Immunoblot analysis of proteins associated with self‐assembled monolayer surfaces of defined chemistries
Bibliographic record
Abstract
Intact and fragmented proteins, eluted from self-assembled monolayer (SAM) surfaces of alkanethiols of different chemistries (-CH₃, -OH, -COOH, -NH₂), following exposure to human plasma (HP) or human serum (HS), were examined using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblotting techniques. The SAM surfaces were incubated for 1 h with 10% (v/v) sterile-filtered, heat-inactivated (h.i.) HS or 1% (v/v) sterile-filtered h.i. HP preparations [both in phosphate buffered saline (PBS)]. Adsorbed proteins were eluted using 10% SDS/2.3% dithioerythritol for characterization of protein profiles. The type of incubating medium may be an important determinant of adsorbed protein profiles, since some variations were observed in eluates from filtered versus control unfiltered h.i. 10% HS or 1% HP. Albumin and apolipoprotein A1 were consistently detected in both filtered h.i 10% HS and 1% HP eluates from all SAM surfaces and from control tissue culture-treated polystyrene (TCPS). Interestingly, Factor H and Factor I, antithrombin, prothrombin, high molecular weight kininogen (HMWK), and IgG were present in eluates from OH, COOH, and NH₂ SAM surfaces and in eluates from TCPS but not in eluates from CH₃ SAM surfaces, following exposure to filtered h.i. 10% HS. These results suggest that CH₃ SAM surfaces were the least proinflammatory of all SAM surfaces. Overall, similar trends were observed in the profiles of proteins eluted from surfaces exposed to filtered 10% HS or 1% HP. However, the unique profiles of adsorbed proteins on different SAM surface chemistries may be related to their differential interactions with cells, including immune/inflammatory cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".