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Metabolomic pattern associated with physical sequelae in patients presenting with respiratory symptoms validates the aestivation concept in dehydrated patients

2024· article· en· W4396870332 on OpenAlexafffund
Annelie Barrueta Tenhunen, Guillaume Butler‐Laporte, Satoshi Yoshiji, David Morrison, Tomoko Nakanishi, Yiheng Chen, Vincenzo Forgetta, Yossi Farjoun, Adriana Marton, Jens Titze, Sandra Nihlén, Robert Frithiof, Miklós Lipcsey, J. Brent Richards, Michael Hultström

Bibliographic record

VenuePhysiological Genomics · 2024
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health ResearchFonds de recherche du QuébecNjurfondenLady Davis Institute for Medical ResearchHjärt-LungfondenMinistry of Education, Culture, Sports, Science and TechnologyCanada Foundation for InnovationVetenskapsrådetMcGill UniversityPublic Health Agency of CanadaJapan Society for the Promotion of ScienceFoundation for the National Institutes of HealthCancer Research UKSvenska Läkaresällskapet
KeywordsAestivationDehydrationCohortWastingMedicineInternal medicineAcute kidney injuryMetabolomeUreaOsmolyteHyponatremiaCohort studyPhysiologyBiologyBiochemistryMetaboliteEcology

Abstract

fetched live from OpenAlex

We have previously shown that humans exhibit an aestivation-like response where dehydration leads to a metabolic shift to urea synthesis, which is associated with long-term weakness indicating muscle wasting. In the present study, we validate this response in a new cohort and present a deeper metabolomic analysis and pathway analysis. Finally, we present a sex-stratified analysis suggesting weaker aestivation in women. However, women show less dehydration, so the association warrants further study.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.021
GPT teacher head0.264
Teacher spread0.244 · 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 designObservational
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

Citations6
Published2024
Admission routes2
Has abstractyes

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