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

Maike Breidenbach, Peter Bader, Andishe Attarbaschi et al.

Abstract Anti-CD19 chimeric antigen receptor T cells (CAR) are a well-established treatment option for children and young adults suffering from relapsed/refractory B-lineage acute lymphoblastic leukemia. Bridging therapy is used to control disease prior to start of lymphodepletion before CAR infusion and thereby improve efficacy of CAR therapy. However, the effect of different bridging strategies on outcome, side effects and response to CAR therapy is still poorly understood. In this retrospective, multinational study, real-world data were collected from 14 different sites in Germany, Austria and Switzerland on 88 patients receiving 93 2nd-generation CAR therapies. Bridging therapy was classified into the categories 1) no systemic therapy (15/93 treatments), 2) low-intensity therapy (38/93 treatments) and 3) high-intensity therapy (39/93 treatments). We analyzed the impact of bridging regimens on clinical outcome. Patients receiving a high-intensity bridging therapy had a significantly higher tumor burden at time of eligibility compared to patients treated with a low-intensity or no systemic bridging therapy. They suffered significantly more from bacterial adverse events and mucositis. Overall survival was significantly better for patients who did not receive any bridging therapy in comparison to patients who had been treated with a low- or high-intensity bridging regimen. In conclusion, in this retrospective cohort, high-intensity bridging therapy has not improved the outcome in terms of overall and progression-free survival in comparison to a low-intensity therapy. Yet, high-intensity bridging therapy was associated with more adverse events. Our study suggests that a low-intensity bridging regimen may be preferred whenever tumor burden and disease kinetics allow this treatment strategy.

Kainat

This research presents a novel method for noninvasive arterial blood pressure ABP prediction using speech signals employing a BERT based regression model Arterial blood pressure is a vital indicator of cardiovascular health and accurate monitoring is essential in preventing hypertension related complications Traditional cuff based methods often yield inconsistent results due to factors like whitecoat and masked hypertension Our approach leverages the acoustic characteristics of speech capturing voice features to establish correlations with blood pressure levels Utilizing advanced deep learning techniques we analyze speech signals to extract relevant patterns enabling real time monitoring without the discomfort of conventional methods In our study we employed a dataset comprising recordings from 95 participants ensuring diverse representation The BERT model was fine tuned on extracted features from speech leading to impressive performance metrics achieving a mean absolute error MAE of 136 mmHg for systolic blood pressure SBP and 124 mmHg for diastolic blood pressure DBP with R scores of 099 and 094 respectively These results indicate the models robustness in accurately predicting blood pressure levels Furthermore the training and validation loss analysis demonstrates effective learning and minimal overfitting Our findings suggest that integrating deep learning with speech analysis presents a viable alternative for blood pressure monitoring paving the way for improved applications in telemedicine and remote health monitoring By providing a user friendly and accurate method for blood pressure assessment this research has significant implications for enhancing patient care and proactive management of cardiovascular health

Yiting Wei, Bingo Wing-Kuen Ling, Danni Chen et al.

Recently, the wearable and non-invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. To address this issue, this paper proposes a polynomial fitting approach to smooth the obtained features or the reference blood glucose values. First, the blood glucose values are estimated based on the individual optimization approaches. Second, the absolute difference values between the estimated blood glucose values and the actual blood glucose values based on each optimization approach are computed. Third, these absolute difference values for each optimization approach are sorted in the ascending order. Fourth, for each sorted blood glucose value, the optimization method corresponding to the minimum absolute difference value is selected. Fifth, the accumulate probability of each selected optimization method is computed. If the accumulate probability of any selected optimization method at a point is greater than a threshold value, then the accumulate probabilities of these three selected optimization methods at that point are reset to zero. A range of the sorted blood glucose values are defined as that with the corresponding boundaries points being the previous reset point and this reset point. Hence, after performing the above procedures for all the sorted reference blood glucose values in the validation set, the regions of the sorted reference blood glucose values and the corresponding optimization methods in these regions are determined. The computer numerical simulation results show that our proposed method yields the mean absolute relative deviation (MARD) at 0.0930 and the percentage of the test data falling in the zone A of the Clarke error grid at 94.1176%.

Léo Puyo, Jonas Franke, Lisa Kutzner et al.

We report on using a laser Doppler processing of Fourier-domain optical coherence tomography (OCT) data for the assessment of pulsatile blood flow in the choriocapillaris. Signal fluctuations in B-scans recorded at 2 kHz were analyzed by Fourier transform to extract blood flow information. The spectral broadening of light backscattered by the choriocapillaris was used to derive a choriocapillaris flow velocity index in physical units, with sufficient temporal resolution to capture heartbeat-induced variations. Furthermore, the asymmetry in the spectral broadening enabled us to determine the axial direction of blood flow with high sensitivity, allowing for the detection of flow orientation in retinal capillaries. This approach is promising as it can be directly implemented on widely available fast-scanning Fourier-domain OCT instruments.

IYS Caitano, LCA Holanda, VBSC Sampaio et al.

A agência transfusional do Hospital Materno Infantil Nossa Senhora de Nazareth é referência para atendimentos de hospitais externos (3 privados e 1 Estadual), além de atender a demanda da ginecologia e obstetrícia no Estado de Roraima. A realização da pesquisa de anticorpos irregulares (PAI) é obrigatória para fins transfusionais e sua identificação auxilia na melhor escolha do hemocomponente. Objetivo: Descrever os resultados da identificação de anticorpos irregulares realizados no ano de 2022 na agência transfusional do Hospital Materno Infantil Nossa Senhora de Nazareth, em Boa Vista - Roraima, extremo norte do Brasil. Materiais e métodos: Estudo retrospectivo e descritivo. As informações foram coletadas de registros de exames de exames de PAI positivos do ano de 2022 na agência transfusional. Verificou-se a especificidade de anticorpos, sexo, grupo sanguíneo dos pacientes e hospital de origem. As informações foram tratadas em planilhas do programa Microsoft Office Excel 2016 para análise de frequência e porcentagem. Os exames foram realizados utilizando a técnica de gel centrifugação, BioRad, conforme as instruções do fabricante e kit de painel de hemácias. Resultados: Em 2022, foram realizados 2964 testes de PAI. Destes, 43 (1,45%) amostras foram positivas. A detecção de anticorpos irregulares revelou maior prevalência de Anti-D, 06 (14%) e Anti-K, 06 (14%). Foram descritos anticorpos Anti-E, em 2 (4,7%) e Anti-Dia 2 (4,7%) . Outros anticorpos detectados incluíram Anti-C, Anti-S, Anti-Jka e Anti-Lea, cada um em 1 paciente (2,3%). Houve 22 testes inconclusivos (51,2%) e 1 teste não realizado (2,3%). A distribuição do grupo sanguíneo ABO/RhD: O+, 26 (60,5%), A Neg 6 (14%), A+ em 5 (11,6%), B+ em 4 (9,3%) e O Neg em 2 (4,7%). A distribuição por sexo mostrou uma predominância de mulheres, com 34 pacientes (79,1%), enquanto 9 pacientes eram homens (20,9%). Quanto à origem das solicitações, 22 (51,2%) eram de hospitais externos e 21 (48,8%) eram da maternidade. Discussão: A prevalência de aloanticorpos e a especificação de cada um em receptores de sangue é essencial para a proteção do receptor e para minimizar possíveis reações transfusionais, visto que os anticorpos identificados possuem importância clínica transfusional e obstétrica. Os resultados apresentados da prevalência corroboram Anti-D e Anti-K com outras análises realizadas na agência em outros períodos. Mulheres representaram 79,1% dos casos, indicando uma maior prevalência de anticorpos irregulares neste grupo. Os resultados inconclusivos requerem uma análise abrangente para minimizar estes resultados. Conclusão: A prevalência de anticorpos Anti-D e Anti-K em 2022 ressalta a importância da triagem de anticorpos irregulares para a segurança transfusional. A identificação adequada desses anticorpos são cruciais para prevenir complicações e melhorar os resultados clínicos, possibilitando uma transfusão segura em situações de urgência, devido à possibilidade de escolha de hemocomponentes com antígenos correspondentes negativos. Melhorias nos processos laboratoriais são essenciais para reduzir testes não realizados e resultados inconclusivos, garantindo maior segurança aos pacientes transfundidos.

LF Alves, MBS Nascimento, LLR Matos et al.

Introdução/objetivos: A aterosclerose é a causa mais comum de trombose arterial, no entanto, existem outras causas que propiciam a ativação da cascata de coagulação. Eventos trombóticos arteriais sem uma etiologia aparente apresentam desafios de diagnóstico e tratamento. A etiologia das tromboses arteriais inexplicadas tem sido relacionada a substâncias químicas, anormalidades vasculares e anatômicas, distúrbios sistêmicos, Síndrome Antifosfolípide (SAF) e trombofilias. Ao tentar determinar a etiologia e o tratamento subsequente, é necessário confirmar a localização vascular anatômica e entender quais fatores propiciaram o evento. Deste modo, o presente trabalho tem como objetivo evidenciar a necessidade da investigação clínica para diagnósticos diferenciais. Matériais e métodos: Foi realizada uma revisão de literatura sobre o tema na base de dados PUBMED a partir de janeiro de 2020. Foram utilizados os descritores: Pathogenesis OR Treatment OR Diagnosis AND Unexplained arterial thrombosis. A busca resultou em 314 artigos. Foram excluídos artigos que abordaram a trombose arterial por causa arteriosclerótica ou embólica, artigos de opinião, relatos ou series de casos. Foram incluídos 27 artigos que abordaram a patogênese, o diagnóstico e o tratamento da trombose arterial de causa inexplicada. Discussão: Substâncias químicas exógenas são a principal causa de trombose arterial não provocada por placas de ateroma, pois advém da hiperviscosidade, aumento da produção e ativação dos fatores coagulantes, vasoespasmo, disfunção endotelial e ativação plaquetária. Essa etiologia tem sido associada a indivíduos jovens e a trombos em locais comuns, como o encéfalo e o coração, gerando acidente vascular cerebral e infarto agudo do miocárdio, respectivamente. Anormalidades vasculares também podem precipitar a formação de trombos, frequentemente em locais pouco usuais. Normalmente ocorre por alterações na parede arterial, tais como vasculite ou displasia fibromuscular, ou no calibre, por compressão externa ou por vasoespasmo. Doenças sistêmicas que cursam com hiperviscosidade, como as neoplasias hematológicas, ou ainda aquelas que cursam com distúrbios autoimunes inflamatórios, como artrite reumatoide, lúpus eritematoso sistêmico e vasculite associada a ANCA, propiciam uma ativação da cascata de coagulação e, por consequência, formação de trombos não ateroscleróticos. Na SAF, os anticorpos antifosfolípides induzem trombose arterial em sítios pouco usuais, ao ativar receptores plaquetários, como apoER2’ e GPIbα, via complexos anti-β2GPI/β2GPI. Por fim, trombofilias, hereditárias ou adquiridas, também estão associadas à trombose arterial de causa inexplicada, podendo se manifestar em diversos locais da circulação e, inclusive, acometendo diferentes faixas etárias. Portanto, sua investigação é crucial. Conclusão: A ocorrência de trombose em indivíduos fora da faixa etária comum ou ausência de fatores comórbidos usuais que envolve a doença aterosclerótica, ou mesmo o acometimento vascular em local pouco usual, cria o alerta necessário para a hipótese de se estar diante de um caso de trombose arterial inexplicada. Portanto, é necessário, além do tratamento, uma investigação detalhada das possíveis causas, pelo elevado risco de recorrência da trombose.

Kemal Fıdan, Gülşah Akyol, Ali Ünal et al.

Case report: Introduction: Secondary leukemias that occur after chemotherapy are mostly myelodysplastic syndrome and acute myeloid leukemias.With the recent increased use of immunomodulatory (IMID) drugs (pomalidomide, thalidomide and lenalidomide); It has been shown that secondary leukemias increase. Acute lymphoblastic leukemia (ALL) has frequently been described in association with IMID.Here, we present our second ASCT experience in a case who underwent autologous stem cell transplantation (ASCT) with the diagnosis of multiple myeloma and developed B-ALL during the maintenance lenalidomide treatment.Key words: Multipl myelom, B-ALL, autologous stem cell transplantation Case report: A 62-year-old female patient was diagnosed with multiple myeloma (MM) in 2017.The patient was given 4 cycles of BED (bortezomide, cyclophosphamide, dexamethasone) treatment.The patient, who was in remission, underwent ASCT with Melphalan 200 mg/m2 preparation regimen in 2018.After ASCT, the patient was started on lenalidomide maintenance treatment. Approximately 4 years later, in 2022, during the course of lenalidomide treatment, CALLA+ B-ALL was diagnosed with a bone marrow biopsy.The patient was given hyper-CVAD Chemotherapy. The patient, who was in remission after the treatment, underwent ASCT again in 2023, for the second time with the TBI + endoxan protocol (3.8 × 106/kg cells) with peripheral blood stem cells collected during the previous MM disease period. Discussion and conclusion: ALL can develop due to cytotoxic agents and immunomodulatory (IMID) drugs such as alkylating agents and topoisomerase inhibitors. Alkylating agents such as Melphalan can cause the development of AML or MDS, often through unbalanced chromosomal abnormalities from first use. The incidence of secondary ALL developing after primary malignancy is 2.3%. Secondary malignancies are a known, albeit rare, complication of long-term lenalidomide therapy. However, the incidence of secondary ALL due to lenalidomide is very low. Parrondo et al demonstrated a significant increase in the risk of secondary malignancies following lenalidomide maintenance following high-dose melphalan and autologous hematopoietic stem cell transplantation in patients with multiple myeloma (MM). 4-17% of these malignancies are hematological malignancies. After ASCT, maintenance lenalidomide has now become the standard treatment for multiple myeloma. Lenalidomide creates a basis for the development of hematological malignancy secondary to treatment in these patients. However, considering the use of melphalan in the chemotherapy regimen before ASCT, lenalidomide alone cannot be blamed for treatment-related ALL. However, as a result of our literature review, there is a stronger association between lenalidomide maintenance therapy and treatment-associated ALL in multiple myeloma patients.Therefore, in case of suspicious hematological findings in patients receiving maintenance lenalidomide treatment, bone marrow aspiration and biopsy samples should be carefully evaluated for leukemia.

André Dias Américo, Cinthya Correa Silva, Mariana Nassif Kerbauy et al.

Yongcheng Li, Lingcong Cai, Ying Lu et al.

Accurate classification of blood cells is of vital significance in the diagnosis of hematological disorders. However, in real-world scenarios, domain shifts caused by the variability in laboratory procedures and settings, result in a rapid deterioration of the model's generalization performance. To address this issue, we propose a novel framework of domain-invariant representation learning (DoRL) via segment anything model (SAM) for blood cell classification. The DoRL comprises two main components: a LoRA-based SAM (LoRA-SAM) and a cross-domain autoencoder (CAE). The advantage of DoRL is that it can extract domain-invariant representations from various blood cell datasets in an unsupervised manner. Specifically, we first leverage the large-scale foundation model of SAM, fine-tuned with LoRA, to learn general image embeddings and segment blood cells. Additionally, we introduce CAE to learn domain-invariant representations across different-domain datasets while mitigating images' artifacts. To validate the effectiveness of domain-invariant representations, we employ five widely used machine learning classifiers to construct blood cell classification models. Experimental results on two public blood cell datasets and a private real dataset demonstrate that our proposed DoRL achieves a new state-of-the-art cross-domain performance, surpassing existing methods by a significant margin. The source code can be available at the URL (https://github.com/AnoK3111/DoRL).

Bastian Wittmann, Yannick Wattenberg, Tamaz Amiranashvili et al.

Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular patterns and scales, signal-to-noise ratios, and background tissues. These variations, along with domain gaps arising from varying imaging protocols, limit the generalization of existing supervised learning-based methods, requiring tedious voxel-level annotations for each dataset separately. While foundation models promise to alleviate this limitation, they typically fail to generalize to the task of blood vessel segmentation, posing a unique, complex problem. In this work, we present vesselFM, a foundation model designed specifically for the broad task of 3D blood vessel segmentation. Unlike previous models, vesselFM can effortlessly generalize to unseen domains. To achieve zero-shot generalization, we train vesselFM on three heterogeneous data sources: a large, curated annotated dataset, data generated by a domain randomization scheme, and data sampled from a flow matching-based generative model. Extensive evaluations show that vesselFM outperforms state-of-the-art medical image segmentation foundation models across four (pre-)clinically relevant imaging modalities in zero-, one-, and few-shot scenarios, therefore providing a universal solution for 3D blood vessel segmentation.

Stefanie Kreutmair, Maximilian Schaefer, Sebastian Stolz et al.

Imen Ben Amor, Maha Charfi, Moez Mdhaffar et al.

Orly Leiva, Andrew Jenkins, Rachel P. Rosovsky et al.

Patients with myeloproliferative neoplasms (MPNs) are at increased risk of cardiovascular disease (CVD), including acute coronary syndrome (ACS). However, data on long-term outcomes of patients with MPN who have had ACS and risk factors for all-cause death or CV events post-ACS hospitalization are lacking. We conducted a single-center study of 41 consecutive patients with MPN with ACS hospitalization after MPN diagnosis. After a median follow-up of 80 months after ACS hospitalization, 31 (76%) experienced death or a CV event (myocardial infarction, ischemic stroke, or heart failure hospitalization). After multivariable Cox proportional hazards regression, index ACS within 12 months of MPN diagnosis (HR 3.84, 95% CI 1.44–10.19), WBC ≥ 20 K/µL (HR 9.10, 95% CI 2.71–30.52), <i>JAK2</i> mutation (HR 3.71, 95% CI 1.22–11.22), and prior CVD (HR 2.60, 95% CI 1.12–6.08) were associated with increased death or CV events. Further studies are warranted to improve cardiovascular outcomes in this patient population.

Aseel Modhfer Al Dayyeni, Bassam T Al-Gailani, Mohammed Ghanim Mahdi

BACKGROUND: According to the World Health Organization (WHO), erythropoietin (EPO) is only a minor criterion for the diagnosis of polycythemia vera (PV), but its diagnostic validity is controversial. OBJECTIVES: The objective was to assess the diagnostic accuracy of EPO levels and the different combinations of the laboratory and clinical criteria, defined by the latest WHO report, as markers for the diagnosis of PV in Iraqi patients. PATIENTS, MATERIALS AND METHODS: This cross-sectional study included 158 myeloproliferative neoplasm-suspected patients (48 PV, 47 essential thrombocythemia, 25 secondary thrombocytosis, and 35 nonclonal erythrocytosis). Patients were assessed for the presence of Janus Kinase 2 (JAK2) V617F mutation. Subsequently, JAK2V617F-negative patients were evaluated for the presence of JAK2 exon 12 mutations. Plasma EPO was measured in PV and nonclonal erythrocytosis patients. RESULTS: Male was more prevalent among the nonclonal erythrocytosis patients. PV patients were older and had higher levels of all hematological variables examined in the study. Although all obtained EPO levels were normal, PV patients had significantly lower levels of EPO than nonclonal erythrocytosis. In addition, the hemoglobin and hematocrit had a better diagnostic accuracy than EPO levels in both male and female patients with PV. Furthermore, a better diagnostic accuracy was obtained when JAK2 mutation status was added to the evaluation of hemoglobin or hematocrit. CONCLUSION: The low EPO level is not a good predictive marker for PV. Hemoglobin and hematocrit had equal predictive validity in the diagnosis of PV. It is convenient to evaluate JAK2 mutation as one of the major criteria in the diagnosis of PV.

Rajeswari Subramaniyan

Introduction: Lewis antibodies have been thought to play a small role in clinical transfusion practice, but recent reports suggest that they have gained more importance in the context of transfusion and transplantation. Data regarding the prevalence of Lewis antibodies and their clinical significance in the Indian context is very limited. Hence, this study was aimed at analyzing the serological characteristics and clinical significance of Lewis antibodies encountered in our patient and donor populations. Methods: The retrospective data analyzed the records of red cell antibody screening results and the additional serological evaluation performed on the donor and patient samples included in the study. Results: A total of 26 study subjects were noted to have Lewis antibodies (including 6 healthy donors and 20 patients). Of them, 13 individuals had anti-Leb, 10 had anti-Lea and the remaining three had an anti-Lea/Leb mixture. IgG Lewis antibodies were detected in 7 individuals. All cases of IgM Lewis antibodies detected were reacting at 37°C. Two patients were suspected of having hemolytic transfusion reactions due to Lewis antibodies. Antigen-negative cross-match compatible units were provided for transfusion in the recipients. Conclusion: Lewis antibodies of the IgM class reacting at 37°C should be regarded as clinically important. The present study findings urge that the lab personnel look for the thermal amplitude of Lewis antibodies, irrespective of the fact that the antibody class and antigen-negative crossmatch compatible units should be provided to avoid hemolytic transfusion reactions.

Harry Rubin-Falcone, Joyce Lee, Jenna Wiens

In time-series forecasting, future target values may be affected by both intrinsic and extrinsic effects. When forecasting blood glucose, for example, intrinsic effects can be inferred from the history of the target signal alone (\textit{i.e.} blood glucose), but accurately modeling the impact of extrinsic effects requires auxiliary signals, like the amount of carbohydrates ingested. Standard forecasting techniques often assume that extrinsic and intrinsic effects vary at similar rates. However, when auxiliary signals are generated at a much lower frequency than the target variable (e.g., blood glucose measurements are made every 5 minutes, while meals occur once every few hours), even well-known extrinsic effects (e.g., carbohydrates increase blood glucose) may prove difficult to learn. To better utilize these \textit{sparse but informative variables} (SIVs), we introduce a novel encoder/decoder forecasting approach that accurately learns the per-timepoint effect of the SIV, by (i) isolating it from intrinsic effects and (ii) restricting its learned effect based on domain knowledge. On a simulated dataset pertaining to the task of blood glucose forecasting, when the SIV is accurately recorded our approach outperforms baseline approaches in terms of rMSE (13.07 [95% CI: 11.77,14.16] vs. 14.14 [12.69,15.27]). In the presence of a corrupted SIV, the proposed approach can still result in lower error compared to the baseline but the advantage is reduced as noise increases. By isolating their effects and incorporating domain knowledge, our approach makes it possible to better utilize SIVs in forecasting.

Sam Royston

Continuous Blood Glucose (CGM) monitors have revolutionized the ability of diabetics to manage their blood glucose, and paved the way for artificial pancreas systems. In this paper we augment CGM data with sensor input collected by a smart phone and use it to provide analytical tools for patients and clinicians. We collected GPS data, activity classifications, and blood glucose data with a custom iOS application over a 9 month period from a single free-living type-1 diabetic patient. This data set is novel in terms of it's size, the inclusion of GPS data, and the fact that it was collected non-intrusively from a free-living patient. We describe a method to measure the occurrence of lifestyle \textit{events} based on GPS and activity data, and show that they can capture instances of food consumption and are therefore correlated to changes in blood glucose. Finally, we incorporate these event representations into our system to create useful visualizations and notifications to aid patients in managing their diabetes.

Rabia Asghar, Sanjay Kumar, Paul Hynds et al.

Machine learning (ML) and deep learning (DL) models have been employed to significantly improve analyses of medical imagery, with these approaches used to enhance the accuracy of prediction and classification. Model predictions and classifications assist diagnoses of various cancers and tumors. This review presents an in-depth analysis of modern techniques applied within the domain of medical image analysis for white blood cell classification. The methodologies that use blood smear images, magnetic resonance imaging (MRI), X-rays, and similar medical imaging domains are identified and discussed, with a detailed analysis of ML/DL techniques applied to the classification of white blood cells (WBCs) representing the primary focus of the review. The data utilized in this research has been extracted from a collection of 136 primary papers that were published between the years 2006 and 2023. The most widely used techniques and best-performing white blood cell classification methods are identified. While the use of ML and DL for white blood cell classification has concurrently increased and improved in recent year, significant challenges remain - 1) Availability of appropriate datasets remain the primary challenge, and may be resolved using data augmentation techniques. 2) Medical training of researchers is recommended to improve current understanding of white blood cell structure and subsequent selection of appropriate classification models. 3) Advanced DL networks including Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN will likely be increasingly employed to supplement or replace current techniques.

Elia Apodaca-Chávez, Roberta Demichelis-Gómez, Adriana Rosas-López et al.

Background: Myelodysplastic syndrome (MDS) is associated with persistent immune activation. High mobility group box-1 (HMGB1) is a ubiquitous, functionally diverse, non-histone intranuclear protein. During acute and chronic inflammatory states, HMGB1 is actively released by inflammatory cells, further amplifying the inflammatory response. A role in MDS and other hypoplastic bone marrow (BM) disorders is incompletely understood. Objectives: The objective of the study is to evaluate whether circulating HMGB1 is elevated in patients with MDS and other BM failure syndromes [namely, aplastic anemia (AA) and paroxysmal nocturnal hemoglobinuria (PNH)]. Design: This is a observational, cross-sectional, single-center, exploratory study. Methods: We evaluated circulating concentrations of HMGB1, interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α in patients with MDS and age-matched hematologically healthy controls as well as patients with AA and PNH. Results: We included 66 patients with MDS and 65 age-matched controls as well as 44 patients with other BM failures (AA = 27, PNH = 17). Circulating levels of HMGB1 were higher in patients with MDS [median, 4.9 ng/ml; interquartile range (IQR): 2.3–8.1] than in AA (median, 2.6 ng/ml; IQR: 1.7–3.7), PNH (median, 1.7 ng/ml; IQR: 0.9–2.5), and age-matched healthy individuals (median, 1.9 ng/ml; IQR: 0.9–2.5) ( p  = 0.0001). We observed higher concentrations of HMGB1 in the very low/low-risk MDS patients than in the intermediate/high/very high-risk ones ( p  = 0.046). Finally, in comparison with patients with AA, those with hypocellular MDS (h-MDS) had significantly higher levels of circulating HMGB1 ( n  = 14; median concentration, 5.6 ng/ml, IQR: 2.8–7.3; p  = 0.006). We determined a circulating HMGB1 value of 4.095 ng/ml as a diagnostic cutoff differentiator between h-MDS and AA. Conclusion: These observations indicate that circulating HMGB1 is increased in patients with MDS. HMGB1 (but not IL-1β or TNF-α) differentiated between MDS and other BM failures, suggesting that HMGB1 may be mechanistically involved in MDS and a druggable target to decrease inflammation in MDS.

F. Locatelli, G. Zugmaier, C. Rizzari et al.