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Polydopamine Biomaterials for Skin Regeneration

2022· review· en· W4281665291 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Biomaterials Science & Engineering · 2022
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRegeneration (biology)BiomaterialBioadhesiveBiocompatibilityWound healingExtracellular matrixCell adhesionNanotechnologyMaterials scienceSelf-healing hydrogelsBiomedical engineeringAdhesionChemistryCell biologyDrug deliveryBiochemistryPolymer chemistryBiologyMedicine

Abstract

fetched live from OpenAlex

Designing biomaterials capable of biomimicking wound healing and skin regeneration has been receiving increasing attention recently. Some biopolymers behave similarly to the extracellular matrix (ECM), supporting biointerfacial adhesion and intrinsic cellular interactions. Polydopamine (PDA) is a natural bioadhesive and bioactive polymer that endows high chemical versatility, making it an exciting candidate for a wide range of biomedical applications. Moreover, biomaterials based on PDA and its derivatives have near-infrared (NIR) absorption, excellent biocompatibility, intrinsic antioxidative activity, antibacterial activity, and cell affinity. PDA can regulate cell behavior by controlling signal transduction pathways. It governs the focal adhesion behavior of cells at the biomaterials interface. These features make melanin-like PDA a fascinating biomaterial for wound healing and skin regeneration. This paper overviews PDA-based biomaterials' synthesis, properties, and interactions with biological entities. Furthermore, the utilization of PDA nano- and microstructures as a constituent of wound-dressing formulations is highlighted.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.778
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.373
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it