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Record W4224088951 · doi:10.3390/ijms23084338

The Role of Systemic Filtrating Organs in Aging and Their Potential in Rejuvenation Strategies

2022· review· en· W4224088951 on OpenAlexafffund
Amal Kassab, Nasser Rizk, Satya Prakash

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2022
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsRejuvenationBiologyGenetics

Abstract

fetched live from OpenAlex

Advances in aging studies brought about by heterochronic parabiosis suggest that agingmight be a reversable process that is affected by changes in the systemic milieu of organs andcells. Given the broadness of such a systemic approach, research to date has mainly questioned theinvolvement of "shared organs" versus "circulating factors". However, in the absence of a clearunderstanding of the chronological development of aging and a unified platform to evaluate thesuccesses claimed by specific rejuvenation methods, current literature on this topic remains scattered.Herein, aging is assessed from an engineering standpoint to isolate possible aging potentiators via ajuxtaposition between biological and mechanical systems. Such a simplification provides a generalframework for future research in the field and examines the involvement of various factors in aging.Based on this simplified overview, the kidney as a filtration organ is clearly implicated, for the firsttime, with the aging phenomenon, necessitating a re-evaluation of current rejuvenation studies tountangle the extent of its involvement and its possible role as a potentiator in aging. Based on thesefindings, the review concludes with potential translatable and long-term therapeutics for aging whileoffering a critical view of rejuvenation methods proposed to date.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.365
Teacher spread0.329 · 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 designNot applicable
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

Citations6
Published2022
Admission routes2
Has abstractyes

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