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Record W1979420039 · doi:10.1073/pnas.0602116103

Slow progressive degeneration of nigral dopaminergic neurons in postnatal <i>Engrailed</i> mutant mice

2006· article· en· W1979420039 on OpenAlexaff
Paola Sgadó, Lavinia Albéri, Daniel Gherbassi, Sherri L. Galasso, Geert M. J. Ramakers, Kambiz N. Alavian, Marten P. Smidt, Richard H. Dyck, Horst H. Simon

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNuclear Receptors and Signaling
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsengrailedSubstantia nigraDopaminergicBiologyNeurosciencePutamenDopamineEndocrinologyGeneticsHomeoboxGeneTranscription factor

Abstract

fetched live from OpenAlex

The homeobox transcription factors Engrailed-1 and Engrailed-2 are required for the survival of mesencephalic dopaminergic neurons in a cell-autonomous and gene-dose-dependent manner. Because of this requirement, the cells die by apoptosis when all four alleles of the Engrailed genes are genetically ablated (En1-/-;En2-/-). In the present study, we show that viable and fertile mice, heterozygous null for Engrailed-1 and homozygous null for Engrailed-2 (En1+/-;En2-/-), have an adult phenotype that resembles key pathological features of Parkinson's disease. Specifically, postnatal mutant mice exhibit a progressive degeneration of dopaminergic neurons in the substantia nigra during the first 3 mo of their lives, leading to diminished storage and release of dopamine in the caudate putamen, motor deficits similar to akinesia and bradykinesia, and a lower body weight. This genetic model may provide access to the molecular etiology for Parkinson's disease and could assist in the development of novel treatments for this neurodegenerative disorder.

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 score0.002
Threshold uncertainty score0.007

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 integrity0.0010.001
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 head0.028
GPT teacher head0.272
Teacher spread0.244 · 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

Citations139
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicNuclear Receptors and SignalingFrench-language works237,207