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Promoters and Antagonists of Phagocytosis: A Plastic and Tunable Response

2021· article· en· W3176125357 on OpenAlexafffund
Spencer A. Freeman, Sergio Grinstein

Bibliographic record

VenueAnnual Review of Cell and Developmental Biology · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsBiologyPhagocytosisCell biologyReceptorEpigeneticsPhagocyteImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Recent observations indicate that, rather than being an all-or-none response, phagocytosis is finely tuned by a host of developmental and environmental factors. The expression of key phagocytic determinants is regulated via transcriptional and epigenetic means that confer memory on the process. Membrane traffic, the cytoskeleton, and inside-out signaling control the activation of phagocytic receptors and their ability to access their targets. An exquisite extra layer of complexity is introduced by the coexistence of distinct "eat-me" and "don't-eat-me" signals on targets and of corresponding "eat" and "don't-eat" receptors on the phagocyte surface. Moreover, assorted physical barriers constitute "don't-come-close-to-me" hurdles that obstruct the engagement of ligands by receptors. The expression, mobility, and accessibility of all these determinants can be modulated, conferring extreme plasticity on phagocytosis and providing attractive targets for therapeutic intervention in cancer, atherosclerosis, and dementia.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.242
Teacher spread0.235 · 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
GenreReview

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

Citations34
Published2021
Admission routes2
Has abstractyes

Explore more

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