Novel ZnO hollow-nanocarriers containing paclitaxel targeting folate-receptors in a malignant pH-microenvironment for effective monitoring and promoting breast tumor regression
Bibliographic record
Abstract
Low pH in the tumor micromilieu is a recognized pathological feature of cancer. This attribute of cancerous cells has been targeted herein for the controlled release of chemotherapeutics at the tumour site, while sparing healthy tissues. To this end, pH-sensitive, hollow ZnO-nanocarriers loaded with paclitaxel were synthesized and their efficacy studied in breast cancer in vitro and in vivo. The nanocarriers were surface functionalized with folate using click-chemistry to improve targeted uptake by the malignant cells that over-express folate-receptors. The nanocarriers released ~75% of the paclitaxel payload within six hours in acidic pH, which was accompanied by switching of fluorescence from blue to green and a 10-fold increase in the fluorescence intensity. The fluorescence-switching phenomenon is due to structural collapse of the nanocarriers in the endolysosome. Energy dispersion X-ray mapping and whole animal fluorescent imaging studies were carried out to show that combined pH and folate-receptor targeting reduces off-target accumulation of the nanocarriers. Further, a dual cell-specific and pH-sensitive nanocarrier greatly improved the efficacy of paclitaxel to regress subcutaneous tumors in vivo. These nanocarriers could improve chemotherapy tolerance and increase anti-tumor efficacy, while also providing a novel diagnostic read-out through fluorescent switching that is proportional to drug release in malignant tissues.
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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) | 0.000 | 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".