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

Anisa Bici, Denise Sutter-Long, Liza Renner et al.

Elisa Lenti, Edoardo Visentin, Engin Bojnik et al.

Abstract Chronic lymphocytic leukemia (CLL) B cells are characterized by a propensity to undergo rapid apoptosis when cultured in vitro, underscoring the importance of the tissue microenvironment for disease survival. One of the major limitations in studying the role of the microenvironment in tumor development and drug response is the inadequacy of conventional two‐dimensional (2D) in vitro assays to physiologically reconstruct the complex spatial organization and interactions of cells in their natural lymphoid niches. To overcome this limitation, we developed a novel in vitro 3D lymph node‐like spheroid model of the leukemic microenvironment by culturing human CLL cells with fibroblastic reticular cells (FRCs). FRCs are a key structural component of secondary lymphoid organs and are emerging as crucial players in tissue homeostasis and immune responses. Our results demonstrate that CLL spheroids maintain the physiological cellular ratio between FRCs and leukemic cells over time and protect tumor cells from apoptosis by mimicking the protective effects of the microenvironment. This was further demonstrated by venetoclax treatment that showed reduced apoptosis in 3D compared to a 2D setting. Importantly, the spheroids promote a gene expression profile more aligned with that of CLL cells in lymphoid tissues. The spheroid model provides a straightforward, quick‐to‐use platform for investigating drug efficacy under conditions that better replicate the natural lymph node microenvironment. This 3D lymph node‐like spheroid model could serve as a valuable tool for studying tumor biology and the protective effects of the stromal microenvironment, and for testing therapeutic strategies in a more clinically relevant setting.

Kyriaki-Evangelia Aslani, Ioannis E. Sarris, Efstratios Tzirtzilakis

This study presents a numerical investigation of a 3D micropolar magnetohydrodynamic (MHD) blood flow through stenosis, with and without the effects of micromagnetorotation (MMR). MMR refers to the magnetic torque caused by the misalignment of the magnetization of magnetic particles in the fluid with the magnetic field, which affects the internal rotation (microrotation) of these particles. Blood can be modeled as a micropolar fluid with magnetic particles due to the magnetization of erythrocytes. In this manner, this study analyzes important flow features, i.e., streamlines, vorticity, velocity, microrotation, wall shear stress, and pressure drop under varying stenosis, hematocrit levels, and magnetic fields, using two newly developed transient OpenFOAM solvers epotMicropolarFoam and epotMMRFoam. Results indicate that micropolar effects become more pronounced at severe stenosis due to the significant reduction in artery size, resulting also in higher wall shear stress and pressure drop. Furthermore, when MMR is disregarded, the magnetic field does not significantly alter blood flow, regardless of its intensity, due to the minimal impact of the Lorentz force on blood. Conversely, MMR substantially affects blood flow, particularly at higher hematocrit levels and severe stenoses, leading to reductions of up to 30% in velocity and vorticity and up to 99.9% in microrotation and higher wall shear stress and pressure drop. Simultaneously, any vortices or disturbances are dampened. These findings underscore the critical role of MMR (which was ignored so far) in altering flow behavior in stenosed arteries, suggesting that it should be considered in future MHD micropolar blood flow studies.

Hadi Barati, Arian Mousavi Madani, Soheil Moradi et al.

In this work, the blood NIR absorbances are recorded using the FT-IR method. It is shown that when the absorbance curves are multiplied by the first derivative of the water absorbance spectrum as well as by the first derivative of the glucose absorbance, the peaks related to the water interferent in the blood are effectively removed from the blood absorbance spectra, allowing for better distinction of the peaks of the blood glucose. The PCR prediction using this method shows smaller errors compared to the PCR employing the net absorbances, while the number of derived principal components is smaller in the PCR method based on the derivatives than the one based on the net absorbances. Additionally, the prediction of blood glucose levels using a linear regression model based on the molar absorptivity of glucose also demonstrates acceptable accuracy.

Alena Jarolímová, Jaroslav Hron, Karel Tůma et al.

The assumption that blood adheres to vessel walls, the ``no-slip'' boundary condition, is an essential premise of cardiovascular fluid dynamics. Yet, whether it holds true \emph{in vivo} has not been established. Using 4D flow magnetic resonance imaging of the human thoracic aorta and modeling blood as a Navier--Stokes fluid, we quantify the velocity of blood at the wall. We find tangential wall velocities of about 30--80\% of the mean luminal velocity, providing clear evidence of blood slippage. To our knowledge, this is the first demonstration that the no-slip condition does not apply to blood flow \emph{in vivo}. This finding challenges a fundamental assumption in cardiovascular modeling and directly affects key blood flow characteristics such as pressure drop, vorticity, wall shear stress, and energy dissipation, all of which play important roles across a wide range of cardiovascular conditions.

Ivica Marić, Klemen Žiberna, Ana Kolenc et al.

Morgana Pinheiro Maux Lessa, Alexandre Soares Ferreira Junior, Margaret Graton et al.

RP Battaglini, ACF Marret, LC Nadai et al.

Introdução: : A terapia com células CAR-T trouxe um grande avanço no tratamento de pacientes oncohematológicos, especialmente aqueles para os quais já não há mais opção terapêutica eficaz. A complicação mais frequente é o desenvolvimento de citopenias, seja pela linfodepleção, pela terapia-ponte ou pelo tratamento da doença. Além da leucaférese e da infusão do produto final, o serviço de Hemoterapia tem um papel importante no apoio ao tratamento destes pacientes através da assistência transfusional adequada. Objetivo: : Avaliar o perfil transfusional dos pacientes atendidos no Hospital BP e submetidos ao TMO e terapia CAR-T. Material e métodos: : Foram avaliados os dados de prontuário de todos os 101 pacientes submetidos a TMO autólogo (n = 49), alogênico (n = 44) e terapia com células CAR-T (n = 8), no período de dez/2023 a jun/2024, quanto ao sexo, idade, tempo de internação (da infusão até alta ou óbito), dose de células CD34+, Hb no dia da infusão, quantidade e tipo de hemocomponente transfundido (concentrado de hemácias, pool de plaquetas randômicas, concentrado de plaquetas por aférese, plasma fresco congelado, crioprecipitado e concentrado de granulócitos), e presença e tipo de reação adversa após infusão de CAR-T. Resultados: : Os pacientes que receberam CAR-T apresentaram uma idade média de 41,8 anos (4-72), sendo 5 do sexo masculino e 3 do sexo feminino, e os produtos infundidos foram o Axi-cel (3) ou o Tisa-cel (5), em doses variáveis. Os pacientes submetidos ao TMO autólogo apresentaram uma média de idade de 53,1 anos (4-74), sendo 27 do sexo masculino (55%). Os submetidos ao TMO alogênico tiveram idade média de 40,19 anos (4m-73), sendo 24 (54%) do sexo masculino. A dose média de células CD34+ infundidas (x106/kg) foi de 4,25 para os autólogos e 7,19 para os alogênicos. Os pacientes que receberam CAR-T ficaram internados, em média, por 21,2 dias após a infusão, enquanto os de TMO autólogo ficaram, em média, 15,6 dias, e os alogênicos, 27,1 dias. A Hb média no dia da infusão do CAR-T foi de 9,5g/dl, no TMO autólogo de 10,9g/dl e TMO alogênico de 9,8g/dl. A média do número de hemocomponentes transfundidos por paciente durante a internação foi de 2,2 hemocomponentes para os pacientes de CAR-T, 3,4 no TMO autólogo e 9,9 no TMO alogênico. A média do número de hemocomponentes transfundidos por paciente e por tipo de hemocomponente nos pacientes de CAR-T foi de 0,87 CH, 1,12 CPAF, 0 pool de plaquetas, 0 PFC, 0,25 crio e 0 CG. No TMO autólogo foi de 0,86 CH, 1,8 CPAF, 0,68 pool de plaquetas e nenhum PFC, crio ou CG. No TMO alogênico foi de 4,02 CH, 5,3 CPAF, 0,38 pool plaquetas, 0,11 PFC, 0 crio e 0,07 CG. Todos os pacientes de CAR-T apresentaram algum tipo de reação adversa: todos tiveram CRS de grau≤2 e metade apresentou ICANS de grau ≤ 3. Discussão: : Os pacientes de CAR-T ficaram internados por mais tempo que os submetidos a TMO autólogo e menos do que os de TMO alogênico e o hemocomponente mais transfundido foi o concentrado de plaquetas (aférese ou pool) para todos os tipos de pacientes. Os pacientes de CAR-T receberam quantidade semelhante de CH que os de TMO autólogo, mas uma quantidade menor de plaquetas, e ambos receberam menos transfusão que os pacientes de TMO alogênico. Conclusão: : Os pacientes submetidos à terapia com células CAR-T apresentam perfil transfusional semelhante ao de pacientes submetidos ao TMO autólogo, sendo imprescindível o adequado suporte hemoterápico destes pacientes para garantir a sua segurança durante o tratamento.

Oana Diana Preda, Sorina Bădeliță, Iulia Ursuleac et al.

<b>Background</b>: Brentuximab Vedotin (BV) has revolutionized the treatment landscape for Hodgkin’s lymphoma, yet its effects on pre-existing autoimmune disorders remain elusive. <b>Methods</b>: Here, we present four cases of patients with concurrent autoimmune conditions—Crohn’s disease, vitiligo, type I diabetes, and minimal change disease—undergoing BV therapy for Hodgkin’s lymphoma. The patients were treated with A-AVD instead of ABVD due to advanced-stage disease with high IPI scores. <b>Results:</b> Our findings reveal the surprising and complex interplay between BV exposure and autoimmune manifestations, highlighting the need for multidisciplinary collaboration in patient management. Notably, the exacerbation of autoimmune symptoms was observed in the first three cases where T-cell-mediated autoimmunity predominated. Additionally, BV exposure precipitated autoimmune thrombocytopenia in the vitiligo patient, underscoring the profound disruptions in immune regulation. Conversely, in the minimal change disease case, a disease characterized by a blend of B- and T-cell-mediated immunity, the outcome was favorable. <b>Conclusions</b>: This paper underscores the critical importance of vigilance toward autoimmune flare-ups induced by BV in patients with concurrent autoimmune conditions, offering insights for tailored patient care.

Ahmed Mazen Amin, Ramy Ghaly, Mohamed T. Abuelazm et al.

Abstract Background Clinical decision support systems (CDSS) have been utilized as a low-cost intervention to improve healthcare process measures. Thus, we aim to estimate CDSS efficacy to optimize adherence to oral anticoagulant guidelines in eligible patients with atrial fibrillation (AF). Methods A systematic review and meta-analysis of randomized controlled trials (RCTs) retrieved from PubMed, WOS, SCOPUS, EMBASE, and CENTRAL through August 2023. We used RevMan V. 5.4 to pool dichotomous data using risk ratio (RR) with a 95% confidence interval (CI). PROSPERO ID: CRD42023471806. Results We included nine RCTs with a total of 25,573 patients. There was no significant difference, with the use of CDSS compared to routine care, in the number of patients prescribed anticoagulants (RR: 1.06, 95% CI [0.98, 1.14], P = 0.16), the number of patients prescribed antiplatelets (RR: 1.01 with 95% CI [0.97, 1.06], P = 0.59), all-cause mortality (RR: 1.19, 95% CI [0.31, 4.50], P = 0.80), major bleeding (RR: 0.84, 95% CI [0.21, 3.45], P = 0.81), and clinically relevant non-major bleeding (RR: 1.05, 95% CI [0.52, 2.16], P = 0.88). However, CDSS was significantly associated with reduced incidence of myocardial infarction (RR: 0.18, 95% CI [0.06, 0.54], P = 0.002) and cerebral or systemic embolic event (RR: 0.11, 95% CI [0.01, 0.83], P = 0.03). Conclusion We report no significant difference with the use of CDSS compared to routine care in anticoagulant or antiplatelet prescription in eligible patients with AF. CDSS was associated with a reduced incidence of myocardial infarction and cerebral or systemic embolic events.

Tulasi Geevar, Yasmeen Abulkhair, Cuihong Wei et al.

A. Ali Heydari, Naghmeh Rezaei, Javier L. Prieto et al.

Blood biomarkers are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. Current reference values and recommended ranges often rely on population-level statistics, which may not adequately account for the influence of inter-individual variability driven by factors such as lifestyle and genetics. In this work, we introduce a novel framework for predicting future blood biomarker values and define personalized references through learned representations from lifestyle data (physical activity and sleep) and blood biomarkers. Our proposed method learns a similarity-based embedding space that captures the complex relationship between biomarkers and lifestyle factors. Using the UK Biobank (257K participants), our results show that our deep-learned embeddings outperform traditional and current state-of-the-art representation learning techniques in predicting clinical diagnosis. Using a subset of UK Biobank of 6440 participants who have follow-up visits, we validate that the inclusion of these embeddings and lifestyle factors directly in blood biomarker models improves the prediction of future lab values from a single lab visit. This personalized modeling approach provides a foundation for developing more accurate risk stratification tools and tailoring preventative care strategies. In clinical settings, this translates to the potential for earlier disease detection, more timely interventions, and ultimately, a shift towards personalized healthcare.

Y. Ben-Ami, B. D. Wood, J. M. Pitt-Francis et al.

In this work we develop a homogenisation methodology to upscale mathematical descriptions of microcirculatory blood flow from the microscale (where individual vessels are resolved) to the macroscopic (or tissue) scale. Due to the assumed two-phase nature of blood and specific features of red blood cells (RBCs), mathematical models for blood flow in the microcirculation are highly nonlinear, coupling the flow and RBC concentrations (haematocrit). In contrast to previous works which accomplished blood-flow homogenisation by assuming that the haematocrit level remains constant, here we allow for spatial heterogeneity in the haematocrit concentration and thus begin with a nonlinear microscale model. We simplify the analysis by considering the limit of small haematocrit heterogeneity which prevails when variations in haematocrit concentration between neighbouring vessels are small. Homogenisation results in a system of coupled, nonlinear partial differential equations describing the flow and haematocrit transport at the macroscale, in which a nonlinear Darcy-type model relates the flow and pressure gradient via a haematocrit-dependent permeability tensor. During the analysis we obtain further that haematocrit transport at the macroscale is governed by a purely advective equation. Applying the theory to particular examples of two- and three-dimensional geometries of periodic networks, we calculate the effective permeability tensor associated with blood flow in these vascular networks. We demonstrate how the statistical distribution of vessel lengths and diameters, together with the average haematocrit level, affect the statistical properties of the macroscopic permeability tensor. These data can be used to simulate blood flow and haematocrit transport at the macroscale.

Irem Topal, Alexander Cherevko, Yuri Bugay et al.

Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the postoperative period, making the management of these conditions highly challenging. Parameters of cerebral blood flow, routinely monitored during medical interventions or with modern noninvasive high-resolution imaging methods, could potentially be utilized in machine learning-assisted protocols for risk assessment and therapeutic prognosis. To this end, we developed a linear oscillatory model of blood velocity and pressure for clinical data acquired from neurosurgical operations. Using the method of Sparse Identification of Nonlinear Dynamics (SINDy), the parameters of our model can be reconstructed online within milliseconds from a short time series of the hemodynamic variables. The identified parameter values enable automated classification of the blood-flow pathologies by means of logistic regression, achieving an accuracy of 73 \%}. Our results demonstrate the potential of this model for both diagnostic and prognostic applications, providing a robust and interpretable framework for assessing cerebral blood vessel conditions.

Kavian Khanjani, Seyed Rasoul Hosseini, Hamid Taheri et al.

In 2019, the world faced a new challenge: a COVID-19 disease caused by the novel coronavirus, SARS-CoV-2. The virus rapidly spread across the globe, leading to a high rate of mortality, which prompted health organizations to take measures to control its transmission. Early disease detection is crucial in the treatment process, and computer-based automatic detection systems have been developed to aid in this effort. These systems often rely on artificial intelligence (AI) approaches such as machine learning, neural networks, fuzzy systems, and deep learning to classify diseases. This study aimed to differentiate COVID-19 patients from others using self-categorizing classifiers and employing various AI methods. This study used two datasets: the blood test samples and radiography images. The best results for the blood test samples obtained from San Raphael Hospital, which include two classes of individuals, those with COVID-19 and those with non-COVID diseases, were achieved through the use of the Ensemble method (a combination of a neural network and two machines learning methods). The results showed that this approach for COVID-19 diagnosis is cost-effective and provides results in a shorter amount of time than other methods. The proposed model achieved an accuracy of 94.09% on the dataset used. Secondly, the radiographic images were divided into four classes: normal, viral pneumonia, ground glass opacity, and COVID-19 infection. These were used for segmentation and classification. The lung lobes were extracted from the images and then categorized into specific classes. We achieved an accuracy of 91.1% on the image dataset. Generally, this study highlights the potential of AI in detecting and managing COVID-19 and underscores the importance of continued research and development in this field.

Shasha Wu, Jiao Jin, Jing Huang et al.

ABSTRACTObjective The prognosis of acute myeloid leukemia (AML) remains poor although the basic and translational research has been highly productive in understanding the genetics and pathopoiesis of AML and a plethora of targeted therapies have been developed. Consequently, it is crucial to deepen the knowledge of molecular pathogenesis underlying AML for the advancement of new treatment options.Method A RSK gene family-related signature was constructed to investigate whether RSK gene family members were useful in predicting the prognosis of AML patients. The relationship between the RSK gene family-related signature and the infiltration of immune cells was further assessed using the CIBERSORT algorithm. The ‘oncoPredict’ package was used to analyze relationships between the RSK gene family-related signature and the sensitivity to drugs or small molecules.Results Patients were classified into two groups using the RSK gene family-related signature following the median risk score. Overall survival (OS) was significantly longer in patients with low-risk scores than that in patients with high-risk scores as showed by both training and validation datasets. Moreover, the signature was helpful in predicting 1-year, 3-year, and 5-year OS in training and validation datasets. In addition, it was identified that low-risk patients exhibited greater sensitivity to 20 drugs or small molecules and that high-risk patients had higher sensitivity to 38 drugs or small molecules.Conclusion RSK gene family members, particularly RPS6KA1 and RPS6KA4, may help to predict prognosis for AML patients. Furthermore, RPS6KA1 may serve as a novel drug target for AML.

Edvan de Queiroz Crusoe, Abrahão Elias Hallack Neto, Deise Ferreira Nantes et al.

Tetsuya Yamamoto, Hiroshi Watanabe

It is important to understand the dynamics of red blood cells (RBCs) in blood flow. This requires the formulation of coarse-grained RBC models that reproduce the hydrodynamic properties of blood accurately. One of the models that successfully reproduce the rheology and morphology of blood has been proposed by Fedosov et al. [D. A. Fedosov, B. Caswell, and G. E. Karniadakis, Comput. Methods Appl. Mech. Eng., Vol. 199, 1937-1948 (2010)]. The proposed RBC model contains several parameters whose values are determined either by various experiments or physical requirements. In this study, we developed a new method of determining the parameter values precisely from the fluctuations of the RBC membrane. Specifically, we studied the relationship between the spectra of the fluctuations and model parameters. Characteristic peaks were observed in the spectra, whose peak frequencies were dependent on the parameter values. In addition, we investigated the spectra of the radius of gyration. We identified the peaks originating from the spring potential and the volume-conserving potential appearing in the spectra. These results lead to the precise experimental determination of the parameters used in the RBC model.

Achyut Tiwari, Aryan Chugh, Aman Sharma

Heart disease is the major cause of non-communicable and silent death worldwide. Heart diseases or cardiovascular diseases are classified into four types: coronary heart disease, heart failure, congenital heart disease, and cardiomyopathy. It is vital to diagnose heart disease early and accurately in order to avoid further injury and save patients' lives. As a result, we need a system that can predict cardiovascular disease before it becomes a critical situation. Machine learning has piqued the interest of researchers in the field of medical sciences. For heart disease prediction, researchers implement a variety of machine learning methods and approaches. In this work, to the best of our knowledge, we have used the dataset from IEEE Data Port which is one of the online available largest datasets for cardiovascular diseases individuals. The dataset isa combination of Hungarian, Cleveland, Long Beach VA, Switzerland & Statlog datasets with important features such as Maximum Heart Rate Achieved, Serum Cholesterol, Chest Pain Type, Fasting blood sugar, and so on. To assess the efficacy and strength of the developed model, several performance measures are used, such as ROC, AUC curve, specificity, F1-score, sensitivity, MCC, and accuracy. In this study, we have proposed a framework with a stacked ensemble classifier using several machine learning algorithms including ExtraTrees Classifier, Random Forest, XGBoost, and so on. Our proposed framework attained an accuracy of 92.34% which is higher than the existing literature.