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Record W4254953115 · doi:10.3410/f.735422952.793580850

Faculty Opinions recommendation of PTENα promotes neutrophil chemotaxis through regulation of cell deformability.

2020· dataset· en· W4254953115 on OpenAlexaff
Leonardo Salmena

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2020
Typedataset
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPTENChemotaxisPseudopodiaCell biologyMoesinBiologyImmune systemInflammationImmunologyCellSignal transductionEzrinActinCytoskeletonReceptorGeneticsPI3K/AKT/mTOR pathway

Abstract

fetched live from OpenAlex

Neutrophils are a major component of immune defense and are recruited through neutrophil chemotaxis in response to invading pathogens. However, the molecular mechanism that controls neutrophil chemotaxis remains unclear. Here, we report that PTENα, the first isoform identified in the PTEN family, regulates neutrophil deformability and promotes chemotaxis of neutrophils. A high level of PTENα is detected in neutrophils and lymphoreticular tissues. Homozygous deletion of PTENα impairs chemoattractant-induced migration of neutrophils. We show that PTENα physically interacts with cell membrane cross-linker moesin through its FERM domain and dephosphorylates moesin at Thr558, which disrupts the association of filamentous actin with the plasma membrane and subsequently induces morphologic changes in neutrophil pseudopodia. These results demonstrate that PTENα acts as a phosphatase of moesin and modulates neutrophil-mediated host immune defense. We propose that PTENα signaling is a potential target for the treatment of infections and immune diseases.© 2019 by The American Society of Hematology. PMID: 30926592

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 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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.071
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0710.048

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 head0.036
GPT teacher head0.341
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueFaculty Opinions – Post-Publication Peer Review of the Biomedical LiteratureSame topicMyeloproliferative Neoplasms: Diagnosis and TreatmentFrench-language works237,207