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Record W2996362238 · doi:10.1073/pnas.1914112117

Hypoxia induces a time- and tissue-specific response that elicits intertissue circadian clock misalignment

2019· article· en· W2996362238 on OpenAlexfundno aff
Gal Manella, Rona Aviram, Nityanand Bolshette, Sapir Muvkadi, Marina Golik, David F. Smith, Gad Asher

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsnot available
FundersAbisch-Frenkel-StiftungNational Heart, Lung, and Blood InstituteAzrieli Foundation
KeywordsCircadian rhythmHypoxia (environmental)Circadian clockBacterial circadian rhythmsIntermittent hypoxiaBiologyChronobiologyNeurosciencePeripheralFree-running sleepObstructive sleep apneaEndocrinologyInternal medicineLight effects on circadian rhythmMedicineChemistryOxygen

Abstract

fetched live from OpenAlex

The occurrence and sequelae of disorders that lead to hypoxic spells such as asthma, chronic obstructive pulmonary disease, and obstructive sleep apnea (OSA) exhibit daily variance. This prompted us to examine the interaction between the hypoxic response and the circadian clock in vivo. We found that the global transcriptional response to acute hypoxia is tissue-specific and time-of-day-dependent. In particular, clock components differentially responded at the transcriptional and posttranscriptional level, and these responses depended on an intact circadian clock. Importantly, exposure to hypoxia phase-shifted clocks in a tissue-dependent manner led to intertissue circadian clock misalignment. This differential response relied on the intrinsic properties of each tissue and could be recapitulated ex vivo. Notably, circadian misalignment was also elicited by intermittent hypoxia, a widely used model for OSA. Given that phase coherence between circadian clocks is considered favorable, we propose that hypoxia leads to circadian misalignment, contributing to the pathophysiology of OSA and potentially other diseases that involve hypoxia.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.291
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations134
Published2019
Admission routes1
Has abstractyes

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