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Leukemia is a malignancy that affects the blood and bone marrow. Its detection and classification are conventionally done through labor-intensive and specialized methods. The diagnosis of blood cancer in children is a critical task that requires high precision and accuracy. This study proposes a novel approach utilizing attention mechanism-based machine learning in conjunction with image processing techniques for the precise detection and classification of leukemia cells. The proposed attention-augmented algorithm for blood cancer detection in children (A2M-LEUK) is an innovative algorithm that leverages attention mechanisms to improve the detection of blood cancer in children. A2M-LEUK was evaluated on a dataset of blood cell images and achieved remarkable performance metrics: Precision = 99.97%, Recall = 100.00%, F1-score = 99.98%, and Accuracy = 99.98%. These results indicate the high accuracy and sensitivity of the proposed approach in identifying and categorizing leukemia, and its potential to reduce the workload of medical professionals and improve the diagnosis of leukemia. The proposed method provides a promising approach for accurate and efficient detection and classification of leukemia cells, which could potentially improve the diagnosis and treatment of leukemia. Overall, A2M-LEUK improves the diagnosis of leukemia in children and reduces the workload of medical professionals.

PurposeImmunogenomics and earlier, pioneering studies, particularly by Whiteside and colleagues, have indicated a positive role for B-cells in breast cancer, as well as a positive role for gamma-delta T-cells. However, these studies have been completely limited to assessing breast cancer tumor tissue.Methods and ResultsOur analyses here has shown that blood-borne T-cell receptor gamma (TRG) chain sequences were associated with greater overall survival, of particular note due to the comparative longevity of primary breast cancer patients, whereby assessments of disease-free, but rarely overall survival parameters are possible. Additional immunogenomics approaches narrowed the overall survival correlations to specific, TRG complementarity determining region-3, amino acid (AA) sequence chemical features, independently of many common, confounding variables in the breast cancer setting, such as estrogen or progesterone receptor status.ConclusionsThese results are discussed in the context of patient age and with regard to potential antigenic targets, based on the chemistry of the TRG CDR3 AA sequences associated with the higher survival rates.

Blood cancers are a type of liquid tumor which means cancer is present in the body fluid. Multiple myeloma, leukemia, and lymphoma are the three common types of blood cancers. Chemotherapy is the major therapy of blood cancers by systemic administration of anticancer agents into the blood. However, a high incidence of relapse often happens, due to the low efficiency of the anticancer agents that accumulate in the tumor site, and therefore lead to a low survival rate of patients. This indicates an urgent need for a targeted drug delivery system to improve the safety and efficacy of therapeutics for blood cancers. In this review, we describe the current targeting strategies for blood cancers and recently investigated and approved drug delivery system formulations for blood cancers. In addition, we also discuss current challenges in the application of drug delivery systems for treating blood cancers.

Blood cancers are characterized by pathological disorders causing uncontrolled hematological cell division. Various strategies were previously explored for the treatment of blood cancers, including chemotherapy, Car-T therapy, targeting chimeric antigen receptors, and platelets therapy. However, all these therapies pose serious challenges that limit their use in blood cancer therapy, such as poor metabolism. Furthermore, the solubility and stability of anticancer drugs limit efficacy and bio-distribution and cause toxicity. The isolation and purification of natural killer cells during Car-T cell therapy is a major challenge. To cope with these challenges, treatment strategies from phyto-medicine scaffolds have been evaluated for blood cancer treatments. Carotenoids represent a versatile class of phytochemical that offer therapeutic efficacy in the treatment of cancer, and specifically blood cancer. Carotenoids, through various signaling pathways and mechanisms, such as the activation of AMPK, expression of autophagy biochemical markers (p62/LC3-II), activation of Keap1-Nrf2/EpRE/ARE signaaling pathway, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), increased level of reactive oxygen species, cleaved poly (ADP-ribose) polymerase (c-PARP), c-caspase-3, -7, decreased level of Bcl-xL, cycle arrest at the G0/G1 phase, and decreasing STAT3 expression results in apoptosis induction and inhibition of cancer cell proliferation. This review article focuses the therapeutic potential of carotenoids in blood cancers, addressing various mechanisms and signaling pathways that mediate their therapeutic efficacy.

Haemophagocytic lymphohistiocytosis (HLH) is a lethal syndrome of excessive immune activation. We undertook a nationwide study in England of all cases of HLH diagnosed between 2003 and 2018, using linked electronic health data from hospital admissions and death certification. We modelled interactions between demographics and comorbidities and estimated one-year survival by calendar year, age group, gender and comorbidity (haematological malignancy, auto-immune, other malignancy) using Cox regression. There were 1628 people with HLH identified. Overall, crude one-year survival was 50% (95% Confidence interval 48–53%) which varied substantially with age (0–4: 61%; 5–14: 76%; 15–54: 61%; > 55: 24% p  < 0.01), sex (males, 46%, worse than females, 55% p  < 0.01) and associated comorbidity (auto-immune, 69%, haematological malignancy 28%, any other malignancy, 37% p  < 0.01). Those aged < 54 years had a threefold increased risk of death at 1-year amongst HLH associated with malignancy compared to auto-immune. However, predicted 1-year survival decreased markedly with age in those with auto-immune (age 0–14, 84%; 15–54, 73%; > 55, 27%) such that among those > 55 years, survival was as poor as for patients with haematological malignancy. One-year survival following a diagnosis of HLH varies considerably by age, gender and associated comorbidity. Survival was better in those with auto-immune diseases among the young and middle age groups compared to those with an underlying malignancy, whereas in older age groups survival was uniformly poor regardless of the underlying disease process.

In this study, we apply QSPR analysis to some potent drugs used in blood cancer treatment using eccentricity‐based topological indices, which are structural descriptors capturing the shape and size of the molecules as well as their branching and symmetry. These index values are computed by partitioning the corresponding molecular graphs of the drugs for both vertex and edge sets based on their degrees and eccentricities. Then, cubic regression analysis is incorporated into the structural property relational models with these indices and their corresponding physical properties. This work also deals with the various statistical parameters that affect the goodness of the model, and their error analysis is discussed, thereby producing impactful regression models through the graphical depiction of their curve fits.

Cancer is a disease linked to the untamed and rapid division of cells in the body. Cancer detection through conventional methods like complete blood count is a tedious and time-consuming task prone to human errors. The introduction of image processing techniques and computer-aided diagnostics is beneficial to this field as the results obtained by utilizing these methods are quick and accurate. The proposed method in this paper uses a design Convolutional Leaky RELU with CatBoost and XGBoost (CLR-CXG) to segment the images and extract the important features that help in classification. The binary classification algorithm and gradient boosting algorithm CatBoost (Categorical Boost) and XGBoost (Extreme Gradient Boost) are implemented individually. Moreover, Convolutional Leaky RELU with CatBoost (CLRC) is designed to decrease bias and provide high accuracy, while Convolutional Leaky RELU with XGBoost (CLRXG) is designed for classification or regression prediction problems which will increase the speed of executing the algorithm and improve its performance. Thus the CLR-CXG classifies the test images into Acute Lymphoblastic Leukemia (ALL) or Multiple Myeloma (MM). Finally, the CLRC algorithm achieved 100% accuracy in classifying cancer cells, and the recorded run time is 10s. Moreover, the CLRXG algorithm has gained an accuracy of 97.12% for classifying cancer cells and 12 s for executing the process.

Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

BackgroundPeer support has been utilized and associated with clinical outcomes (e.g., improved mood) in patients with solid malignancies. However, to date, there is minimal literature examining peer support among patients with hematologic malignancies and/or patients who have undergone hematopoietic stem cell transplantation (HSCT).MethodsIn accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we completed a systematic review using five databases to assess the relationship between peer support and clinical outcomes (e.g., distress, physical symptoms) among patients with hematologic malignancies or HSCT recipients.ResultsThe eight included studies examined peer support in a total of 574 patients. Four intervention studies highlighted the potential benefits of peer support, such as improved physical symptoms. Two studies, one interventional and one cross-sectional, highlighted the need for more empirically based peer support interventions in the HSCT population.ConclusionAmong patients with hematologic malignancies and/or HSCT recipients, there is a dearth of literature examining the association between peer support and outcomes, although few studies have described a positive association between peer support and better health outcomes. More randomized controlled studies are needed to better understand the role of peer support and peer support interventions on outcomes in these vulnerable populations.

Chronic inflammation and involvement of myeloid blood cells are associated with the development of Alzheimer’s disease (AD). Chronic inflammation is a highly important driving force for the development and progression of the chronic myeloproliferative blood cancers (MPNs), which are characterized by repeated thrombotic episodes years before MPN-diagnosis, being elicited by elevated erythrocytes, leukocytes, and platelets. Mutations in blood cells, the JAK2V617F and TET2 -mutations, contribute to the inflammatory and thrombogenic state. Herein, we discuss the MPNs as a human neuroinflammation model for AD development, taking into account the many shared cellular mechanisms for reduction in cerebral blood, including capillary stalling with plugging of blood cells in the cerebral microcirculation. The therapeutic consequences of an association between MPNs and AD are immense, including reduction in elevated cell counts by interferon-alpha2 or hydroxyurea and targeting the chronic inflammatory state by JAK1-2 inhibitors, e.g., ruxolitinib, in the future treatment of AD.