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Record W4296982322 · doi:10.1002/dvdy.540

Retinal neuroblast migration and ganglion cell layer organization require the cytoskeletal‐interacting protein Mllt11

2022· article· en· W4296982322 on OpenAlexafffund
Marley Blommers, Danielle Stanton‐Turcotte, Angelo Iulianella

Bibliographic record

VenueDevelopmental Dynamics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsBiologyRetinaCell biologyInner nuclear layerNeuroblastGanglion cell layerInner plexiform layerRetinalCell migrationRetinal ganglion cellGiant retinal ganglion cellsNeuroscienceCellGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: The vertebrate retina is an organized laminar structure comprised of distinct cell types populating three nuclear layers. During development, each retinal cell type follows a stereotypical temporal order of genesis, differentiation, and migration, giving rise to its stratified organization. Once born, the precise positioning of cells along the apico-basal (radial) axis of the retina is critical for subsequent connections to form, relying on highly orchestrated migratory processes. While these processes are critical for visual function to arise, the regulators of cellular migration and retinal lamination remain largely unexplored. RESULTS: We report a role for a microtubule-interacting protein, Mllt11 (myeloid/lymphoid or mixed-lineage leukemia; translocated to chromosome 11/All1 fused gene from chromosome 1q) in mammalian retinal cell migration during retinogenesis. We show that Mllt11 loss-of-function in mouse retinal neuroblasts affected the migration of ganglion and amacrine cells into the ganglion cell layer and led to their aberrant accumulation in the inner nuclear and plexiform layers. CONCLUSIONS: We demonstrate a role for Mllt11 in neuroblast migration and formation of the ganglion cell layer of the retina.

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 score0.152
Threshold uncertainty score0.544

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 science0.0000.000
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 head0.005
GPT teacher head0.195
Teacher spread0.191 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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