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Record W2069519707 · doi:10.1073/pnas.1221172110

TAp73 knockout mice show morphological and functional nervous system defects associated with loss of p75 neurotrophin receptor

2013· article· en· W2069519707 on OpenAlexaff
Maria Victoria Niklison-Chirou, Joern R. Steinert, Massimiliano Agostini, Richard A. Knight, David Dinsdale, Antonino Cattaneo, Tak W. Mak, Gerry Melino

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsLow-affinity nerve growth factor receptorNeurotrophinNeuriteKnockout mouseBiologyCell biologyNerve growth factorMyelinPeripheral nervous systemReceptorNeuroscienceActivator (genetics)Neurotrophic factorsCentral nervous systemGeneticsIn vitro

Abstract

fetched live from OpenAlex

Total and N-terminal isoform selective p73 knockout mice show a variety of central nervous system defects. Here we show that TAp73 is a transcriptional activator of p75 neurotrophin receptor (p75(NTR)) and that p75(NTR) mRNA and protein levels are strongly reduced in the central and peripheral nervous systems of p73 knockout mice. In parallel, primary cortical neurons from p73 knockout mice showed a reduction in neurite outgrowth and in nerve growth factor-mediated neuronal differentiation, together with reduced miniature excitatory postsynaptic current frequencies and behavioral defects. p73 null mice also have impairments in the peripheral nervous system with reduced thermal sensitivity, axon number, and myelin thickness. At least some of these morphological and functional impairments in p73 null cells can be rescued by p75(NTR) re-expression. Together, these data demonstrate that loss of p75(NTR) contributes to the neurological phenotype of p73 knockout mice.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.257
Teacher spread0.210 · 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 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

Citations52
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicNerve injury and regenerationFrench-language works237,207