Modulating macrophage polarization on titanium implant surface by poly(dopamine)‐assisted immobilization of IL4
Bibliographic record
Abstract
BACKGROUND: In the past few decades, very little research has been carried out to modify implant surfaces to improve osteointegration through the regulation of immune cells. PURPOSE: The aim of this study is to investigate whether the poly(dopamine) (pDA)-assisted immobilization of IL4 on titanium surfaces could modulate the inflammatory profile of macrophages in vitro and search for the possibility of enhancing implant integration in this way. MATERIAL AND METHODS: The surface composition, topography, and roughness of SLA, SLA-pDA, and SLA-pDA-IL4 discs were examined by scanning electron microscope (SEM) and energy dispersive spectrometer (EDS). Then the releasing profile of the SLA-pDA-IL4 implants was recorded for 1 week and the bioactivity of released IL4 was investigated by ELISA. Then macrophage polarization was investigated via three methods including: (a) surface marker via immunofluorescence; (b) mRNA levels of M1 and M2 polarization markers via real-time PCR, and (c) cytokine release via ELISA. RESULTS: SEM and EDS revealed that pDA and IL4 were coated successfully on SLA surfaces. The ELISA results showed that IL4 remained its bioactivity on SLA surface and were immobilized on the SLA surface. The immobilization of IL4 through pDA has no significant influence on the attachment, morphology, and proliferation of macrophages, while it increased the M2/M1 proportion in human macrophages revealed by immunofluorescence. The real-time PCR and ELISA results demonstrated that SLA-pDA-IL4 surface reduced the pro-inflammatory profile compared with SLA-pDA and SLA surfaces. CONCLUSIONS: The SLA-pDA-IL4 surfaces described here is able to activate adherent macrophages into M2 phenotype and reduce the release of pro-inflammatory cytokines. Immobilization of IL4 via pDA is convenient and effective, thus providing an applicable way to control macrophage behavior upon implant insertion and is anticipated to accelerating further bone integration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".