Differential response of human blood leukocytes to brushite, monetite, and calcium polyphosphate biomaterials
Bibliographic record
Abstract
Calcium phosphate-based biomaterials are extensively used for bone replacement and regeneration in orthopedic, dental, and maxillofacial surgical applications. The injury induced by surgical implantation of bone replacement graft materials initiates a cascade of host responses, starting with blood-biomaterial contact, protein adsorption on the material surface, blood coagulation, and leukocyte responses. During the initial acute inflammatory response, polymorphonuclear neutrophils (PMNs) and monocytes, abundant circulating leukocytes of the myeloid lineage, are recruited to the site of inflammation. In addition to responding to pathogenic challenges, these cells respond to particulate substances within the body including crystals of monosodium urate (MSU). Host responses toward grafts impact short- and long-term success in tissue engineering and regenerative applications. Although multinucleated osteoclasts, formed by monocyte/macrophage fusion, are generally thought to be responsible for resorption of implant biomaterials, the ability of different biomaterials to trigger PMNs, which are invariably present at the early stages after implant surgery, and are abundant in the oral cavity, has never been tested. In this article, we present analysis of the response of human blood-derived PMNs and monocytes toward brushite, monetite, and calcium polyphosphate (CPP) biomaterial substrates and compare this to the response to MSU crystals, the latter serving as a positive control. Employing multicolor flow cytometry to look at PMN and monocyte cell surface markers of activation to gauge the response to different biomaterials, we found that both types of myeloid cells are highly activated after exposure to brushite, monetite, and MSU but not CPP. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 108B:253-262, 2020.
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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".