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

Iris Haxhiu

Introduction. Diffuse large B-cell lymphoma (DLBCL) displays profound epigenetic deregulation driving transcriptional plasticity which sustains therapy resistance. EZH2, a key histone methyltransferase frequently altered in germinal center (GC) DLBCL, promotes aberrant H3K27me3 accumulation, rewiring chromatin states, and it is therapeutically targetable with the selective inhibitor tazemetostat. Protein kinase CK2 is a constitutively active serine/threonine kinase that controls oncogenic signaling and multiple epigenetic regulators, including DNA methyl transferase (DNMT1), histone methyltransferases (HMT), and ATP-citrate lyase (ACLY), potentially linking oncogenic signaling to chromatin organization. In DLBCL, CK2 sustains AKT and NF-κB-p65 cascades, yet its impact on epigenetic regulation remains poorly understood. We hypothesized that CK2 inhibition could constrain epigenetic flexibility and potentiate the response of epidrugs such as tazemetostat in GCB DLBCL. Methods. CK2 expression was assessed in DLBCL cell lines versus normal B cells. Functional and epigenetic effects of CK2 inhibition were evaluated using the clinical inhibitor CX-4945 (silmitasertib) and the newly developed selective compound SGC-CK2-1 in EZH2-mutant (OCI-Ly1) and wild-type (OCI-Ly19) GCB-DLBCL cells. Viability, apoptosis, and cell-cycle distribution were determined by viability assays and flow cytometry. Western blotting of total and histone-enriched fractions was used to assess signaling and histone modification changes. A CK2 knockdown OCI-Ly1 model was generated using an IPTG-inducible shRNA system to genetically validate pharmacological inhibition. The potential synergy between CK2 inhibitors and tazemetostat was tested through combined treatment. RESULTS CK2 was overexpressed in DLBCL compared with normal B cells. CK2 blockade reduced cell viability, and induced apoptosis, accompanied by marked increase in repressive histone marks (H3K27me3, H3K9me) and altered histone acetylation activating markers (H3K27ac, H3k9ac), indicating a key role for this kinase in maintaining chromatin flexibility. CK2 inhibition decreased the activating phosphorylation of AKT on Ser129 and modulated DNMT1 and ACLY activity, suggesting a link between CK2 signaling, metabolic control and DNA methylation maintenance. Combination of CK2 inhibitors with tazemetostat produced a remarkable decrease in viability and enhanced apoptosis compared to single treatments. Conclusions. These findings identify CK2 as a central regulator of epigenetic plasticity, linking oncogenic signaling with chromatin modifications. Combined inhibition of CK2 and EZH2 represents a promising strategy to overcome resistance and improve therapeutic outcomes in aggressive B-cell lymphomas.

Bin Xue, Yifan Liu, Aibin Liang et al.

Abstract The field of chimeric antigen receptor (CAR)-T cell therapy is undergoing a paradigm shift from complex ex vivo manufacturing to direct in vivo generation of CAR-T cells. This innovative approach leverages non-viral delivery platforms to reprogram a patient’s own immune cells in situ, promising to overcome critical barriers of cost, scalability, and accessibility. The 2025 American Society of Hematology (ASH) Annual Meeting served as a showcase for groundbreaking preclinical data across a diverse array of non-viral technologies, including advanced lipid nanoparticles (LNPs), virus-like particles (VLPs), and polymeric nanoparticles. This correspondence summarizes the latest reports on these platforms, highlighting their potential to revolutionize the treatment of both autoimmune diseases and hematological malignancies.

Hao Yao, Shi-hui Ren, Lin-hui Wang et al.

Abstract Relapsed/refractory multiple myeloma (RRMM) with extramedullary disease (EMD) represents a challenging condition, with limited treatment options and poor prognosis. We conducted a phase 1 clinical trial to evaluate the safety and effectiveness of a novel bispecific chimeric antigen receptor (CAR) T-cell therapy targeting two antigens, B-cell maturation antigen and G protein-coupled receptor class C group 5 member D (BCMA/GPRC5D), in this high-risk population. A total of 12 patients were enrolled, of whom 3 were excluded due to disease progression or death before CAR T-cell infusion, despite meeting the inclusion criteria, leaving 9 for analysis. The median follow-up was 6.08 months (Interquartile Range [IQR]: 0.9–16.5). All patients received BCMA/GPRC5D bispecific CAR T-cell therapy after bridging therapy with localized radiotherapy or Elranatamab. Efficacy assessments revealed that 100% of patients achieved partial response (PR) or better, with 44.4% achieving complete response (CR). Common adverse events included hematological toxicities such as anemia, leukopenia, and thrombocytopenia. Cytokine release syndrome (CRS) occurred in 66.7% of patients, all of which were grade 1–2, and no neurotoxicity (ICANS) was observed. The 1-year overall survival (OS) and progression-free survival (PFS) rates were 60% and 63%, respectively. Median OS and PFS were not reached. Collectively, these findings highlight a potential therapeutic strategy involving BCMA/GPRC5D dual-targeted CAR T-cell therapy for patients with aggressive forms of multiple myeloma, particularly those with extramedullary disease, and support the need for further exploration and validation in larger, multi-center clinical studies.

Antonio Giovanni Solimando, Cirino Botta

Not available.

Chandrasekar Muthiah, Qinghua Lian, Samantha Benz et al.

Md Taimur Ahad, Sajib Bin Mamun, Sumaya Mustofa et al.

Over the years in object detection several efficient Convolutional Neural Networks (CNN) networks, such as DenseNet201, InceptionV3, ResNet152v2, SEresNet152, VGG19, Xception gained significant attention due to their performance. Moreover, CNN paradigms have expanded to transfer learning and ensemble models from original CNN architectures. Research studies suggest that transfer learning and ensemble models are capable of increasing the accuracy of deep learning (DL) models. However, very few studies have conducted comprehensive experiments utilizing these techniques in detecting and localizing blood malignancies. Realizing the gap, this study conducted three experiments; in the first experiment -- six original CNNs were used, in the second experiment -- transfer learning and, in the third experiment a novel ensemble model DIX (DenseNet201, InceptionV3, and Xception) was developed to detect and classify blood cancer. The statistical result suggests that DIX outperformed the original and transfer learning performance, providing an accuracy of 99.12%. However, this study also provides a negative result in the case of transfer learning, as the transfer learning did not increase the accuracy of the original CNNs. Like many other cancers, blood cancer diseases require timely identification for effective treatment plans and increased survival possibilities. The high accuracy in detecting and categorization blood cancer detection using CNN suggests that the CNN model is promising in blood cancer disease detection. This research is significant in the fields of biomedical engineering, computer-aided disease diagnosis, and ML-based disease detection.

Rabia Asghar, Arslan Shaukat, Usman Akram et al.

Human immune system contains white blood cells (WBC) that are good indicator of many diseases like bacterial infections, AIDS, cancer, spleen, etc. White blood cells have been sub classified into four types: monocytes, lymphocytes, eosinophils and neutrophils on the basis of their nucleus, shape and cytoplasm. Traditionally in laboratories, pathologists and hematologists analyze these blood cells through microscope and then classify them manually. This manual process takes more time and increases the chance of human error. Hence, there is a need to automate this process. In this paper, first we have used different CNN pre-train models such as ResNet-50, InceptionV3, VGG16 and MobileNetV2 to automatically classify the white blood cells. These pre-train models are applied on Kaggle dataset of microscopic images. Although we achieved reasonable accuracy ranging between 92 to 95%, still there is need to enhance the performance. Hence, inspired by these architectures, a framework has been proposed to automatically categorize the four kinds of white blood cells with increased accuracy. The aim is to develop a convolution neural network (CNN) based classification system with decent generalization ability. The proposed CNN model has been tested on white blood cells images from Kaggle and LISC datasets. Accuracy achieved is 99.57% and 98.67% for both datasets respectively. Our proposed convolutional neural network-based model provides competitive performance as compared to previous results reported in literature.

Xiaofan Cai, Ming Li, Chao Chen et al.

Blood, a ubiquitous and fundamental carbohydrate material composed of plasma, red blood cells, white blood cells, and platelets, has been playing an important role in biology, life science, history, and religious study, while graphene has garnered significant attention due to its exceptional properties and extensive range of potential applications. Achieving environmentally friendly, cost-effective growth using hybrid precursors and obtaining high-quality graphene through a straightforward CVD process has been traditionally considered mutually exclusive. This study demonstrates that we can produce high-quality graphene domains with controlled thickness through a one-step growth process at atmospheric pressure using blood as a precursor. Raman spectroscopy confirms the uniformity of the blood-grown graphene films, and observing the half-integer quantum Hall effect in the measured devices highlights its outstanding electronic properties. This unprecedented approach opens possibilities for blood application, facilitating an unconventional route in graphene growth applications.

Gyutae Hwang, Sang Jun Lee

Human health can be critically affected by cardiovascular diseases, such as hypertension, arrhythmias, and stroke. Heart rate and blood pressure are important biometric information for the monitoring of cardiovascular system and early diagnosis of cardiovascular diseases. Existing methods for estimating the heart rate are based on electrocardiography and photoplethyomography, which require contacting the sensor to the skin surface. Moreover, catheter and cuff-based methods for measuring blood pressure cause inconvenience and have limited applicability. Therefore, in this thesis, we propose a vision-based method for estimating the heart rate and blood pressure. This thesis proposes a 2-stage deep learning framework consisting of a dual remote photoplethysmography network (DRP-Net) and bounded blood pressure network (BBP-Net). In the first stage, DRP-Net infers remote photoplethysmography (rPPG) signals for the acral and facial regions, and these phase-shifted rPPG signals are utilized to estimate the heart rate. In the second stage, BBP-Net integrates temporal features and analyzes phase discrepancy between the acral and facial rPPG signals to estimate SBP and DBP values. To improve the accuracy of estimating the heart rate, we employed a data augmentation method based on a frame interpolation model. Moreover, we designed BBP-Net to infer blood pressure within a predefined range by incorporating a scaled sigmoid function. Our method resulted in estimating the heart rate with the mean absolute error (MAE) of 1.78 BPM, reducing the MAE by 34.31 % compared to the recent method, on the MMSE-HR dataset. The MAE for estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 10.19 mmHg and 7.09 mmHg. On the V4V dataset, the MAE for the heart rate, SBP, and DBP were 3.83 BPM, 13.64 mmHg, and 9.4 mmHg, respectively.

Alexander Farutin, Abdessamad Nait-Ouhra, Gopal Dixit et al.

Despite decades of research on blood flow, an analogue of Navier-Stokes equations that accurately describe blood flow properties has not been established yet. The reason behind this is that the properties of blood flow seem à priori non universal as they depend on various factors such as global concentration of red blood cells (RBCs) and channel width. Here, we have discovered a universal law when the stress and strain rate are measured at a given local RBCs concentration. However, the local concentration must be determined in order to close the problem. We propose a non-local diffusion equation of RBCs concentration that agrees with the full simulation. The universal law is exemplified for both shear and pressure driven flows. While the theory is restricted to a simplistic geometry (straight channel) it provides a fundamental basis for future research on blood flow dynamics and could lead to the development of a new theory that accurately describes blood flow properties under various conditions, such as in complex vascular networks.

Naoki Takeishi, Tomohiro Nishiyama, Kodai Nagaishi et al.

The deformability of human red blood cells (RBCs), which comprise almost 99% of the cells in whole blood, is largely related not only to pathophysiological blood flow but also to the levels of intracellular compounds. Therefore, statistical estimates of the deformability of individual RBCs are of paramount importance in the clinical diagnosis of blood diseases. Although the micro-scale hydrodynamic interactions of individual RBCs lead to non-Newtonian blood rheology, there is no established method to estimate individual RBC deformability from the rheological data of RBC suspensions, and the possibility of this estimation has not been proven. To address this issue, we conducted an integrated analysis of a model of the rheology of RBC suspensions, coupled with macro-rheological data of human RBCs suspended in plasma. Assuming a non-linear curve of the relative viscosity of the suspensions as a function of the cell volume fraction, the statistical average of the membrane shear elasticity was estimated for individual intact RBCs or hardened RBCs. Both estimated values reproduced well the experimentally observed shear-thinning non-Newtonian behavior in these suspensions. We hereby conclude that our complementary approach makes it possible to estimate the statistical average of individual RBC deformability from macro-rheological data obtained with usual rheometric tests.

Rialda Myrtaj, Liri Seraj, Evriola Dede et al.

Marta Molina-Pérez, Jorge M. Nieto, Paloma Ropero Gradilla et al.

Barry K. Gilbert, Clifton R. Haider, Daniel J. Schwab et al.

Significance: A path is described to increase the sensitivity and accuracy of body-worn devices used to monitor patient health. This path supports improved health management. A wavelength-choice algorithm developed at Mayo demonstrates that critical biochemical analytes can be assessed using accurate optical absorption curves over a wide range of wavelengths. Aim: Combine the requirements for monitoring cardio/electrical, movement, activity, gait, tremor, and critical biochemical analytes including hemoglobin makeup in the context of body-worn sensors. Use the data needed to characterize clinically important analytes in blood samples to drive instrument requirements. Approach: Using data and knowledge gained over previously separate research threads, some providing currently usable results from more than eighty years back, determine analyte characteristics needed to design sensitive and accurate multiuse measurement and recording units. Results: Strategies for wavelength selection are detailed. Fine-grained, broad-spectrum measurement of multiple analytes transmission, absorption, and anisotropic scattering are needed. Post-Beer-Lambert, using the propagation of error from small variations, and utility functions that include costs and systemic error sources, improved measurements can be performed. Conclusions: The Mayo Double-Integrating Sphere Spectrophotometer (referred hereafter as MDISS), as described in the companion report arXiv:2212.08763, produces the data necessary for optimal component choice. These data can provide for robust enhancement of the sensitivity, cost, and accuracy of body-worn medical sensors. Keywords: Bio-Analyte, Spectrophotometry, Body-worn monitor, Propagation of error, Double-Integrating Sphere, Mt. Everest medical measurements, O2SAT Please see also arXiv:2212.08763

Kanaya Malakar, Rafael I. Rubenstein, Dapeng Bi et al.

The organization of cells within tissues plays a vital role in various biological processes, including development and morphogenesis. As a result, understanding how cells self-organize in tissues has been an active area of research. In our study, we explore a mechanistic model of cellular organization that represents cells as force dipoles that interact with each other via the tissue, which we model as an elastic medium. By conducting numerical simulations using this model, we are able to observe organizational features that are consistent with those obtained from vertex model simulations. This approach provides valuable insights into the underlying mechanisms that govern cellular organization within tissues, which can help us better understand the processes involved in development and disease.

Ario Sadafi, Raheleh Salehi, Armin Gruber et al.

Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving clinical settings, data sources, and disease classifications, it is necessary to update machine learning classification models regularly for practical real-world use. Such models significantly benefit from sequentially learning from incoming data streams without forgetting previously acquired knowledge. However, models can suffer from catastrophic forgetting, causing a drop in performance on previous tasks when fine-tuned on new data. Here, we propose a rehearsal-based continual learning approach for class incremental and domain incremental scenarios in white blood cell classification. To choose representative samples from previous tasks, we employ exemplar set selection based on the model's predictions. This involves selecting the most confident samples and the most challenging samples identified through uncertainty estimation of the model. We thoroughly evaluated our proposed approach on three white blood cell classification datasets that differ in color, resolution, and class composition, including scenarios where new domains or new classes are introduced to the model with every task. We also test a long class incremental experiment with both new domains and new classes. Our results demonstrate that our approach outperforms established baselines in continual learning, including existing iCaRL and EWC methods for classifying white blood cells in cross-domain environments.

Lionel Blanc, Raffaele Renella

S. Delicou, M. Triantafyllou, A. Xydaki et al.

J. Mei, Y. Wang, S. Kabir et al.

Z. Wang, S. Cui, J. Xu et al.