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Record W4412422705 · doi:10.1097/moh.0000000000000889

Megakaryocytes as mitochondria factories: potential donors for mitochondria transplantation

2025· article· en· W4412422705 on OpenAlexaff
Émilie Mercure, Martin Pelletier, Éric Boilard

Bibliographic record

VenueCurrent Opinion in Hematology · 2025
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMitochondrionMegakaryocyteTransplantationBiologyMitochondrial biogenesisCell biologyBiogenesisHaematopoiesisStem cellImmunologyCancer researchMedicineBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: There is an increasing recognition that mitochondria are dynamic regulators of cell fate. Mitochondria transplantation has emerged as a promising therapeutic strategy for conditions ranging from metabolic disorders to neurodegenerative diseases. Thus, there is a growing need for scalable mitochondrial sources for transplantation. We highlight megakaryocytes, best known for their role in platelet production, as a novel and versatile candidate source for mitochondria transplantation. RECENT FINDINGS: Megakaryocytes are naturally equipped to package and deliver functional mitochondria when producing platelets. Furthermore, MKs can share their mitochondria with neighboring cells in the bone marrow. Given the abundance of mitochondria in megakaryocytes, they may represent an ideal source of mitochondria for transplantation. A better understanding of the role of mitochondria in megakaryocyte heterogeneity and metabolic functions may help harness megakaryocytes for therapeutic transplantation applications. SUMMARY: Megakaryocyte-derived mitochondria transplantation offers a promising avenue for treating metabolic disorders, leveraging existing mechanisms. Future research should address limitations in megakaryocyte biogenesis and heterogeneity, and optimize delivery systems to maximize therapeutic efficacy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.767

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)0.0000.000

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 head0.026
GPT teacher head0.360
Teacher spread0.334 · 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.

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

Citations3
Published2025
Admission routes1
Has abstractyes

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