Material surfaces affect the protein expression patterns of human macrophages: A proteomics approach
Bibliographic record
Abstract
Monocyte-derived macrophages (MDM) are key inflammatory cells and are central to the foreign body response to implant materials. MDM have been shown to exhibit changes in actin cytoskeleton, multinucleation, cell size, and function in response to small alterations in polycarbonate-urethane (PCNU) surface chemistry. Although PCNU chemistry has an influence on de novo protein synthesis, no assessments of the protein expression profiles of MDM have yet been reported. The rapid emerging field of expression proteomics facilitates the study of changes in cellular protein profiles in response to their microenvironment. The current study applied proteomic techniques, 2-dimensional electrophoresis (2-DE) combined with MALDI-ToF (matrix assisted laser desorption ionization-time of flight) mass spectrometry, to determine differences in MDM protein expression influenced by PCNU. Results indicated that MDM responded to material chemistry by modulation of structural proteins (i.e. actin, vimentin, and tubulin). Additionally, intracellular protein modulation which requires proteins responsible for trafficking (i.e. chaperone proteins) and protein structure modification (i.e. bond rearrangement and protein folding) were also altered. This study demonstrated for the first time that a proteomics approach was able to detect protein expression profile changes in MDM cultured on different material surfaces, forming the basis for utilizing further quantitative proteomics techniques that could assist in elucidation of the mechanisms involved in MDM-material interaction.
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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".