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Should we Try to Alleviate Immunosenescence and Inflammaging - Why, How and to What Extent?

2019· review· en· W2987747230 on OpenAlexaff
Jacek M. Witkowski, Ewa Bryl, Tamàs Fülöp

Bibliographic record

VenueCurrent Pharmaceutical Design · 2019
Typereview
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversité de Sherbrooke
FundersMinisterstwo Edukacji i Nauki
KeywordsImmunosenescenceImmune systemOrganismBiologyImmunityImmunologyGenetics

Abstract

fetched live from OpenAlex

With advancing age, immune responses of human beings to external pathogens, i.e., bacteria, viruses, fungi and parasites, and to internal pathogens - malignant neoplasm cells - become less effective. Two major features in the process of aging of the human immune system are immunosenescence and inflammaging. The immune systems of our predecessors co-evolved with pathogens, which led to the occurrence of effective immunity. However, the otherwise beneficial activity may pose problems to the organism of the host and so it has builtin brakes (regulatory immune cells) and - with age - it undergoes adaptations and modifications, examples of which are the mentioned inflammaging and immunosenescence. Here we describe the mechanisms that first created our immune systems, then the consequences of their changes associated with aging, and the mechanisms of inflammaging and immunosenescence. Finally, we discuss to what extent both processes are detrimental and to what extent they might be beneficial and propose some therapeutic approaches for their wise control.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.970
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.390
GPT teacher head0.467
Teacher spread0.077 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations11
Published2019
Admission routes1
Has abstractyes

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