Cross-reactivities in conjugation reactions involving iron oxide nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The preparation of multimodal nanoparticles by capping magnetic iron oxide nanoparticles (IONPs) with functional organic molecules is a major area of research for biomedical applications. Conjugation reactions, such as carbodiimide coupling and the highly selective class of reactions known as "click chemistry", have been instrumental in tailoring the ligand layers of IONPs to produce functional biomedical nanomaterials. However, few studies report the controls performed to determine if the loading of molecules onto IONPs is due to the proposed coupling reaction(s) employed, or some other unknown interaction with the IONP surface. Herein, we use 3,4-dihydroxybenzoic acid-functionalized IONPs (IONP-3,4-DHBA) as a platform upon which carbodiimide coupling can be used to conjugate clickable small molecules for further functionalization using two common click reactions, namely, the copper-catalyzed azide-alkyne cycloaddition (CuAAC), and the thiol-maleimide Michael addition reactions. Through the judicious use of controls, we demonstrate significant cross-reactivities of amines, thiols, maleimides, and common disulfide reducing agents with surface Fe of IONPs and show how these unwanted interactions can produce false positive results. Without proper controls, these can lead to erroneous conclusions about the efficacy of conjugation reactions, which can have detrimental impacts on the functionality and safety of IONPs in biomedical applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it