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Record W4394919587 · doi:10.3324/haematol.2024.285257

‘One way, or another, I’m gonna find ya’: miR-221-3p finds its targets via small extracellular vesicles

2024· editorial· en· W4394919587 on OpenAlexaff
Jonathan Tak-Sum Chow, Leonardo Salmena

Bibliographic record

VenueHaematologica · 2024
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExtracellular vesiclesVesicleExtracellularChemistryMolecular biologyCell biologyBiophysicsBiologyBiochemistryMembrane

Abstract

fetched live from OpenAlex

In this issue of Haematologica, Li et al. report yet another mechanism by which the ubiquitously oncogenic miR-221-3p locates its targets, through small extracellular vesicle (sEV)-mediated autocrine and paracrine actions to promote leukemogenesis. 1 Extracellular vesicles (EV) are lipid-bilayer enclosed structures released by cells into the extracellular (EC) space.EV are classified into three different subclasses based on size: sEV, previously known as exosomes, are the smallest, ranging from 30-150 nm in diameter; apoptotic bodies that are 50-5,000 nm in diameter; and microvesicles that range from 100-1,000 nm in diameter. 2EV payloads consist of various cellular components including proteins, lipids and DNA, RNA and microRNA (miRNA) that can participate in intercellular signal transduction.Recent studies have revealed that the unique payload composition of sEV can contribute to the progression of acute myeloid leukemia (AML) by promoting intercellular signaling among cells within the bone marrow (BM) niche.In particular, miRNA transferred from AML cells to non-malignant cells of the BM niche have been demonstrated to promote leukemogenesis through paracrine suppression of normal hematopoiesis by inhibiting hematopoietic stem and progenitor cells (HSPC), a consequence which contributes to a favorable leukemogenic BM niche. 2 Examples include exosomal mir-150 and mir-155, both found to suppress HSPC primarily through inhibition of c-myb. 3Similarly, mir-548ac was found to be transported in AML-sEV and could suppress normal hematopoiesis by targeting TRIM28 leading to subsequent STAT3 activation. 4 Additionally, sEV-miRNA can function in an autocrine manner by promoting oncogenic properties of neighboring AML cells. 2 To complicate matters, sEV can also be derived from bone marrow stromal cells (BMSC).For instance, in acute lymphoblastic leukemia (ALL), mir-181a was found to be enriched in exosomes derived from both pediatric patient samples and cell lines. 5Exposure of exosomes containing elevated mir-181a promoted cell proliferation by upreg-

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.284
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0040.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.261
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2024
Admission routes1
Has abstractyes

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