Absence of sphingosine kinase 1 alters progression of TNF-alpha induced arthritis (99.25)
Bibliographic record
Abstract
Abstract Changes in sphingolipid levels can alter cellular functions. Sphingosine 1 phosphate (S1P) and hTNF stimulates COX-2 and PGE2 in fibroblast-like synoviocytes (FLS). In vitro, hTNF and S1P both produce more COX-2 and PGE2 than either separately. Blocking sphingosine kinase 1(SphK1) decreases COX-2 and PGE2 after hTNF stimulation. Our hypothesis is mice overexpressing hTNF without functional SphK1 (hTNF/SphK1-/-) will have less synovial inflammation than mice that overexpress hTNF with functional SphK1 (hTNF/SphK1+/+). Transgenic hTNF mice were crossed with SphK1-/- mice, genotyped and observed, while histological sections were collected to evaluate disease activity. FLS were isolated from the knee joints of WT and SphK1-/- mice, cultured, and stimulated with hTNF. hTNF/SphK1-/- mice had significantly lower arthritis scores, less inflammatory infiltrates and preserved joint spaces compared to hTNF/SphK1+/+ mice. hTNF-stimulated FLS from SphK1-/- mice produced less PGE2 than WT mice. Deletion of SphK1 significantly delays progression and severity of hTNF induced arthritis, synovial proliferation and inflammatory infiltrates. SphK1-/- FLS produced less PGE2 in response to hTNF. Therefore, inhibiting SphK1 is a potential novel therapeutic agent. The project described was supported by Grant Number R01GM062887 from the National Institute of General Medical Sciences and a grant from the ACR REF.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".