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Record W2807339320 · doi:10.3324/haematol.2017.185165

Impaired mitochondrial activity explains platelet dysfunction in thrombocytopenic cancer patients undergoing chemotherapy

2018· article· en· W2807339320 on OpenAlexaff
Constance C. F. M. J. Baaten, Floor CJI Moenen, Yvonne Henskens, Frauke Swieringa, Rick J. H. Wetzels, René van Oerle, Harry F.G. Heijnen, Hugo Ten Cate, Graham P. Holloway, Erik A.M. Beckers, Johan W. M. Heemskerk, Paola E. J. van der Meijden

Bibliographic record

VenueHaematologica · 2018
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsUniversity of Guelph
FundersEuropean Hematology Association
KeywordsPlateletMedicineChemotherapyMultiple myelomaInternal medicineMalignancyCancerPhosphatidylserinePlatelet activationGastroenterologyImmunologyBiology

Abstract

fetched live from OpenAlex

Severe thrombocytopenia (≤50×109 platelets/L) due to hematological malignancy and intensive chemotherapy is associated with an increased risk of clinically significant bleeding. Since the bleeding risk is not linked to the platelet count only, other hemostatic factors must be involved. We studied platelet function in 77 patients with acute leukemia, multiple myeloma or malignant lymphoma, who experienced chemotherapy-induced thrombocytopenia. Platelets from all patients - independent of disease or treatment type - were to a variable extent compromised in Ca2+ flux, integrin a β activation and P-selectin expression when stimulated with a panelIIbof3 agonists. The patients’ platelets were also impaired in spreading on fibrinogen. Whereas the Ca2+ store content was unaffected, the patients’ platelets showed ongoing phosphatidylserine exposure, which was not due to apoptotic caspase activity. Interestingly, mitochondrial function was markedly reduced in platelets from a representative subset of patients, as evidenced by a low mitochondrial membrane potential (P

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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 teacher head, not a consensus.

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

Citations53
Published2018
Admission routes1
Has abstractyes

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