Amphiphilic Thermoresponsive Triblock PLA-PEG-PLA and Diblock mPEG-PLA Copolymers for Controlled Deferoxamine Delivery
Bibliographic record
Abstract
This study focuses on the synthesis and characterization of thermoresponsive hydrogels of poly(lactic acid) (PLA) and poly(ethylene glycol) (PEG), PLA–PEG copolymers, aiming at the targeted and controlled release of deferoxamine (DFO), a clinically applied iron-chelating drug. Triblock (PLA-PEG-PLA) and diblock (mPEG-PLA) copolymers were synthesized using ring-opening polymerization (ROP) with five different PEGs with molecular weights of 1000, 1500, 2000, 4000, and 6000 g/mol and two types of lactide (L-lactide and D-lactide). Emulsions of the polymers in phosphate-buffered saline (PBS) were prepared at concentrations ranging from 10% to 50% w/w to study the sol–gel transition properties of the copolymers. Amongst the synthesized copolymers, only those that demonstrated thermoresponsive sol-to-gel transitions near physiological temperature (37 °C) were selected for further analysis. Structural and molecular confirmation was performed by Nuclear Magnetic Resonance (NMR) and Fourier-transform infrared spectroscopy (FTIR), while the molecular weights were determined via Gel Permeation Chromatography (GPC). The thermal transitions were studied by calorimetry (DSC) and crystallinity via X-ray diffraction (XRD) analysis. DFO-loaded hydrogels were prepared, and their drug release profiles were investigated under simulated physiological conditions (37 °C) for seven days using HPLC analysis. The thermoresponsive characteristics of these systems can offer a promising strategy for injectable drug delivery applications, where micelles serve as drug carriers and undergo in situ gelation, enabling controlled release. This alternative procedure may significantly improve the bioavailability of DFO and enhance patient compliance by addressing key limitations of conventional administration routes.
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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".