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Additional file 3 of Post-stroke butyrate treatment shows sex-dependent microglial responses but does not improve outcomes in a mouse model of endothelin-1 sensory motor stroke

2025· other· en· W6977137680 on OpenAlexaff

Bibliographic record

VenueFigshare · 2025
Typeother
Languageen
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsConcordia University of EdmontonUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsButyrateChronic strokeAnalysis of varianceStroke (engine)Acute strokeSensory systemSecond line treatment

Abstract

fetched live from OpenAlex

Additional file 3. Supplementary Figure 1: Butyrate treatment eliminates sex differences in microglial morphology during chronic stroke. The number of primary branches was counted in acute (D4) (A) and chronic (D20) (B) stroke. The maximum number of intersections was measured in acute (C) and chronic (D) stroke. The radius at which the maximum number of intersections occurs was determined in acute (E) and chronic (F) stroke. All measurements were performed on both male and female animals, in addition to both butyrate-treated (blue and purple) and vehicle-treated (orange and green) animals. Each dot represents a cell. Each colour represents an animal. The horizontal dashed line represents the mean of the data. A 2-way ANOVA with uncorrected Fisher’s LSD was performed using the average cell morphology metrics per animal; p values (< 0.05) are represented on graphs. ND4-male-NaCl = 3 animals, 89 cells; ND4-male-NaB = 5 animals, 136 cells; ND4-female-NaCl = 4 animals, 111 cells; ND4-female-NaB = 6 animals, 186 cells; ND20-male-NaCl = 5 animals, 131 cells, ND20-male-NaB = 4 animals, 87 cells; ND20-female-NaCl = 5 animals, 113 cells; ND20-female-NaB = 5 animals, 117 cells.

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)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.858
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.8580.143

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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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.

Study designBench or experimental
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractyes

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