Modulating the interaction of graphenic substrates with human interleukin-6 and its monoclonal antibody: a study by Raman images
Bibliographic record
Abstract
Interleukin-6 (IL-6) is a cytokine with wide-ranging biological effects, playing an important role on the immune system and inflammatory responses. Therefore, it is important to develop alternative, highly sensitive and reliable analytical methodologies for the accurate detection of this biomarker in biological fluids. Graphene substrates (GS), such as pristine graphene (G), graphene oxide (GO), and reduced graphene oxide (rGO), have shown great benefits for biosensing and in the development of novel biosensor devices. In this work, we present a proof of concept for the development of a new analytical platform for the specific recognition of human interleukin-6, that is based on the coffee-ring formation of monoclonal antibodies of interleukin-6 (mabIL-6) onto amine functionalized GS. The prepared GS/mabIL-6/IL-6 systems were successfully used to show that IL-6 was specifically and selectively adsorbed onto the area of the mabIL-6 coffee-ring. Raman imaging was confirmed as a versatile tool to investigate different antigen-antibody interactions and their surface distribution. This experimental approach can be used to develop a wide variety of substrates for antigen-antibody interaction allowing the specific detection of an analyte in a complex matrix.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".