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Record W4400904828 · doi:10.3390/antiox13080888

Investigation of Antioxidant Mechanisms of Novel Peptides Derived from Asian Swamp Eel Hydrolysate in Chemical Systems and AAPH-Induced Human Erythrocytes

2024· article· en· W4400904828 on OpenAlexaff
Xiao Wang, Bingjie Chen, Khushwant S. Bhullar, Hang Yang, Xiaohu Luo, Juan Fu, Hongru Liu, Di Su, Dapeng Sun, Yongjin Qiao, Wenzong Zhou

Bibliographic record

VenueAntioxidants · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsHydrolysateAntioxidantSwampChemistryPeptideBiochemistryFisheryBiologyEcologyHydrolysis

Abstract

fetched live from OpenAlex

Sixteen novel antioxidant peptides from Asian swamp eel (ASE) were identified in previous studies. However, their chemical and cellular antioxidant mechanisms remain unclear. Molecular docking of these peptides with ABTS and DPPH radicals revealed the critical role of hydrogen bonding and Pi-Pi stacking hydrophobic interactions between hydrophobic amino acid residues and free radicals. Residues, such as tryptophan, proline, leucine, and valine, played significant roles in these interactions. All these peptides exhibited notable erythrocyte morphoprotective effects in a model of AAPH-induced oxidative damage of human erythrocytes. Erythrocyte hemolysis was reduced primarily through the modulation of both non-enzymatic (GSH/GSSG) and enzymatic antioxidant systems (SOD, CAT, and GSH-Px) by these peptides. A decrease in levels of MDA, LDH release, and hemoglobin oxidation was observed. Among the peptides, VLYPW demonstrated superior chemical and cellular antioxidant activities, which may be attributed to its higher levels of tyrosine and tryptophan, as well as to its increased hydrophobic amino acid content.

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 score0.024
Threshold uncertainty score0.812

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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