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Record W4390685640 · doi:10.1016/j.fochx.2024.101125

Effects of phosphoglycerate kinase 1 and pyruvate kinase M2 on metabolism and physiochemical changes in postmortem muscle

2024· article· en· W4390685640 on OpenAlexaff
Caiyan Huang, Dequan Zhang, Christophe Blecker, Yingxin Zhao, Can Xiang, Zhenyu Wang, Shaobo Li, Li Chen

Bibliographic record

VenueFood Chemistry X · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Alberta
FundersAgricultural Science and Technology Innovation ProgramChinese Academy of Agricultural SciencesChina Scholarship CouncilInstitute of Food Science and Technology, Chinese Academy of Agricultural SciencesNational Natural Science Foundation of China
KeywordsPKM2GlycolysisPyruvate kinasePhosphoglycerate kinaseBiochemistryCalpainMyofibrilKinaseChemistryBiologyCell biologyEnzyme

Abstract

fetched live from OpenAlex

The objective of this work was to investigate the influence of phosphoglycerate kinase-1 (PGK1) and pyruvate kinase-M2 (PKM2) activity on glycolysis, myofibrillar proteins, calpain system, and apoptosis pathways of postmortem muscle. The activity of PGK1 and PKM2 was regulated by their inhibitors and activators to construct the postmortem glycolysis vitro model and then incubated at 4 °C for 24 h. The results showed that compared to PGK1 and PKM2 inhibitors groups, the addition of PGK1 and PKM2 activators could accelerate glycogen consumption, ATP and lactate production, while declining pH value. Moreover, the addition of PGK1 and PKM2 activators could increase desmin degradation, μ-calpain activity, and caspase-3 abundance. Interestingly, troponin-T degradation was significantly increased both in PKM2 inhibitor and activator groups. It was suggested that PGK1 and PKM2 might be used as robust indicators to regulate meat quality by affecting the glycolysis, myofibrillar proteins, μ-calpain and apoptosis pathways in postmortem muscle.

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 score0.013
Threshold uncertainty score0.242

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 studies0.0000.000
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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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