Surface chemistry to minimize fouling from blood-based fluids
Bibliographic record
Abstract
Upon contact with bodily fluids/tissues, exogenous materials spontaneously develop a layer of proteins on their surface. In the case of biomedical implants and equipment, biological processes with deleterious effects may ensue. For biosensing platforms, it is synonymous with an overwhelming background signal that prevents the detection/quantification of target analytes present in considerably lower concentrations. To address this ubiquitous problem, tremendous efforts have been dedicated over the years to engineer protein-resistant coatings. There is now extensive literature available on stealth organic adlayers able to minimize fouling down to a few ng cm(-2), however from technologically irrelevant single-protein buffered solutions. Unfortunately, few coatings have been reported to present such level of performance when exposed to highly complex proteinaceous, real-world media such as blood serum and plasma, even diluted. Herein, we concisely review the surface chemistry developed to date to minimize fouling from these considerably more challenging blood-based fluids. Adsorption dynamics is also discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".