Alkaline phosphatase inhibits TNF-α and IL-6 release by freshly extracted human leukocytes in the absence of LPS: a promising therapeutic candidate
Bibliographic record
Abstract
BACKGROUND AND PURPOSE The use of human intestinal alkaline phosphatase (IAP) has already been validated as a novel treatment for endotoxin lipopolysaccharide (LPS)-induced inflammatory diseases, which is mediated through IAP’ s ability to detoxify LPS. However, there has been little investigation into the full extent of IAP’ s physiological function. This study investigates IAP’s non-LPS related functions and clinical applications. EXPERIMENTAL APPROACH In this study, we use freshly extracted human leukocytes to study effects of a recombinant human intestinal alkaline phosphatase (recIAP) on secretion of TNF-ɑ and IL-6 by the leukocytes in absence of LPS. Physiological substrates of alkaline phosphatase and their dephosphorylated products at neutral pH including ATP, ADP, AMP and adenosine were employed to investigate their effects on secretion of TNF-ɑ by the leukocyte. KEY RESULTS We found that recIAP inhibit TNF-α and IL-6 secretion by freshly extracted human leukocyte in the absence of LPS. recIAP dephosphorylates ATP and ADP etc at physiological pH. The dephosphorylated products by recIAP including AMP and adenosine inhibit TNF-α secretion of the freshly extracted human leukocyte. CONCLUSIONS AND IMPLICATIONS Human leukocyte migrates into inflamed tissues and secretes TNF-α and IL-6. Thus, leukocyte plays an important role in inflammatory diseases. recIAP inhibits TNF-α and IL-6 secreted by freshly extracted human leukocyte in the absence of LPS. The results in this study indicate that recIAP is a promising therapeutic candidate for diseases related to leukocyte TNF-α and IL-6 secretion.
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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 | 0.001 |
| 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".