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Record W2019933158 · doi:10.1161/strokeaha.106.476507

Peripheral Inflammation Exacerbates Damage After Global Ischemia Independently of Temperature and Acute Brain Inflammation

2007· article· en· W2019933158 on OpenAlexafffund
Sarah J. Spencer, Abdeslam Mouihate, Quentin J. Pittman

Bibliographic record

VenueStroke · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsInstitute of Infection and Immunity
FundersAlberta Heritage Foundation for Medical ResearchAstraZenecaCanadian Stroke NetworkHeart and Stroke Foundation of CanadaFondation pour la Recherche MédicaleCanadian Institutes of Health ResearchAstraZeneca Canada
KeywordsMedicineHippocampal formationInflammationIschemiaLipopolysaccharideCytokineBrain damageOpen fieldAnesthesiaImmunologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Concomitant infection can exacerbate damage caused by cerebral ischemia. However, the interaction between and relative importance of the febrile and inflammatory components of the immune response is still unknown. METHODS: Male Sprague-Dawley rats were subjected to a 2-vessel occlusion with hypotension, immediately followed by intraperitoneal injection of lipopolysaccharide or pyrogen-free saline. RESULTS: Inflammation immediately after 2-vessel occlusion exacerbated hippocampal cell loss at 3 days and enhanced anxiety-related behaviors in the elevated plus maze and open field. These effects were not associated with differences in body temperature changes or with hippocampal pro-inflammatory cytokine production or hippocampal microglial activation. CONCLUSIONS: We show a previously undocumented dissociation between lipopolysaccharide-exacerbated damage after global ischemia in the rat and the temperature and acute brain immune response, indicating that the mechanism for enhanced lipopolysaccharide damage is hippocampal cytokine and temperature independent in this case.

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 score0.007
Threshold uncertainty score0.850

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 communication0.0000.001
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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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

Citations61
Published2007
Admission routes2
Has abstractyes

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