Molecularly Imprinted Nanobeacons Redirect Innate Immune Killing towards Triple Negative Breast Cancer
Bibliographic record
Abstract
Harnessing innate immunity is an appealing strategy for cancer treatment. Herein, we report a new strategy called molecularly imprinted nanobeacons (MINBs) for redirecting innate immune killing towards triple-negative breast cancer (TNBC). The MINBs were molecularly imprinted nanoparticles with the N-epitope of glycoprotein nonmetastatic B (GPNMB) as the template and grafted with plentiful fluorescein moieties as the hapten. The MINBs could tag the TNBC cells via binding with GPNMB and thereby provide navigation for recruiting hapten-specific antibodies. The gathered antibodies could further trigger effective Fc-domain-mediated immune killing towards the tagged cancer cells. In vivo experiments showed that the TNBC growth was significantly inhibited after MINBs treatment by intravenous injection as compared with control groups. This study not only opens a new access for redirecting innate immunity towards TNBC but also paves the way for innate immunity-based therapy of other diseases.
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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".