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Record W3012253759

Topical Oxygen Therapy Shifts Microbiome Dynamics in Chronic Diabetic Foot Ulcers.

2020· article· en· W3012253759 on OpenAlexaff
Paul Hunter, Elisa Greco, Karen Cross, Julie Perry

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMicrobiomeMedicineDiabetic footDiabetic foot ulcerWound healingAnaerobic exerciseChronic woundBioburdenWound careBiofilmFlora (microbiology)MicrobiologySurgeryBioinformaticsDiabetes mellitusBiologyPhysiologyBacteria
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Bacterial biofilm in wounds prevents healing by acting as a physical barrier to wound closure and hyperactivating local inflammatory processes, thus making its removal a high priority. The authors previously have shown that adding topical oxygen to standard wound care increased healing of Texas Grade II and III diabetic foot ulcers (DFUs), which they hypothesized was a result of alterations of the wound microbiome/biofilm. OBJECTIVE: This study aims to determine the mechanism of action of topical oxygen in DFUs by examining the diversity of bacterial genera present in DFUs treated with topical oxygen. MATERIALS AND METHODS: Six patients with chronic DFUs had their wounds swabbed weekly over an 8-week period of continuous topical oxygen treatment, and microbiome diversity was assessed by metagenomic 16S rDNA sequencing using a next-generation sequencing platform. RESULTS: The wound microbiome shifted toward a diverse flora dominated by aerobes and facultative anaerobes with oxygen therapy in 5 healed wounds. In contrast, anaerobic flora persisted in a single nonhealing ulcer in the present study cohort. CONCLUSIONS: Although the sample size was small, this study suggests topical oxygen therapy may have the ability to encourage the growth of aerobic members of the wound microbiome and be an effective alternative to antibiotics in this area.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.618

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 studies0.0000.000
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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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 designObservational
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

Citations15
Published2020
Admission routes1
Has abstractyes

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