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

Aki Sato, Nozomi Yusa, Hiroyuki Takamori et al.

Not available.

Leo Reap, Ritwick S. Mynam, Radhika Takiar et al.

Background: Hemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening hyperinflammatory syndrome characterized by uncontrolled macrophage activation. Secondary HLH is more common in adults and may be triggered by infection, malignancy, or autoimmune disease. Dyslipidemia, particularly hypolipoproteinemia, has been described but remains underexplored. Methods: We retrospectively reviewed 18 adult HLH cases diagnosed between 2012 and 2020 at two institutions where complete lipid profiles were obtained at or near diagnosis. HLH was defined according to HLH-2004 criteria. Results: Among 18 patients, 17 (94%) had secondary HLH, most commonly idiopathic (<i>n</i> = 5, 28%) or Epstein–Barr virus-associated (<i>n</i> = 3, 17%). Hypolipidemia was nearly universal: all (18/18) had HDL-C < 30 mg/dL, 15/18 (83%) had HDL-C < 20 mg/dL, and 12/18 (67%) had HDL-C < 10 mg/dL. LDL-C was <100 mg/dL in 12/18 (67%), with 6/18 (33%) undetectable. Triglycerides were variably elevated (median 279 mg/dL, range 96–1658 mg/dL). Three representative cases with profound hypolipoproteinemia demonstrated lipid normalization after HLH-directed therapy. Conclusions: Severe reductions in HDL-C and LDL-C appear to accompany HLH and may contribute to its pathophysiology by impairing antioxidant defenses, destabilizing membranes, and potentiating macrophage activation. This case series highlights a consistent association between hypolipoproteinemia and HLH, suggesting potential diagnostic value. However, the observational design and small cohort limit generalizability. Larger prospective studies are needed to clarify mechanisms and evaluate whether full lipid profiling should be incorporated into diagnostic algorithms.

Kazuho Morichika, Keita Tamaki, Takuya Fukushima et al.

Abstract Introduction We previously reported that sodium‐glucose co‐transporter 2 (SGLT‐2) was ectopically overexpressed in adult T‐cell leukemia (ATL) cells notably in aggressive type but in indolent type, and widely‐used anti‐diabetic SGLT‐2 inhibitors (SGLT‐2i) considerably attenuated proliferation of leukemic cells. Methods We performed retrospective analyses for 10 years to see whether SGLT‐2i would prevent aggressive transformation in patients with indolent type ATL accompanied by diabetes. Nucleosome occupancy in the promotor region of the SGLT‐2 gene was also assessed to explore the possible involvement of epigenetic modification in such an ectopic overexpression. Results In patients of indolent ATL with diabetes, the cumulative progression rate in the non‐SGLT‐2i‐treated group was 71%, while no patients developed aggressive transformation in the SGLT‐2i treated group. ATL cells showed an apparent trend to decrease nucleosome occupancy in the promotor region of the SGLT‐2 gene. Conclusion Our data suggest that SGLT‐2i is advantageous for preventing aggravative transformation in indolent ATL. Trial Registration Authors confirmed that clinical trial registration was not requested for the present study and this manuscript.

TAO Cuihua, HU Yingsen, LIAO Xin et al.

[Objective] To improve the efficiency and standardization of preoperative anemia diagnosis and treatment by establishing a systematic intelligent management platform for preoperative anemia. [Methods] A multidisciplinary collaborative model was adopted to develop a preoperative anemia management system that integrates intelligent early warning, standardized treatment pathways, and quality control. The system utilizes natural language processing technology to automatically capture laboratory data and establish evidence-based medical decision support functions. A pre-post study design was employed to compare changes in preoperative anemia screening rates, preoperative anemia intervention rates, reasonable use of iron supplements, and perioperative red blood cell transfusion rates before and after system implementation. [Results] After system implementation, the standardization of anemia diagnosis and treatment significantly improved: 1) Screening effectiveness: The anemia screening rate increased to 50.00% (an increase of 27.24%); 2) Intervention effectiveness: The anemia treatment rate rose to 56.30% (an increase of 14.02%); 3) Treatment standardization: The reasonable use rate of iron supplements increased to 55.33% (an increase of 21.02%); the red blood cell transfusion rate decreased to 18.29% (a decrease of 4.07%), and the amount of red blood cell transfusions was reduced by 291 units. [Conclusion] This system achieves full-process management of preoperative anemia through information technology, significantly enhancing the standardization of diagnosis and treatment as well as intervention effectiveness, providing an effective solution for perioperative anemia management.

C. Shen, M. Zhang, H. Keramati et al.

Despite tremendous advances in cardiovascular medicine, significant opportunities remain to improve coronary artery disease (CAD) prevention and treatment strategies. The key limitation lies in the understanding of disease formation and progression mechanisms. The coronary anatomy plays an important role in local haemodynamics, governing endothelial health and, thus, pathophysiological responses. The significant variation of the coronary anatomy among patients, with significant trends across different populations, increases the complexity of understanding the details of disease progression. This review covers different aspects of the current status and understanding of the blood flow investigation in coronary arteries. We summarised the current knowledge of the haemodynamic effect of coronary anatomy and its evaluation and analysis methods. We discussed recent progress across medical imaging techniques and computational haemodynamic analysis. Based on the reviewed papers, we identified the persisting knowledge gaps and challenges in the field. We then elaborated on future directions and opportunities to increase understanding of the fundamental mechanism of CAD in individuals representative of large populations and how this may translate to the patient's bedside.

Manuela Daniela Danu, George Marica, Constantin Suciu et al.

The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While contextualized language models have demonstrated impressive performance improvements for named entity recognition (NER) systems in English corpora, there remains a scarcity of research focused on clinical texts in low-resource languages. To bridge this gap, our study aims to develop multiple deep contextual embedding models to enhance clinical NER in the cardiology domain, as part of the BioASQ MultiCardioNER shared task. We explore the effectiveness of different monolingual and multilingual BERT-based models, trained on general domain text, for extracting disease and medication mentions from clinical case reports written in English, Spanish, and Italian. We achieved an F1-score of 77.88% on Spanish Diseases Recognition (SDR), 92.09% on Spanish Medications Recognition (SMR), 91.74% on English Medications Recognition (EMR), and 88.9% on Italian Medications Recognition (IMR). These results outperform the mean and median F1 scores in the test leaderboard across all subtasks, with the mean/median values being: 69.61%/75.66% for SDR, 81.22%/90.18% for SMR, 89.2%/88.96% for EMR, and 82.8%/87.76% for IMR.

Michael Deutges, Chen Yang, Raheleh Salehi et al.

The detection and segmentation of white blood cells in blood smear images is a key step in medical diagnostics, supporting various downstream tasks such as automated blood cell counting, morphological analysis, cell classification, and disease diagnosis and monitoring. Training robust and accurate models requires large amounts of labeled data, which is both time-consuming and expensive to acquire. In this work, we propose a novel approach for weakly supervised segmentation using neural cellular automata (NCA-WSS). By leveraging the feature maps generated by NCA during classification, we can extract segmentation masks without the need for retraining with segmentation labels. We evaluate our method on three white blood cell microscopy datasets and demonstrate that NCA-WSS significantly outperforms existing weakly supervised approaches. Our work illustrates the potential of NCA for both classification and segmentation in a weakly supervised framework, providing a scalable and efficient solution for medical image analysis.

Fatima Zulfiqar Ali, Atrooba Ilyas

Blood donation is a critical component of healthcare, yet locating suitable donors in emergencies often presents significant challenges. This paper presents the design and development of a Blood Donation Web Platform, a web-based system that connects patients, donors, and administrators within a centralized digital space. The platform allows interested donors to register their personal information, including blood group, contact details, and availability. Patients can search for donors based on blood group and location, and the system provides a list of nearby donors who are ready to donate. The platform design was guided by use case, database, class, and sequence diagrams to ensure a well-structured and efficient system architecture. Modern web technologies, including PHP (Laravel framework), HTML, CSS, Bootstrap, and MySQL, supported by XAMPP and Visual Studio Code, were employed to implement a dynamic, interactive, and user-friendly platform. By streamlining donor refgistration, blood requests, and communication, the proposed system reduces delays and complexities in emergencies, improving timely accessibility of blood and enhancing overall efficiency in blood donation services.

Malik A. Altayar, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon et al.

Identification of a person is central in forensic science, security, and healthcare. Methods such as iris scanning and genomic profiling are more accurate but expensive, time-consuming, and more difficult to implement. This study focuses on the relationship between the fingerprint patterns and the ABO blood group as a biometric identification tool. A total of 200 subjects were included in the study, and fingerprint types (loops, whorls, and arches) and blood groups were compared. Associations were evaluated with statistical tests, including chi-square and Pearson correlation. The study found that the loops were the most common fingerprint pattern and the O+ blood group was the most prevalent. Even though there was some associative pattern, there was no statistically significant difference in the fingerprint patterns of different blood groups. Overall, the results indicate that blood group data do not significantly improve personal identification when used in conjunction with fingerprinting. Although the study shows weak correlation, it may emphasize the efforts of multi-modal based biometric systems in enhancing the current biometric systems. Future studies may focus on larger and more diverse samples, and possibly machine learning and additional biometrics to improve identification methods. This study addresses an element of the ever-changing nature of the fields of forensic science and biometric identification, highlighting the importance of resilient analytical methods for personal identification.

Gordon J. Ruan, Xiaosheng Wu, Kimberly A. Gwin et al.

The open reading frame 8 (ORF8) protein, encoded by the SARS-CoV-2 virus after infection, stimulates monocytes/macrophages to produce pro-inflammatory cytokines. We hypothesized that a positive ex vivo monocyte response to ORF8 protein pre-COVID-19 would be associated with subsequent severe COVID-19. We tested ORF8 ex vivo on peripheral blood mononuclear cells (PBMCs) from 26 anonymous healthy blood donors and measured intracellular cytokine/chemokine levels in monocytes by flow cytometry. The % monocytes staining positive in the sample and change in mean fluorescence intensity (ΔMFI) after ORF8 were used to calculate the adjusted MFI for each cytokine. We then tested pre-COVID-19 PBMC samples from 60 CLL patients who subsequently developed COVID-19 infection. Severe COVID-19 was defined as hospitalization due to COVID-19. In the 26 normal donor samples, the adjusted MFI for interleukin (IL)-1β, IL-6, IL-8, and CCL-2 were significantly different with ORF8 stimulation vs controls. We next analyzed monocytes from pre-COVID-19 PBMC samples from 60 CLL patients. The adjusted MFI to ORF8 stimulation of monocyte intracellular IL-1β was associated with severe COVID-19 and a reactive ORF8 monocyte response was defined as an IL- 1β adjusted MFI ≥ 0.18 (sensitivity 67%, specificity 75%). The median time to hospitalization after infection in CLL patients with a reactive ORF8 response was 12 days versus not reached for patients with a non-reactive ORF8 response with a hazard ratio of 7.7 (95% CI: 2.4-132, p=0.005). These results provide new insight on the monocyte inflammatory response to virus with implications in a broad range of disorders involving monocytes.

Jeremiah Fadugba, Patrick Köhler, Lisa Koch et al.

Retinal blood vessel segmentation can extract clinically relevant information from fundus images. As manual tracing is cumbersome, algorithms based on Convolution Neural Networks have been developed. Such studies have used small publicly available datasets for training and measuring performance, running the risk of overfitting. Here, we provide a rigorous benchmark for various architectural and training choices commonly used in the literature on the largest dataset published to date. We train and evaluate five published models on the publicly available FIVES fundus image dataset, which exceeds previous ones in size and quality and which contains also images from common ophthalmological conditions (diabetic retinopathy, age-related macular degeneration, glaucoma). We compare the performance of different model architectures across different loss functions, levels of image qualitiy and ophthalmological conditions and assess their ability to perform well in the face of disease-induced domain shifts. Given sufficient training data, basic architectures such as U-Net perform just as well as more advanced ones, and transfer across disease-induced domain shifts typically works well for most architectures. However, we find that image quality is a key factor determining segmentation outcomes. When optimizing for segmentation performance, investing into a well curated dataset to train a standard architecture yields better results than tuning a sophisticated architecture on a smaller dataset or one with lower image quality. We distilled the utility of architectural advances in terms of their clinical relevance therefore providing practical guidance for model choices depending on the circumstances of the clinical setting

Hiroyuki Sato, Keisuke Suzuki, Atsushi Hashizume et al.

Progressive cognitive decline spanning across decades is characteristic of Alzheimer's disease (AD). Various predictive models have been designed to realize its early onset and study the long-term trajectories of cognitive test scores across populations of interest. Research efforts have been geared towards superimposing patients' cognitive test scores with the long-term trajectory denoting gradual cognitive decline, while considering the heterogeneity of AD. Multiple trajectories representing cognitive assessment for the long-term have been developed based on various parameters, highlighting the importance of classifying several groups based on disease progression patterns. In this study, a novel method capable of self-organized prediction, classification, and the overlay of long-term cognitive trajectories based on short-term individual data was developed, based on statistical and differential equation modeling. We validated the predictive accuracy of the proposed method for the long-term trajectory of cognitive test score results on two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and the Japanese ADNI study. We also presented two practical illustrations of the simultaneous evaluation of risk factor associated with both the onset and the longitudinal progression of AD, and an innovative randomized controlled trial design for AD that standardizes the heterogeneity of patients enrolled in a clinical trial. These resources would improve the power of statistical hypothesis testing and help evaluate the therapeutic effect. The application of predicting the trajectory of longitudinal disease progression goes beyond AD, and is especially relevant for progressive and neurodegenerative disorders.

Mahesh S. Nagargoje, Eneko Lazpita, Jesús Garicano-Mena et al.

The heart is the central part of the cardiovascular network. Its role is to pump blood to various body organs. Many cardiovascular diseases occur due to an abnormal functioning of the heart. A diseased heart leads to severe complications and in some cases death of an individual. The medical community believes that early diagnosis and treatment of heart diseases can be controlled by referring to numerical simulations of image-based heart models. Computational Fluid Dynamics (CFD) is a commonly used tool for patient-specific simulations in the cardiac flows, and it can be equipped to allow a better understanding of flow patterns. In this paper, we review the progress of CFD tools to understand the flow patterns in healthy and dilated cardiomyopathic (DCM) left ventricles (LV). The formation of an asymmetric vortex in a healthy LV shows an efficient way of blood transport. The vortex pattern changes before any change in the geometry of LV is noticeable. This flow change can be used as a marker of DCM progression. We can conclude that understanding vortex dynamics in LV using various vortex indexes coupled with data-driven approaches can be used as an early diagnosis tool and improvement in DCM treatment.

Ciro Rinaldi, Kristian Boasman, Matthew Simmonds

Guohui Li, Conghui Zhu, Dan Qiao et al.

Objective: Circ_0001946 has been identified as an oncogenic factor, and the aim of this study was to explore the detailed roles and putative targets of circ_0001946 in acute myeloid leukemia (AML). Materials and Methods: Levels of circ_0001946 were examined in AML tissues and cells. Furthermore, the regulatory functions of circ_0001946 in AML were explored. The expression of circ_0001946 was evaluated in AML samples and a matched para-carcinoma control, as well as in AML cell lines and a human bone marrow stromal cell line using reverse transcription-quantitative polymerase chain reaction. Cell proliferation was examined using a CCK-8 kit, and migration/ invasion was measured by transwell assay. Furthermore, interactions between associated molecules were assessed using RNA pulldown, and the mRNA stability of the relevant gene was examined by mRNA stability assay. Results: Our data indicated that circ_0001946 was upregulated in AML specimens/cells. Additionally, overexpression of circ_0001946 promoted the proliferation, migration, and invasion of AML cells and, vice versa, these biological processes were suppressed by knockdown of circ_0001946. Furthermore, PDL1 is a potential downstream molecule of circ_0001946 in AML and its stability was improved by circ_0001946. The expression of PDL1 was increased in AML specimens and positively correlated with circ_0001946 expression. Moreover, biological behavioral alterations in AML cells induced by oe-circ_0001946 were abrogated by sh-PDL1 and the effects of sh-circ_0001946 were enhanced by treatment with sh-PDL1. Conclusion: Taken together, these data suggest that levels of circ_0001946 are elevated in AML and that circ_0001946 could promote the growth of AML cells. Furthermore, PDL1 is a novel downstream molecule of circ_0001946 in AML. Circ_0001946/PDL1 signaling may play crucial roles in tumor progression in AML and could be a novel candidate for targeted treatments for AML patients.

Mithunan Ravindran, Jennifer Teichman, Lee Mozessohn et al.