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Renal Oxygen Sensing Mechanisms May Contribute to Maintaining Cerebral Perfusion During Acute Anemia

2021· article· en· W3170655872 on OpenAlexaff
Kyle Chin, Hannah Joo, Iryna Savinova, Helen Jiang, Chloe Lin, Melina P. Cazorla‐Bak, Jeremy A. Simpson, Kim A. Connelly, Richard E. Gilbert, Andrew Baker, C. David Mazer, William C Darrah, Gregory M. T. Hare

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsMuscular Dystrophy CanadaUniversity of TorontoUniversity of GuelphSt. Michael's Hospital
Fundersnot available
KeywordsErythropoietinMedicineHypoxia (environmental)KidneyAnemiaRenal blood flowInternal medicineCerebral blood flowPerfusionRenal circulationEndocrinologyCardiologyOxygenChemistry

Abstract

fetched live from OpenAlex

Introduction The integrative physiological response to anemia is complex and incompletely understood. We utilized data from studies of acute anemia in rodents to determine the relationship between changes in blood oxygen content (C a O 2 ) and the heterogenous response of the kidney, heart and brain. We hypothesize that renal hypoxia sensing mechanisms contribute to adaptive physiological responses to maintain cerebral oxygen delivery (DO 2 ) during acute anemia. Methods With animal care committee approval, we synthesized novel and published data from 5 previously published studies. Outcomes included: assessment of the relationship between C a O 2 and microvascular renal and brain PO 2 (phosphorescence quenching of oxyphor G4); cardiac output (CO) and renal and cerebral blood flow (ultrasound Doppler); hypoxia induced cellular responses (brain and kidney erythropoietin (EPO) mRNA and serum protein levels (ELISA)). Statistical analysis (SigmaPlot 14) was performed by ANOVA, Holm‐Sidak and Mann‐Whitney rank sum test when appropriate. Significance was assigned at p<0.05. Results In two models of anemia (hemodilution and RBC antibody mediated), acute reductions in blood C a O 2 were associated with larger decreases in renal microvascular PO 2 , relative to brain microvascular PO 2 (p<0.05). After acute hemodilution, there was a strong relationship between C a O 2 and renal microvascular PO 2 (r 2 =0.75). The magnitude of reduction in renal microvascular PO 2 correlated with the degree of renal EPO mRNA expression and serum EPO protein levels. The magnitude of the increase in EPO mRNA was much larger in the kidney than in the brain (p<0.03). While no change in renal blood flow was observed in either model, a significant increase in common carotid and internal carotid blood flow was observed in both models (p<0.012). When DO 2 was assessed, the kidney DO 2 was reduced at all levels of anemia (p<0.01) whereas brain tissue DO 2 was maintained in mild and moderate (Hb 90 and 70 g/L) (p=0.44) anemia but reduced in severe anemia (Hb 50 g/L) (p<0.02). The role of active cardiovascular increases in brain blood flow and maintained DO 2 during anemia was impaired by systemic beta blockade, suggesting that active cardiovascular mechanisms are required to maintain optimal brain DO 2 during anemia. Discussion Our analysis demonstrated: evidence of quantitative renal PO 2 sensing of changes in C a O 2 ; the clamping of renal blood flow (reduced DO 2 ) during anemia may be a central mechanism allowing for sensing of changes in C a O 2 ; reduced arterial C a O 2 resulted in a local renal hypoxia response (increased serum EPO) and may have initiated the cardiovascular response to increase cerebral blood flow and maintain cerebral DO 2 . Inhibition of the adrenergic system impaired these responses and resulted in reduced brain DO 2 . Understanding the heterogeneous adaptive responses to acute anemia may inform clinical practice and optimize management of acutely anemia patients.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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