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Record W1966902810 · doi:10.4137/mbi.s13914

Antibiofilm Efficacy of DispersinB<sup>®</sup> Wound Spray Used in Combination with a Silver Wound Dressing

2014· article· en· W1966902810 on OpenAlexaff
Purushottam V. Gawande, Allie Clinton, Karen LoVetri, Nandadeva Yakandawala, Kendra P. Rumbaugh, Srinivasa Madhyastha

Bibliographic record

VenueMicrobiology Insights · 2014
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsKane Biotech (Canada)
Fundersnot available
KeywordsAntimicrobialBiofilmMedicineStaphylococcus aureusChronic woundWound healingMicrobiologyBacteriaSurgeryBiology

Abstract

fetched live from OpenAlex

Chronic wounds including diabetic foot ulcers, pressure ulcers, and venous leg ulcers are a worldwide health problem. As the traditional methods of treatment have proven ineffective against chronic wounds involving biofilms, there is an unmet clinical need for developing products with an antibiofilm component that inhibits and/or disrupts biofilms and thus make the biofilm-embedded bacteria more susceptible to antimicrobial therapy. We developed a DispersinB® antibiofilm enzyme-based wound spray for treating chronic wounds in conjunction with an antimicrobial. Under in vitro conditions, the DispersinB® and Acticoat™ combination performed significantly better (P < 0.05) than Acticoat™ alone, indicating the synergy between the two compounds because of DispersinB® enhancing the antimicrobial activity of Acticoat™. Furthermore, DispersinB® wound spray enhanced the antimicrobial activity of Acticoat™ in a chronic wound mouse model of methicillin-resistant Staphylococcus aureus (MRSA) infection. Thus, this novel combination of DispersinB® and Acticoat™, an antimicrobial dressing, prompts clinical evaluation for potential applications in biofilm-based chronic wound management.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations50
Published2014
Admission routes1
Has abstractyes

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