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Record W2934424005 · doi:10.1002/mnfr.201800987

Effect of Different Cereal Peptides on the Development of Type 1 Diabetes is Associated with Their Anti‐inflammatory Ability: In Vitro and In Vivo Studies

2019· article· en· W2934424005 on OpenAlexaff
Suling Sun, Hao Zhang, Kai Shan, Tianjun Sun, Mengyuan Lin, Lingling Jia, Yong Q. Chen

Bibliographic record

VenueMolecular Nutrition & Food Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsIn vivoInsulitisNOD miceNodStreptozotocinInflammationIn vitroDiabetes mellitusChemistryAntioxidantApoptosisPharmacologyPeptideInternal medicineEndocrinologyBiochemistryBiologyMedicine

Abstract

fetched live from OpenAlex

SCOPE: The aim of the study is to explore which properties of selected peptides will positively predict their antidiabetic activity in vitro and in vivo. METHODS AND RESULTS: ) for 10 weeks. CP and WP improve hyperglycemia homeostasis in streptozotocin-induced diabetic mice. Female nonobese diabetic (NOD) mice are treated with CP, WP, fractions C1 and C2 (isolated from CP), and W1 and W2 (isolated from WP) beginning at 3 weeks of age. CP, C2, and W2 delay the initiation of diabetes and decrease serum IL-6 levels in NOD mice. CP also reduces insulitis and increases the β-cell area in NOD mice. MIN-6 cells are incubated with the selected peptides. CP, C2, and W2 result in the reduced expression of LPS-induced IL-6 mRNA in MIN-6 cells. CP inhibits signaling pathways related to apoptosis and inflammation. The antioxidative, hydrophobic, and proliferative properties of the selected peptides are analyzed. The hypoglycemic effects of cereal peptides are not associated with their antioxidant activity, hydrophobicity, or proliferative ability. CONCLUSION: Findings suggest that the effect of cereal peptides on the development of T1D is associated with their anti-inflammatory ability.

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)0.001
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 score0.019
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.291
Teacher spread0.269 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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