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Differential expression of Tie-2 receptors and angiopoietins in response to in vivo hypoxia in rats

2001· article· en· W2300571892 on OpenAlexaff
Kefeya Abdulmalek, Fathia Ashur, N. Ezer, Fengchun Ye, Sheldon Magder, Sabah N. A. Hussain

Bibliographic record

VenueAmerican Journal of Physiology-Lung Cellular and Molecular Physiology · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsHypoxia (environmental)AngiopoietinBiologyInternal medicineReceptorEndocrinologyKidneyDownregulation and upregulationIn vivoMessenger RNAChemistryMedicineVascular endothelial growth factorBiochemistryGeneOxygen

Abstract

fetched live from OpenAlex

In this study, we assessed the effects of in vivo hypoxia on the expression of Tie-2 receptors and angiopoietins in various organs of conscious rats and correlated these effects with the expression of hypoxia-inducible factor-1 (HIF-1). RT-PCR and Southern blotting were used to amplify mRNA expression of angiopoietin-1, -2, and -3, Tie-2, and HIF-1 alpha in tissues of normoxic and hypoxic (fraction of inspired oxygen of 9--10% for either 12 or 48 h) rats. Hypoxia provoked a decline in angiopoietin-1 mRNA and Tie-2 mRNA, protein, and phosphorylation levels in the lung, liver, cerebellum, and heart but not in the kidney and diaphragm. In comparison, hypoxia raised the levels of angiopoietin-2 mRNA in the cerebellum and angiopoietin-3 mRNA in the lung, kidney, and diaphragm. HIF-1 alpha mRNA was abundant in most organs of normoxic rats but was significantly induced in the kidney and diaphragm of hypoxic rats. We conclude that in vivo hypoxia exerts inhibitory effects on the activity of the angiopoietin-1/Tie-2 receptor pathway through reduction of angiopoietin-1 and upregulation of angiopoietin-2 and -3. Induction of angiopoietin-3 in the kidney and diaphragm of hypoxic rats could be mediated through the HIF-1 transcription factor.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.101
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.229
Teacher spread0.225 · 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

Citations68
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Physiology-Lung Cellular and Molecular PhysiologySame topicCancer, Hypoxia, and MetabolismFrench-language works237,207