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Record W2909798042 · doi:10.1111/ijfs.14077

Effects of a synthetic di‐phosphoserine peptide (SS‐2) on gene expression profiling against TNF‐α induced inflammation

2019· article· en· W2909798042 on OpenAlexaff
Hua Zhang, Cai Na Xu, Yoshinori Mine

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNF-κB Signaling Pathways
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPhosphoserinep38 mitogen-activated protein kinasesInflammationNF-κBMAPK/ERK pathwayCell biologyPhosphorylationTumor necrosis factor alphaIκBαSignal transductionPeptideHedgehog signaling pathwayChemistryBiologyBiochemistryImmunologySerine

Abstract

fetched live from OpenAlex

Summary It has been showed bioactive di‐phosphoserine peptide ( SS ‐2) possesses functions to ameliorate oxidative stress in vitro . This study aimed to substantiate the role of bioactive di‐phosphoserine peptide ( SS ‐2) in modulating inflammatory responses in TNF ‐α‐stimulated HT ‐29 cells, and its mechanism of action. SS ‐2 significantly reduced IL ‐8 secretion in TNF ‐α‐induced HT ‐29 cells, and also suppressed pro‐inflammatory cytokines, including IL ‐8, IL ‐12, MCP ‐1 and TNF ‐α. Moreover, SS ‐2 inhibited TNF ‐α initiated signalling cascades by suppressing phosphorylation of the ERK 1/2, JNK , P38 and IκB those culminate in above cellular inflammatory responses. Differentially expressed genes analysis within NF ‐κB signalling pathway revealed that SS ‐2 blocks multiple sites of upstream NF ‐κB signalling cascade, including FADD and MyD88, thereby preventing the signalling transduction involved in cellular inflammatory response. These results provide a new insight into molecular mechanism for anti‐inflammatory action of SS ‐2, suggesting SS ‐2 is a potential alternative approach to treat IBD by particular targeting TNF ‐α driven inflammatory event.

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)0.001
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 score0.004
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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