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Record W4308773339 · doi:10.3390/ph15111382

Alginate–Chitosan Hydrogel Formulations Sustain Baculovirus Delivery and VEGFA Expression Which Promotes Angiogenesis for Wound Dressing Applications

2022· article· en· W4308773339 on OpenAlex
Sabrina Schaly, Paromita Islam, Ahmed Abosalha, Jacqueline L. Boyajian, Dominique Shum‐Tim, Satya Prakash

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

VenuePharmaceuticals · 2022
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMcGill University Health CentreRoyal Victoria Regional Health CentreMcGill University
Fundersnot available
KeywordsSelf-healing hydrogelsChitosanWound healingAngiogenesisHemostasisVascular endothelial growth factor ABiomedical engineeringChemistryMedicineVEGF receptorsVascular endothelial growth factorCancer researchImmunologySurgeryBiochemistry

Abstract

fetched live from OpenAlex

Hydrogel wound dressings are effective in their ability to provide a wound-healing environment but are limited by their ability to promote later stages of revascularization. Here, a biosafe recombinant baculovirus expressing VEGFA tagged with EGFP is encapsulated in chitosan-coated alginate hydrogels using ionic cross-linking. The VEGFA, delivered by the baculovirus, significantly improves cell migration and angiogenesis to assist with the wound-healing process and revascularization. Moreover, the hydrogels have an encapsulation efficiency of 99.9%, no cytotoxicity, antimicrobial properties, good blood compatibility, promote hemostasis, and enable sustained delivery of baculoviruses over eight days. These hydrogels sustain baculovirus delivery and may have clinical implications in wound dressings or future gene therapy applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.054
GPT teacher head0.380
Teacher spread0.325 · 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