The Interaction of Cationic Polymers and Their Bisphosphonate Derivatives with Hydroxyapatite
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
Conjugating proteins with bisphosphonates (BPs), a class of molecules with exceptional affinity to hydroxyapatite (HA), is a feasible means to impart bone affinity to protein-based therapeutic agents. To increase the targeting effectiveness while minimizing protein modification, a polymeric linker containing multiple copies of BPs could be constructed for protein conjugation and targeting to bone. Towards this goal, poly(L-lysine) (PLL) and poly(ethylenimine) (PEI) were utilized as the polymeric backbones to incorporate a BP, namely 2-(3-mercaptopropylsulfanyl)-ethyl-1,1-bisphosphonic acid (thiolBP), by using N-hydroxysuccinimidyl polyethylene glycol maleimide and succinimidyl-4-(N-maleimidomethyl)-cyclohexane-1-carboxylate, respectively. In vitro and in vivo mineral affinity of the polymer-BP conjugates were determined in comparison with the unmodified polymers. The in vitro results indicated strong binding of the cationic polymers to HA in their unmodified form. BP conjugation did not enhance the inherent mineral affinity of the polymers; in contrast, certain modifications negatively affected the polymers' binding to the HA. In vivo results from a subcutaneous implant model in rats also showed no significant difference in mineral affinity of the BP modified and unmodified PEI. We conclude that thiolBP conjugation to the cationic polymers PLL and PEI was not beneficial for increasing the mineral affinity of the polymeric molecules. The strong interaction between the cationic polymers and HA may make the polymers suitable for imparting mineral affinity to bone-acting therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it