Carboxymethyl Hyaluronan-Stabilized Nanoparticles for Anticancer Drug Delivery
Bibliographic record
Abstract
Carboxymethyl hyaluronic acid (CMHA) is a semisynthetic derivative of HA that is recognized by HA binding proteins but contains an additional carboxylic acid on some of the 6-hydroxyl groups of the N-acetyl glucosamine sugar units. These studies tested the ability of CMHA to stabilize the formation of calcium phosphate nanoparticles and evaluated their potential to target therapy resistant, CD44(+)/CD24(-/low) human breast cancer cells (BT-474EMT). CMHA stabilized particles (nCaP(CMHA)) were loaded with the chemotherapy drug cis-diamminedichloroplatinum(II) (CDDP) to form nCaP(CMHA)CDDP. nCaP(CMHA)CDDP was determined to be poorly crystalline hydroxyapatite, 200 nm in diameter with a -43 mV zeta potential. nCaP(CMHA)CDDP exhibited a two-day burst release of CDDP that tapered resulting in 86% release by 7 days. Surface plasmon resonance showed that nCaP(CMHA)CDDP binds to CD44, but less effectively than CMHA or hyaluronan. nCaP(CMHA-AF488) was taken up by CD44(+)/CD24(-) BT-474EMT breast cancer cells within 18 hours. nCaP(CMHA)CDDP was as cytotoxic as free CDDP against the BT-474EMT cells. Subcutaneous BT-474EMT tumors were more reproducibly inhibited by a near tumor dose of 2.8 mg/kg CDDP than a 7 mg/kg dose nCaP(CMHA)CDDP. This was likely due to a lack of distribution of nCaP(CMHA)CDDP throughout the dense tumor tissue that limited drug diffusion.
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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".