A survey of state-of-the-art surface chemistries to minimize fouling from human and animal biofluids
Bibliographic record
Abstract
Upon contact with bodily fluids, synthetic materials spontaneously acquire a layer of various species (most notably proteins) on their surface. The concern with respect to biomedical equipment, implants or devices resides in the possibility for biological processes with potentially harmful effects to ensue. In biosensor technology, the issue with this natural fouling phenomenon is that of non-specific adsorption to sensing platforms, which generates an often overwhelming interference signal that prevents the detection, not to mention the quantification, of target analytes present at considerably lower concentration. To alleviate this ubiquitous, recurrent problem - this genuine biotechnological plague - considerable research efforts have been devoted over the last few decades to engineer antifouling coatings. Extensive literature now exists that describes stealth organic adlayers capable of reducing fouling surface coverage Γ down to a few ng cm(-2)- however from biotechnologically irrelevant buffered solutions free or nearly depleted of any potentially interfering species. Regrettably indeed, few coatings are known to display/retain such level of performance when exposed to otherwise more complex, real-life biosamples (even diluted). Herein, we comprehensively review the state-of-the-art surface chemistries developed to date (January 2015) to minimize fouling from 8 such uncomparatively more challenging biological media (blood plasma, blood serum, cell lysate, cerebrospinal fluid, egg, milk, saliva, and urine) - whether of human or animal origin. Literature search for another 25 biological milieux generated no (exploitable) hit. Also discussed in this Review are the identification of the species responsible for fouling, and the dependence of antifouling properties on biosample source variability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".