Small Gold Quantum Probes for Drug‐Free Cancer Theranostics
Bibliographic record
Abstract
Abstract Gold quantum dots (QDs) have emerged as a distinct approach for cancer theranostics by using labeled and functionalized chemotherapeutic‐drug to realize its detection and chemotherapeutic functions. However, the toxicity induced by chemical‐based synthesis, their nonspecific accumulation in healthy tissues and organs hamper its translation to clinical applications. QDs are functionalized/labeled to reduce their toxicity and helps the QDs' cellular‐uptake. In addition, labeling increases the overall hydrodynamic size of QDs, which limits the dispersion, impedes efficient renal clearance and causes the accumulation of QDs in organs. Therefore, the unique properties that arise from quantum‐size (less than 5nm) are lost. Reports on the effect of real quantum‐size on cell‐interaction are, to date, limited. In this work, the concept of a small‐sized self‐functionalized pristine bright quantum gold probe (BQGP) for cancer‐theranostics. To the best of authors' knowledge, it is the first time cell‐interaction of BQGPs has been investigated and the results suggest that it can be used for label‐free/drug‐free cancer quantum‐theranostics. The pristine BQGPs demonstrate cancer‐selective cell‐uptake, enabling dosage‐dependent fluorescent detection/differentiation of cancerous‐cells and cancer‐selective cytotoxicity. The fabricated BQGPs demonstrate fluorophore‐free illuminance at a broad‐range of excitation wavelengths and drug‐free cancer‐specific treatment. Moreover, the drug‐free BQGPs may find advantages in fighting drug‐resistant cancers.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".