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Record W3098255473 · doi:10.1007/s00281-020-00824-x

The untwining of immunosenescence and aging

2020· review· en· W3098255473 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSeminars in Immunopathology · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsImmunosenescenceImmune systemSenescenceBiologyCellular senescenceImmunologyTelomereLongevityCellular AgingCell biologyGeneticsPhenotype

Abstract

fetched live from OpenAlex

From a holistic point of view, aging results from the cumulative erosion of the various systems. Among these, the immune system is interconnected to the rest as immune cells are present in all organs and recirculate through bloodstream. Immunosenescence is the term used to define the remodelling of immune changes during aging. Because immune cells-and particularly lymphocytes-can further differentiate after their maturation in response to pathogen recognition, it is therefore unclear when senescence is induced in these cells. Additionally, it is also unclear which signals triggers senescence in immune cells (i) aging per se, (ii) specific response to pathogens, (iii) underlying conditions, or (iv) inflammaging. In this review, we will cover the current knowledge and concepts linked to immunosenescence and we focus this review on lymphocytes and T cells, which represent the typical model for replicative senescence. With the evidence presented, we propose to disentangle the senescence of immune cells from chronological aging.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.305
Teacher spread0.287 · 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