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Megakaryocyte Apoptosis in the Bone Marrow of Patients with Immune Thrombocytopenia

2016· article· en· W2625742390 on OpenAlexaff
John R. Vrbensky, Ishac Nazy, Lisa J. Toltl, Catherine Ross, John G. Kelton, Donald M. Arnold

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsCanadian Blood ServicesMcMaster University
Fundersnot available
KeywordsMegakaryocyteBone marrowMedicineThrombopoiesisImmunologyTerminal deoxynucleotidyl transferaseThrombopoietin receptorBone marrow failurePathologyHaematopoiesisPancytopeniaThrombopoietinBiologyTUNEL assayImmunohistochemistryStem cell

Abstract

fetched live from OpenAlex

Abstract Introduction: Immune thrombocytopenia (ITP) is an autoimmune bleeding disorder in which autoantibodies promote the destruction and underproduction of platelets. Recent evidence suggests that immune-mediated destruction of bone marrow megakaryocytes is associated with the pathogenesis of this disease. In addition, the attenuation of megakaryocyte apoptosis can lead to platelet underproduction since this process appears to be involved in thrombopoiesis. In the current study, we investigated megakaryocyte apoptosis as a possible mechanism in the pathogenesis of ITP. Patients/Methods: Bone marrow biopsy sections from ITP patients and controls were stained with anti-human CD61 to enumerate megakaryocytes. Terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling (TUNEL) staining was performed as a measure of megakaryocyte apoptosis. The samples were coded and reviewed by an experienced hematopathologist who was blinded to the diagnosis. Adult primary ITP patients had isolated thrombocytopenia with no underlying cause and a platelet count less than 100 x109/L at the time of bone marrow biopsy procurement. Patients with secondary ITP (in the context of medications, lymphoproliferative disease, HIV, hepatitis B or C infection) were excluded, as were patients who received prior treatment with thrombopoietin receptor agonists or other hematopoietic growth factors. Thrombocytopenic controls were patients with myelodysplastic syndrome (MDS) who had platelet counts below 100 x109/L. Control patients with normal platelet counts had bone marrow biopsies performed as part of investigations for lymphoma or plasma cell dyscrasia with negative test results. Results: The average platelet count for ITP patients, MDS patients, and controls were 18 x109/L (range 2-76 x109/L), 29 x109/L (range 9-60 x109/L), and 281 x109/L (range 171-400 x109/L), respectively. Elevated megakaryocyte counts were observed in the bone marrow sections of 5/14 (36%) ITP patients, 0/8 (0%) MDS patients, and 2/11 (18%) controls. Megakaryocyte apoptosis was comparable between ITP patients and MDS patients [2/14 (14%) vs 1/8 (13%) (p=1.00)], while fewer ITP patients exhibited megakaryocyte apoptosis compared to controls with normal platelet counts [2/14 (14%) vs 7/11 (64%) (p=0.02)]. In the entire study cohort, the average platelet counts of patients with negative and positive TUNEL staining were 66 x109/L (range 2-378 x109/L) and 206 x109/L (range 20-400 x109/L), respectively (p=0.01). In addition, the normalized megakaryocyte counts (per high powered field) were 8.2 ± 5.5 and 5.4 ± 2.5 in patients with negative and positive TUNEL staining, respectively (p=0.05). Conclusion: Megakaryocyte apoptosis was reduced in ITP bone marrow samples compared to controls with normal platelet counts, but was also low in thrombocytopenic MDS patients. Reduced megakaryocyte apoptosis was found to be associated with a low platelet count, and may be related to thrombocytopenia regardless of etiology. Our study is consistent with the hypothesis that attenuated megakaryocyte apoptosis is relevant in the context of platelet underproduction in ITP. Disclosures Arnold: Novartis: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy; UCB: Consultancy; Amgen: Consultancy, Research Funding.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.215
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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