In vitro immunomodulatory effects of novel strontium and zinc-containing GPCs
Bibliographic record
Abstract
BACKGROUND: Glass polyalkenoate cements (GPCs) are bio-adhesives which consist of ionomeric glass particles embedded in a poly-salt matrix. These materials have been used in dentistry and orthodontics extensively but are presently being optimized as bone putties for orthopedic applications. OBJECTIVE: This study utilized a patented ionomeric glass (mole fraction: SiO2:0.48, ZnO:0.36, CaO:0.12, SrO:0.04) to formulate two GPCs: GPC A (<45 μm particle size glass) and GPC B (45 μm-63 μm). These formulations were previously assessed for their effect on osteoblast viability and osteogenic function. However, the immunomodulatory effects of GPC A and B have not previously been investigated. METHOD: Non-toxic concentrations of (a) GPC dissolution products and (b) fragmented GPC particles were tested for their ability to affect the secretion of cytokines (TNF-α, IL-1β, IL-6 and IL-10) by rat peripheral blood mononuclear cells (PBMCs), in the presence or absence of the stimulant liposaccharide (LPS). Additionally, the ionic concentrations of Sr, Zn, Ca, and Si were measured in GPC ionic extracts, and the size, shape and concentration of fragmented GPC particles in deionized water were characterized using an optical microscope-based particle analyzer. RESULTS: The results showed that GPC A ionic products reduced the concentration of TNF-α secreted by stimulated cells compared with cells stimulated in the absence of GPC products. Interestingly, the particles released from GPC A significantly increased the secretion of both TNF-α and IL-6 from unstimulated cells, compared to control cells. CONCLUSION: Neither GPC B ionic products nor released particles were found to be biologically active with respect to PBMC cytokine 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".