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Localization of the Heat‐Shock Protein Hsp70 to the Synapse Following Hyperthermic Stress in the Brain

2000· article· en· W2008980964 on OpenAlexaff
David A. Bechtold, Sheila J. Rush, Ian R. Brown

Bibliographic record

VenueJournal of Neurochemistry · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of TorontoThe Scarborough HospitalToronto Zoo
Fundersnot available
KeywordsSynapsePostsynaptic potentialHsp70Heat shock proteinNeuroscienceCell biologyNeuroprotectionShock (circulatory)BiologyPostsynaptic densityHeat shockHyperthermiaInhibitory postsynaptic potentialExcitatory postsynaptic potentialMedicineBiochemistryReceptorInternal medicine

Abstract

fetched live from OpenAlex

Heat-shock proteins are induced in response to cellular stress. Although heat-shock proteins are known to function in repair and protective mechanisms, their relationship to critical neural processes, such as synaptic function, has received little attention. Here we investigate whether the major heat-shock protein Hsp70 localizes to the synapse following a physiologically relevant increase in temperature in the mammalian nervous system. Our results indicate that hyperthermia-induced Hsp70 is associated with pre- and postsynaptic elements, including the postsynaptic density. The positioning of Hsp70 at the synapse could facilitate the repair of stress-induced damage to synaptic proteins and also contribute to neuroprotective events at the synapse.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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

Citations99
Published2000
Admission routes1
Has abstractyes

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