PLLA-PEG-TCH-labeled bioactive molecule nanofibers for tissue engineering
Bibliographic record
Abstract
Jun Chen1,2, Beth Zhou1–3, Qi Li1,2, Jun Ouyang4, Jiming Kong2,4,5, Wen Zhong3,6, Malcolm MQ Xing1,2,4,71Department of Mechanical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada; 2Manitoba Institute of Child Health, Winnipeg, MB, Canada; 3Department of Textile Sciences, Faculty of Human Ecology, University of Manitoba, Winnipeg, MB, Canada; 4School of Basic Medical Science, Southern Medical University, Guangzhoug, China; 5Department of Human Anatomy and Cell Sciences, 6Department of Medical Microbiology, Faculty of Medicine, 7Department of Biochemistry and Medical Genetics, Faculty of Medicine, University of Manitoba, Winnipeg, MB, CanadaAbstract: By mimicking the native extracellular matrix, electrospun nanofibrous scaffolds (ENSs) can provide both chemical and physical cues to modulate cell adherence and differentiation and to promote tissue regeneration while retaining bioresorbable and biocompatible properties. In this study, ENSs were developed to deliver multiple biomolecules by loading them into the core-sheath structure and/or by conjugating them to the nanofiber surfaces. In this work, poly(L-lactide)-poly(ethylene glycol)-NH2 and poly(L-lactide) were emulsion electrospun into nanofibers with a core-sheath structure. A model drug, tetracycline hydrochloride, was loaded within the nanofibers. Amino and carboxyl reactive groups were then activated on the fiber surfaces using saturated water vapor exposure and base hydrolysis, respectively. These reactive groups allowed the surface of the ENS to be functionalized with two other bioactive molecules, fluorescein isothiocyanate- and rhodamine-labeled bovine serum albumins, which were used as model proteins. The ENSs were shown to retain their antimicrobial capacity after two functionalization reactions, indicating that multifunctional nanofibers can potentially be developed into functional wound dressings or periodontal membranes or used in more complicated tissue systems where multiple growth factors and anti-infection precautions are critical for the successful implantation and regeneration of tissues.Keywords: multifunctional nanofibrous scaffold, drug delivery, antibiotics
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.
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".