Antibody Micropatterned Lubricant‐Infused Biosensors Enable Sub‐Picogram Immunofluorescence Detection of Interleukin 6 in Human Whole Plasma
Bibliographic record
Abstract
Abstract Recent studies have shown a correlation between elevated interleukin 6 (IL‐6) concentrations and the risk of respiratory failure in COVID‐19 patients. Therefore, detection of IL‐6 at low concentrations permits early diagnosis of worst‐case outcome in viral respiratory infections. Here, a versatile biointerface is presented that eliminates nonspecific adhesion and thus enables immunofluorescence detection of IL‐6 in whole human plasma or whole human blood during coagulation, down to a limit of detection of 0.5 pg mL−1. The sensitivity of the developed lubricant‐infused biosensor for immunofluorescence assays in detecting low molecular weight proteins such as IL‐6 is facilitated by i) producing a bioink in which the capture antibody is functionalized by an epoxy‐based silane for covalent linkage to the fluorosilanized surface and ii) suppressing nonspecific adhesion by patterning the developed bioink into a lubricant‐infused coating. The developed biosensor addresses one of the major challenges for biosensing in complex fluids, namely nonspecific adhesion, therefore paving the way for highly sensitive biosensing in complex fluids.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".