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Record W4414398593 · doi:10.1139/cjz-2025-0061

Molecular control of inositol pentaphosphate in red blood cells of pirarucu ( <i>Arapaima gigas</i> )

2025· article· en· W4414398593 on OpenAlexvenueno aff
Giovana A. Bataglion, Adalberto Luís Val

Bibliographic record

VenueCanadian Journal of Zoology · 2025
Typearticle
Languageen
FieldMedicine
TopicSynthesis of Organic Compounds
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado do AmazonasConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsInositolAdenosine triphosphateInositol phosphateGeneGuanosinePhosphateAdaptation (eye)Gene expressionOxygen

Abstract

fetched live from OpenAlex

The oxygen affinity of fish hemoglobins is regulated by organic phosphates, adjusting oxygen uptake and delivery. While adenosine triphosphate (ATP) and guanosine triphosphate (GTP) are common regulators, inositol pentaphosphate (IP 5 ) is unique to certain fish and birds. The pirarucu ( Arapaima gigas (Schinz, 1822)), an air-breathing Amazonian fish, transitions from gill respiration to air-breathing, accompanied by changes in Hb-O2 affinity regulation. This study examined IP 5 biosynthesis in pirarucu erythrocytes across development. Organic phosphate levels (ATP, GTP, IP 5 ) and expression of genes involved in inositol phosphate metabolism ( ipmk, itpk-1, ip3k) were analyzed in aquatic (9.86 ± 0.38 cm) and air-breathing (60.2 ± 3.8 cm) specimens. Air-breathing pirarucu had higher erythrocyte IP 5 and lower ATP and GTP. Gene expression analysis showed increased ipmk, itpk-1, and ip3k, suggesting regulatory rather than molecular changes. Phylogenetic analysis confirmed conservation of these genes. This ontogenetic increase in IP 5 , along with the upregulation of genes involved in its synthesis, highlights a genetic and cellular adaptation that may contribute to improved oxygen regulation during aerial respiration in A. gigas.

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 score0.244
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.211
Teacher spread0.206 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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