquetas e plasma, sendo o plasma o componente que representa 60% da porção do sangue. A partir da doação de sangue, o plasma é um dos hemocomponentes produzidos e pode ser utilizado para fins terapêuticos, assim como para produção de hemoderivados. A Portaria Consolidação Nº5 (2017) determina que o plasma atinja o volume mínimo de 150 ml, sendo que plasmas com volume inferior devem ser desprezados. Alguns fatores podem interferir no volume do plasma, como o volume da bolsa coletada e o hematócrito do doador. Os parâmetros aceitáveis para doação de sangue são hematócritos mínimos de 38% para mulheres e 39% para homens sendo hematócrito igual ou superior a 54%, desclassificados. Visando compreender a influência de alguns fatores no baixo volume dos plasmas, este estudo buscou avaliar o impacto do perfil dos doadores no volume dos plasmas produzidos a partir de doações de sangue que possuem a produção dos três hemocomponentes (concentrado de hemácias, plaquetas e plasma). Materiais e métodos: Foram coletados os dados de 1.176 amostras entre os meses de Outubro de 2023 a Maio de 2024 em 03 processamentos da Associação Beneficente de Coleta de Sangue - COLSAN. Os dados foram divididos nos grupos: BV (baixo volume) - plasmas produzidos com volume inferior a 150ml e CT (controle) - plasmas produzidos com volume igual e/ou superior a 150 ml. As informações obtidas foram: sexo, hematócrito de doadores e volume de sangue total coletado. Resultados: Em relação ao sexo, no grupo BV, 97% foram doadores do sexo masculino e 3% doadores do sexo feminino, enquanto que no grupo CT, 56% foram doadores do sexo masculino e 43% a doadores do sexo feminino. Na análise total do hematócrito, a média no grupo BV foi de 45,7% e no grupo CT 43%. Quanto ao hematócrito por sexo, no grupo BV, a média foi 46,4% e 43,7% nos doadores do sexo masculino e feminino, respectivamente. No grupo CT, a média foi 44,6% e 40,5% nos doadores do sexo masculino e sexo feminino 40,5%, respectivamente. Em relação ao volume de sangue total coletado, não foi observada diferença entre os grupos. Conclusão: Observamos que no grupo CT a quantidade de doadores do sexo feminino e do sexo masculino foram semelhantes sendo a diferença de apenas 13%, prevalecendo doadores do sexo masculino. Entretanto, ao avaliar o perfil do grupo BV, houve a predominância de doadores do sexo masculino e hematócrito elevado em relação ao grupo CT. No presente estudo, observamos que doadores do sexo masculino apresentam maiores hematócritos, que podem estar relacionados ao menor volume plasmático que resulta em plasmas de menor volume na produção de hemocomponentes. Embora haja prevalência no descarte de plasma por baixo volume associado a doadores do sexo masculino, mais estudos se fazem necessários à fim de avaliar demais fatores que possam contribuir com a produção de plasmas de baixo volume.

Heinz Ludwig, Thomas Melchardt, Ilvy Schweitzer et al.

Abstract Understanding the impact of induction and maintenance therapy on patients’ quality of life (QoL) is important for treatment selection. This study aims to compare patient‐reported QoL between patients treated with KTd or KRd induction therapy and K maintenance therapy or observation. QoL was assessed using the EORTC QOL‐C 30 and QOL‐MY20 questionnaires in the AGMT‐02 study, in which 123 patients with newly diagnosed transplant ineligible multiple myeloma were randomized to nine cycles of either KTd or KRd induction therapy, followed by 12 cycles of K maintenance therapy, or observation. Longitudinal assessments showed statistically significant improvements in global health‐related QoL, various disease symptoms and pain for both treatment regimens. KTd improved insomnia and fatigue, and KRd improved physical functioning. Cross‐sectional comparisons indicated a “slight” superiority of KTd over KRd in several scales, with the exception of higher neuropathy scores with KTd. During maintenance, longitudinal comparisons showed no statistically significant changes. Cross‐sectional comparisons revealed a “slight” improvement in cognitive functioning during carfilzomib therapy, but a worsening in most other QoL scales. Induction therapy led to improvements in most QoL items, while maintenance therapy with K maintenance was associated with “slight” or “moderate” impairments in several QoL scales compared with the observation group.

Mathieu Alonzo, Nathaniel J. Karst, Thomas Podgorski et al.

We present experimental evidence of multiple blood flow configurations in a relatively simple microfluidic network under constant inlet conditions. We provide evidence of multistability and unsteady dynamics and find good agreement with a theoretical {one-dimensional advection} model for blood flow in microvascular networks{ that relies on the widely used laws for rheology and phase separation}. We discuss the ramifications for microfluidic experiments and measurements using blood and implications for in vivo microcirculation. Our findings suggest that further modeling in microvascular networks should discard the usual assumption of unique, steady-state flow solutions, with crucial consequences regarding gas, nutrient, and waste transport.

Wang J, Baidoun F, Tun HW et al.

Jing Wang,1 Firas Baidoun,2 Han W Tun,2 Muhamad Alhaj Moustafa2 1Department of Internal Medicine, Mayo Clinic Florida, Jacksonville, FL, 32224, USA; 2Division of Hematology and Medical Oncology, Mayo Clinic Florida, Jacksonville, FL, 32224, USACorrespondence: Muhamad Alhaj Moustafa, Mayo Clinic Florida, Division of Hematology/Oncology, 4500 San Pablo Road S, Jacksonville, FL, 32224, USA, Tel +1 904-953-2000, Email alhajmoustafa.muhamad@mayo.eduAbstract: Radioimmunotherapy (RIT) with radio-labeled monoclonal antibodies to CD20 produces a high response rate in patients with low-grade B-cell lymphomas. The use of this modality in patients with chronic lymphocytic leukemia (CLL) has been sporadic in clinical trials and was hampered by the extensive marrow involvement seen commonly in patients with CLL, which would produce a high risk for marrow aplasia after treatment with RIT. Herein, we report our experience with RIT in 5 patients with CLL or SLL showing short-lived responses and significant myelosuppression. After 90Y-ibritumomab tiuxetan treatment, the median time to relapse was 65 days, and no cases of MDS or AML were observed during follow-up. All patients experienced grade ≥ 3 thrombocytopenia and neutropenia, with median durations of 39.5 days and 107 days, respectively.Keywords: CD20, chronic lymphocytic leukemia, small lymphocytic lymphoma, radioimmunotherapy, targeted irradiation, Zevalin

Barbara Izzo, Fabrizio Quarantelli, Ciro Del Prete et al.

Mohammad Zolfaghari, Hedieh Sajedi

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.

A. Sharp, A. Williams, S. Blagden et al.

S. Tayabali, R. Baker, E. Nacheva et al.

Mingxiang SONG, Qiming ZHANG, Zi DOU et al.

Objective To establish gas chromatography for the determination of tributyl phosphate(TBP) residues in human prothrombin complex and then verify it. Methods Acid modified polyethylene glycol(PEG)(20M) capillary column was used with n-hexane as solvent. The chromatographic parameters were as follows: gasification chamber temperature at 220 ℃, column temperature at 155 ℃, detector temperature at 220 ℃, column flow rate at 2.0 mL/min, carrier gas as N2, detector as FID, and collection time for 10min. The accuracy, repeatability, linearity, specificity, intermediate precision, detection limit, quantitative limit, range and durability were verified. Results The verification results showed that the method had good specificity. The linear regression correlation coefficient of standard curve was 0.999 90. The recovery rate were 98.4%, 97.5% and 95.7% when the concentration at 50%, 100%(30μg/mL) and 150%, respectively, with an average recovery of 97.2% and a relative deviation of 2.15%. When the concentration was 100%, the repeatability was 2.08%, and the relative deviation of intermediate precision was 1.63%. The detection limit was 0.255 μg/mL, and the quantitative limit was 0.511 μg/mL. After changing capillary chromatographic columns with different batch numbers but the same types and manufacturer, the applicability test of the system met the requirements, and the method had good durability. Conclusion This method can be used for the determination of TBP residues of human prothrombin complex in laboratory.

Raheleh Salehi, Ario Sadafi, Armin Gruber et al.

Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures, staining, illumination, and microscope settings hamper the re-usability of recently developed machine learning methods on data collected from different sites. Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears. The autoencoder is based on an R-CNN architecture allowing it to focus on the relevant white blood cell and eliminate artifacts in the image. To evaluate the quality of the extracted features we use a simple random forest to classify single cells. We show that thanks to the rich features extracted by the autoencoder trained on only one of the datasets, the random forest classifier performs satisfactorily on the unseen datasets, and outperforms published oracle networks in the cross-domain task. Our results suggest the possibility of employing this unsupervised approach in more complicated diagnosis and prognosis tasks without the need to add expensive expert labels to unseen data.

Leonardo Rossi, Marco Valenti, Sara Elisabetta Legler et al.

The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control. To address the problem related to early disease detection and diagnosis on vines plants, a new dataset has been created with the goal of advancing the state-of-the-art of diseases recognition via instance segmentation approaches. This was achieved by gathering images of leaves and clusters of grapes affected by diseases in their natural context. The dataset contains photos of 10 object types which include leaves and grapes with and without symptoms of the eight more common grape diseases, with a total of 17,706 labeled instances in 1,092 images. Multiple statistical measures are proposed in order to offer a complete view on the characteristics of the dataset. Preliminary results for the object detection and instance segmentation tasks reached by the models Mask R-CNN and R^3-CNN are provided as baseline, demonstrating that the procedure is able to reach promising results about the objective of automatic diseases' symptoms recognition.

Senna Staessens, Olivier François, Linda Desender et al.

Abstract Background Mechanical removal of a thrombus by thrombectomy can be quite challenging. For reasons that are not fully understood, some thrombi require multiple passes to achieve successful recanalization, whereas other thrombi are efficiently removed in a single pass. Since first pass success is associated with better clinical outcome, it is important to better understand the nature of thrombectomy resistant thrombi. The aim of this study was therefore to characterize the cellular and molecular composition of a thrombus that was very hard to retrieve via mechanical thrombectomy. Case presentation In a patient that was admitted with a right middle cerebral artery M1-occlusion, 11 attempts using various thrombectomy devices and techniques were required for removal of the thrombus. This peculiar case provided a rare opportunity to perform an in-depth histopathological study of a difficult to retrieve thrombus. Thrombus material was histologically analyzed using hematoxylin and eosin, Martius Scarlet Blue stain (red blood cells and fibrin), Feulgen stain (DNA), von Kossa stain (calcifications) and immunohistochemical analysis of von Willebrand factor, platelets, leukocytes and neutrophil extracellular traps. Histological analysis revealed abnormally high amounts of extracellular DNA, leukocytes, von Willebrand factor and calcifications. Extracellular DNA stained positive for markers of leukocytes and NETs, suggesting that a significant portion of DNA is derived from neutrophil extracellular traps. Conclusion In this unique case of a nearly thrombectomy-resistant stroke thrombus, our study showed an atypical composition compared to the common structural features found in ischemic stroke thrombi. The core of the retrieved thrombus consisted of extracellular DNA that colocalized with von Willebrand factor and microcalcifications. These results support the hypothesis that von Willebrand factor, neutrophil extracellular traps and microcalcifications contribute to mechanical thrombectomy resistance. Such information is important to identify novel targets in order to optimize technical treatment protocols and techniques to increase first pass success rates.

Ansel Blumers, Minglang Yin, Hiroyuki Nakajima et al.

Various biological processes such as transport of oxygen and nutrients, thrombus formation, vascular angiogenesis and remodeling are related to cellular/subcellular level biological processes, where mesoscopic simulations resolving detailed cell dynamics provide a key to understanding and identifying the cellular basis of disease. To break this bottleneck and achieve a biologically meaningful timescale, we propose a multiscale parareal algorithm in which a continuum-based solver supervises a mesoscopic simulation in the time-domain. Using an iterative prediction-correction strategy, the parallel-in-time mesoscopic simulation supervised by its continuum-based counterpart can converge fast. The effectiveness of the proposed method is first verified in a time-dependent flow with a sinusoidal flowrate through a Y-shaped bifurcation channel. Physical quantities of interest including velocity, wall shear stress and flowrate are computed to compare against those of reference solutions, showing a less than 1% relative error on flowrate in the Newtonian flow and a less than 3\% relative error in the non-Newtonian blood flow. The proposed method is then applied to a large-scale mesoscopic simulation of microvessel blood flow in a zebrafish hindbrain for temporal acceleration. The time-dependent blood flow from heartbeats in this realistic vascular network of zebrafish hindbrain is simulated using dissipative particle dynamics as the mesoscopic model, which is supervised by a one-dimensional blood flow model (continuum-based model) in multiple temporal sub-domains. The computational analysis shows that the resulting microvessel blood flow converges to the reference solution after only two iterations. The proposed method is suitable for long-time mesoscopic simulations with complex fluids and geometries.

Dinh-Tuan Phan, Kerwin Kwek Zeming, Sophie Wan Mei Lian et al.

To perform precision medicine in realtime at home, a device capable of long distance continuously monitoring target biomolecules in unprocessed blood under dynamic situations is essential. In this study, an integrative buffer free wireless device is developed to measure drug concentrations in patients blood in real time for remote clinical healthcare. To demonstrate its capability, the drug molecules (i.e., small molecule drug doxorubicin, DOX) are continuously measured in the unprocessed whole blood of live animals (e.g., rats). The dynamic changes of drug concentrations with sub minute temporal resolution are recorded for an extended period of time (8 hours). As an advance in remote diagnosis, this device would benefit the public by enabling long-distance precision medicine to prevent pandemics in advance.

V. Tomazini, D.C. Stocco, J.P.L. Silva et al.