Effect of enginereed GOLD nanoparticles targeted to mesenchymal cells on inflammatory response
Bibliographic record
Abstract
In a previous work we proved that gold nanoparticles engineered with a specific anti-CD44 antibody (GNP-HCe) directed against mesenchymal cells (MCs)from BOS patients were able to specifically inhibit their proliferation and increase their apoptosis. Aim of this work is to prove that GNP-HCe do not stimulate inflammatory cells in vitro since inflammatory process plays a pivotal role in BOS onset and its activation would exacerbate the reactive milieu. Macrophages were isolated from BAL of three subjects by adhesion procedure. Lymphocytes and neutrophils were obtained from peripheral blood by Lympholyte gradient. IL-8 was assayed by ELISA test while IFN-g, IL-17 and IL-10 by Elispot. Elastase release was evaluated by enzymatic procedure. Apoptosis and proliferation rate were evaluated by flow cytometry (Annexin V and CFSE). In vitro experiments proved that 24 and 48 h of incubation with GNP-HCe did not increase IL-8 secretion by macrophages but instead a significant (P<0,001) reduction was observed. Similarly, IFN-g, IL-17 and IL-10 produced by lymphocytes significantly decreased in presence of functionalized nanoparticles. Neutrophils were not stimulated by incubation with GNP-HCe as evidenced by unchanged elastase release in the culture medium. Lymphocyte proliferation rate in presence of GNP-HCe was not significantly different from controls while a significant increase (p<0.01) of apoptosis was recorded at 24-48 h incubation. This work opens new scenarios on the possibility to target a drug to specific cells without rising a further inflammatory response that would contribute to perpetuate the vicious cycle epithelial damage/inflammatory response/fibroproliferative process.
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 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".