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Differential expression of the genes involved in amino acids and nitrogen metabolisms during liver regeneration of mice

2008· article· en· W2117683664 on OpenAlexaff
Yunsheng Yuan, Xiaoying Wu, Qiuxiang Ou, Jin Gao, Bud C. Tennant, Wei Han, Yan Yu

Bibliographic record

VenueHepatology Research · 2008
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAmino acidLiver injuryLiver regenerationMetabolismGeneRegeneration (biology)BiologyCarbon tetrachlorideGene expressionBiochemistryLiver functionChemistryCell biologyEndocrinologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

AIM: Liver regeneration is a highly coordinated response to hepatic injury or resection that is controlled by the body's overall requirement for liver function. The level of circulating amino acids in blood increases after acute liver injury and administration of amino acid mixtures induces hepatic DNA replication. These findings suggest a close connection between amino acid metabolism and hepatic proliferation. However, the underlying molecular mechanisms have not been completely elucidated. Here, we applied a cDNA micro-array technique to analyze expression profiles of the genes associated with nitrogen and amino acid metabolism during liver regeneration in mice following treatment with CCl(4). METHODS: Seventy-nine genes were identified for their significantly altered expression patterns at different stages of liver damage and regeneration. RESULTS: We observed that the numbers of down-regulated genes were remarkably higher than that of up-regulated genes at 1.5 days following carbon tetrachloride administration when hepatic DNA replication was most active, indicating the existence of a counter balance between cell proliferation and liver metabolism functions. CONCLUSIONS: Our results suggest that suppression of amino acids metabolism after acute liver injury results in the accumulation of amino acids in plasma that serves as a driving force for liver regeneration.

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 score0.064
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.080
GPT teacher head0.324
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 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

Citations8
Published2008
Admission routes1
Has abstractyes

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