Electrospun polycaprolactone membranes functionalized with nanochitin for enhanced bioactivity in localized cancer photodynamic therapy
Bibliographic record
Abstract
ABSTRACT The encapsulation of photosensitizers (PSs) in electrospun membranes has emerged as a promising approach in photodynamic therapy (PDT) on tumor sites, overcoming the drawbacks associated with systemic administration. In this work, localized implants for cancer treatment using PDT were developed by incorporating EL-2 squaraine into poly(ɛ-caprolactone) (PCL) electrospun microfibers. The latter were coated with chitin nanocrystals (ChNC) by electrospraying, which may improve the biocompatibility and bioactivity of the developed membranes, potentially enhancing the clinical outcomes. The developed electrospun membranes were characterized by water contact angle, imaging, and spectroscopy techniques. The uniform encapsulation and distribution of EL-2 within the microfibers were confirmed while ChNC endowed the membranes with surface hydrophilicity. EL-2 alone displayed about 20 times more cytotoxicity after irradiation compared to the dark condition against HeLa cervical carcinoma cells. Meanwhile, the photodynamic action of PCL+EL-2/ChNC membranes promoted a significant inhibition of cancer cells’ proliferation under irradiation, achieving 66.25% of inhibition, compared to only 24.78% in dark conditions, using the highest concentration of EL-2. Overall, this work introduces a disruptive strategy using electrospinning-electrospraying to design fibrous therapeutic platforms for cancer PDT, taking advantage of electrospun fibers unique features and the localized nature of photodynamic therapy.
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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".