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Record W2186073278 · doi:10.18632/aging.100447

Epigenetic bystander-like effects of stroke in somatic organs

2012· article· en· W2186073278 on OpenAlexafffund
Anna Kovalchuk, Michael D. Lowings, Rocio Rodriguez‐Juarez, Arif Muhammad, Slava Ilnytskyy, Bryan Kolb, Olga Kovalchuk

Bibliographic record

VenueAging · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Lethbridge
FundersCanadian Institutes of Health Research
KeywordsBystander effectSomatic cellEpigeneticsKidneyStroke (engine)InflammationmicroRNAMedicineBiologyImmunologyBioinformaticsInternal medicineGeneGenetics

Abstract

fetched live from OpenAlex

Clinical evidence suggests that stroke may lead to damage of somatic organs. This communication of damage is well- established in the case of exposure to genotoxic agents is termed a bystander effect. Genotoxic stress-induced bystander effects are epigenetically mediated. Here we investigated whether stroke causes epigenetic bystander-like effects in the liver, kidney and heart. We found a significant increase in the levels of H3K3 acetylation and H3K4 trimethylation, as well as a decrease in the H3K9 trimethylation in the kidney tissue of stroked rats. Furthermore, here we for the first time show changes in the gene and microRNA expression profile in the kidney tissues of stroked rats, as compared to intact control animals. Interestingly, the observed changes were somewhat similar to those reported earlier in kidney injury, inflammation, and acute renal failure. Our data explain the recent epidemiological evidence for the increased incidence of acute kidney injury post-stroke and provide an important roadmap for the future analysis of the mechanisms and cellular repercussions of the stroke-induced bystander-like effects in distal somatic organs.

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 score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.230
Teacher spread0.225 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

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