A highly sensitive luminescent lectin sensor based on an α-d-mannose substituted Tb3+ antenna complex
Bibliographic record
Abstract
Lectin-carbohydrate interactions are the basis of many biological processes and essentially they constitute the language through which intercellular communications are codified. Thus they represent powerful tools in the examination and interpretation of changes that occur on cell surfaces during both physiological and, more importantly, pathological events. The development of optical techniques that exploit the unique properties of luminescent lanthanoid metal complexes in the investigation of lectin-carbohydrate recognition can foster research in the field of ratiometric biosensing and disease detection. Here we report the synthesis of a Tb(3+)-DO3A complex (Tb⊂1) bearing an α-D-mannose residue and the related study of binding affinity with concanavalin A (Con A) labeled with rhodamine-B-isothiocyanate (RITC-Con A). Luminescence spectroscopy and dynamic studies show changes in emission spectra that can be ascribed to a luminescence resonance energy transfer (LRET) from Tb⊂1 (donor) to RITC-Con A (acceptor). The binding constant value between the two species was found to be one order of magnitude larger than those previously reported for similar types of recognition. To the best of our knowledge this is the first example of the use of a pre-organized luminescent lanthanoid complex in the study of carbohydrate-protein interactions by LRET.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".