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Record W2142976942 · doi:10.1091/mbc.e13-05-0228

Wounded cells drive rapid epidermal repair in the early<i>Drosophila</i>embryo

2013· article· en· W2142976942 on OpenAlexaff
Rodrigo Fernández‐González, Jennifer A. Zallen

Bibliographic record

VenueMolecular Biology of the Cell · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersTechnische Universiteit Delft
KeywordsBiologyCell biologyWound healingActinEmbryoMyosinWound closureAnatomyGenetics

Abstract

fetched live from OpenAlex

Epithelial tissues are protective barriers that display a remarkable ability to repair wounds. Wound repair is often associated with an accumulation of actin and nonmuscle myosin II around the wound, forming a purse string. The role of actomyosin networks in generating mechanical force during wound repair is not well understood. Here we investigate the mechanisms of force generation during wound repair in the epidermis of early and late Drosophila embryos. We find that wound closure is faster in early embryos, where, in addition to a purse string around the wound, actomyosin networks at the medial cortex of the wounded cells contribute to rapid wound repair. Laser ablation demonstrates that both medial and purse-string actomyosin networks generate contractile force. Quantitative analysis of protein localization dynamics during wound closure indicates that the rapid contraction of medial actomyosin structures during wound repair in early embryos involves disassembly of the actomyosin network. By contrast, actomyosin purse strings in late embryos contract more slowly in a mechanism that involves network condensation. We propose that the combined action of two force-generating structures--a medial actomyosin network and an actomyosin purse string--contributes to the increased efficiency of wound repair in the early embryo.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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 designBench or experimental
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

Citations76
Published2013
Admission routes1
Has abstractyes

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