The macrophage–biomaterial interface: a proteomic analysis of the conditioned medium environment
Bibliographic record
Abstract
Abstract BACKGROUND: Proteomic techniques provide an approach for exploration of intracellular structure and function as well as extracellular microenvironments. However, proteomics has not been extensively applied to examine cell–material interface dynamics or the conditioned medium (CM) that ensues. Monocyte‐derived macrophages (MDM) are of particular interest as they are the predominant cell type contributing to chronic inflammation at long‐term implant sites. The current study compares the CM generated when MDM were adherent to tissue culture polystyrene (TCPS) or a model polycarbonate–urethane (PCNU). Two‐dimensional electrophoresis and either matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry or liquid chromatography Fourier transform mass spectrometry were used for protein identification to begin to assess how the CM is altered when MDM are in contact with a degradable PCNU material relative to a typical tissue culture surface. RESULTS: Proteomic analysis of CM samples identified regulatory molecules associated with specific activation pathways, such as interferon‐γ, and degradative proteins, such as matrix metalloproteinases and chitinase‐like proteins, suggesting their involvement in the MDM response to PCNU materials. Furthermore, this study illustrates the challenges in analysing CM protein profiles against a background of intracellular protein, originating from lysed cells or cell fragments, and cell culture additives such as fetal bovine serum. CONCLUSIONS: The microenvironment surrounding an MDM–biomaterial interface is complex, containing proteins that relate to intracellular signalling pathways, cytoskeletal remodelling, degradation as well as the presence of serum factors necessary for cell attachment. The use of proteomics to determine the CM protein profile should continue to aid in the elucidation of cell–material interactions. Copyright © 2008 Society of Chemical Industry
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".