Anti-inflammatory Effects of Poly-<scp>l</scp>-lysine in Intestinal Mucosal System Mediated by Calcium-Sensing Receptor Activation
Bibliographic record
Abstract
Calcium-sensing receptor (CaSR) is involved in maintaining cellular homeostasis and promoting recovery of damaged intestinal epithelial cells (IECs). Poly-L-lysine (PL) is a basic polypeptide identified for its role in the activation of CaSR through allosteric binding. The primary goal of the current study was to identify the modulatory effect of PL on intestinal inflammation and to determine whether these effects were mediated by CaSR activation. We used human intestinal epithelial cell lines, Caco-2 and HT-29, to assess PL anti-inflammatory activities in vitro. We found that PL reduced the IL-8 secretion from tumor necrosis factor (TNF)-α-treated human intestinal epithelial cell lines. On the other hand, the gene expression of pro-inflammatory cytokines TNF-α, IL-6, and IL-1β was inhibited by PL supplementation. We subsequently evaluated the anti-inflammatory activity of PL in vivo using a DSS-induced mouse colitis model. PL supplementation was shown to prevent dextran sulfate sodium salt (DSS)-induced loss of weight, colitic symptoms, and shortening of colon length but maintained colonic morphology. The pro-inflammatory cytokine expression in the mouse colon, including TNF-α, IL-6, INF-γ, IL-17, and IL-1β, was significantly up-regulated by DSS treatment, but was inhibited upon PL administration. As shown by the results from both in vitro and in vivo studies, the reduction of inflammatory cytokine production caused by PL was reversed by NPS-2143 pretreatment. In the present study, we provide evidence that PL exerts anti-inflammatory effects on the gut system, which is primarily mediated by allosteric ligand activation of CaSR.
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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".