Effects of a synthetic di‐phosphoserine peptide (SS‐2) on gene expression profiling against TNF‐α induced inflammation
Bibliographic record
Abstract
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 ERK1/2, 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".