Dextran Functionalization of Semiconducting Polymer Dots and Conjugation with Tetrameric Antibody Complexes for Bioanalysis and Imaging
Bibliographic record
Abstract
Brightly fluorescent semiconducting polymer dots (Pdots) are emerging as very useful probes for bioanalysis and imaging. Unfortunately, Pdot materials often suffer from limitations such as poor colloidal and physical stability, nonspecific adsorption, and relatively few reported surface chemistries and bioconjugate chemistries. To help address these limitations, we have developed dextran-functionalized Pdots (Dex-Pdots). This functionalization improves particle stability over a range of pH and at high ionic strength, hinders surface-induced unfolding, and enables the preparation of immunoconjugates via tetrameric antibody complexes (TAC). The utility of TAC-conjugated Dex-Pdots is demonstrated through a proof-of-concept fluorescence-linked immunosorbent assay (FLISA) for human erythropoietin (EPO), and through immunolabeling of human epidermal growth factor receptor 2 (HER2)-positive SK-BR3 breast cancer cells. The conjugates exhibited less nonspecific binding and greater specific binding than Pdots without dextran functionalization. Overall, dextran functionalization is a highly promising surface chemistry for biological applications of Pdots.
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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) | 0.000 | 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".