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Abstract 4136204: Clearing Senescent Cells Improves Mouse Survival Rate Post Myocardial Infarction through Alteration of Cardiomyocyte and Immune Cell Subpopulations

2024· article· en· W4404302470 on OpenAlexaff
Mozhdeh Mehdizadeh, Francis Leblanc, Patrice Naud, Guillaume Lettre, Éric Thorin, Gerardo Ferbeyre, Stanley Nattel

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineImmune systemMyocardial infarctionCellCardiologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Cellular senescence often involves a p16-pathway, and p16 overexpression is a hallmark of senescent cells. The role of cellular senescence in myocardial infarction (MI) and any mediating mechanisms remain unclear. Aims: To investigate the effect of p16 + cell clearance on survival post MI and elucidate underlying mechanisms. Methods: We utilized INK-ATTAC transgenic mice, in which p16 + cells undergo targeted apoptosis upon exposure to AP20187 (AP). Sham and MI mice were treated with AP or vehicle (V) twice-weekly for one month, starting 3-4 hours post-MI. Survival rate improvement post MI in the AP group (Fig A, P<0.001) prompted us to evaluate effects of senescent cell clearance at the cardiac cellular level and its involvement in MI-associated pathways at 3 days post-MI. The infarct and peri-infarct areas were collected and snap-frozen. Nuclei were extracted with modified gentleMACS methods; libraries were constructed using 10X Genomics. Standard software (CellRanger, Seurat) were employed for single-nuclei-RNA-seq data analysis. Results: Cardiomyocytes (CMs) showed 5 distinct subsets. CM subclusters 1 and 2 showed enrichments of genes characteristic of infarct boarder zones, while most sham CMs were found in subcluster 0, reflecting healthy-CMs. AP-treated mice exhibited a higher proportion of healthy CMs (Fig B). Differentially expressed genes (DEGs) between V and AP group were enriched in the healthy CMs, suggesting that clearance of p16 + cells improves the status of CMs post MI (Fig C). Immune-cell profiling showed that by far the largest number of DEG for AP vs vehicle were expressed in the (arginase-1) Arg1 macrophage subgroup (Fig D). The percentage of Arg1 macrophages decreased in the AP group (FDR=0.059) (Fig E). We noted significant enrichment in the hypoxia and cardiac rupture pathways in Arg1 macrophages (Fig F), correlating with the high death rate from myocardial rupture in V-mice. Conclusion: These results suggest a significant role of p16 + senescent cells in post-MI mortality, potentially through effects on cardiomyocytes and a specific macrophage subpopulation, Arg1 macrophages.

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: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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
GenreOther

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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