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Record W4400889181 · doi:10.1007/s11033-024-09750-9

Extracellular matrix-inspired biomaterials for wound healing

2024· review· en· W4400889181 on OpenAlexaff
Louise Hosty, Thomas Heatherington, Fabio Quondamatteo, S. Browne

Bibliographic record

VenueMolecular Biology Reports · 2024
Typereview
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsTrinity College
FundersRoyal College of Surgeons in IrelandAnatomical Society
KeywordsExtracellular matrixWound healingCell biologyChemistryExtracellularMatrix (chemical analysis)BiologyImmunology

Abstract

fetched live from OpenAlex

Diabetic foot ulcers (DFU) are a debilitating and life-threatening complication of Diabetes Mellitus. Ulceration develops from a combination of associated diabetic complications, including neuropathy, circulatory dysfunction, and repetitive trauma, and they affect approximately 19-34% of patients as a result. The severity and chronic nature of diabetic foot ulcers stems from the disruption to normal wound healing, as a result of the molecular mechanisms which underly diabetic pathophysiology. The current standard-of-care is clinically insufficient to promote healing for many DFU patients, resulting in a high frequency of recurrence and limb amputations. Biomaterial dressings, and in particular those derived from the extracellular matrix (ECM), have emerged as a promising approach for the treatment of DFU. By providing a template for cell infiltration and skin regeneration, ECM-derived biomaterials offer great hope as a treatment for DFU. A range of approaches exist for the development of ECM-derived biomaterials, including the use of purified ECM components, decellularisation and processing of donor/ animal tissues, or the use of in vitro-deposited ECM. This review discusses the development and assessment of ECM-derived biomaterials for the treatment of chronic wounds, as well as the mechanisms of action through which ECM-derived biomaterials stimulate wound healing.

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 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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.029
GPT teacher head0.383
Teacher spread0.354 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations47
Published2024
Admission routes1
Has abstractyes

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