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Record W4200297697 · doi:10.1093/ofid/ofab466.1212

1018. Bacterial Bioburden Characterization of Infected Diabetic Foot Ulcers in Hospitalized Patients in Association with Clinical Outcomes: Traditional Cultures vs. Molecular Sequencing Methods

2021· article· en· W4200297697 on OpenAlexaff
Hadar Mudrik‐Zohar, Shaqed Carasso, Tal Gefen, Anat Zalmanovich, Michal Katzir, Yaël C. Cohen, Yossi Paitan, Naama Geva‐Zatorsky, Michal Chowers

Bibliographic record

VenueOpen Forum Infectious Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsMedicineMetagenomicsDiabetic footMicrobiologyBacteroidesDiabetic foot ulcer16S ribosomal RNAStaphylococcus aureusInternal medicineStaphylococcusMicrobiomeAmputationBacteriaAnaerobic bacteriaDiabetes mellitusBiologySurgeryBioinformaticsGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Background Infected diabetic foot ulcers (IDFU) are a major complication of diabetes mellitus. These potentially limb-threatening ulcers are challenging to treat due to the impairment of wound healing in diabetic patients and the complex microbial environment characterizing these ulcers. Our aim was to analyze the microbiome of IDFU in association with clinical outcomes. Methods Wound biopsies from IDFU were obtained from hospitalized patients and were analyzed using traditional microbiology cultures, 16S rRNA sequencing and shotgun metagenomic sequencing. Patients’ characteristics, culture-based results and sequencing data were analyzed in association with clinical outcomes. Study Design Results 31 patients were enrolled. Significantly more anaerobic and Gram-negative bacteria were detected with sequencing methods compared to conventional cultures (59% and 76% were anaerobes according to 16SrRNA and metagenomic respectively vs. 26% in cultures, p=0.001, and 79%, 59% and 54% were Gram negative bacteria respectively, p< 0.001). Culture-based results showed that Staphylococcus aureus was more prevalent among patients who were conservatively treated (p=0.048). In metagenomic analysis the Bacteroides genus was more prevalent among patients who underwent toe amputation (p< 0.001). Analysis of metagenomic-based functional data showed that antibiotic resistance genes and genes related to biofilm production and to bacterial virulent factors were more prevalent in IDFU that resulted in toe amputation (p< 0.001). Occurrences and mean relative abundances of the most prevalent bacteria of IDFU Comparison between [A] traditional cultures, 16S rRNA sequencing and metagenomic sequencing results (genera level - 12 samples) [B] traditional cultures and metagenomic sequencing results (species level – 30 samples) [C] traditional cultures and 16S rRNA sequencing results (genera level - 30 samples) CUL – cultures; 16S - 16S rRNA sequencing; MTG – metagenomic sequencing Bacteroides genus association with toe amputation Bacteroides genera was more common among samples of patients who underwent toe amputation compared with samples of patients who were conservatively treated (p < 0.001). Species level analysis showed that Bacteroides fragilis and Bacteroides xylanisolvens predominated IDFU of patients who underwent toe amputation (p=0.04, p=0.002 respectively). No – conservative treatment; Yes – toe amputation. Functional genes differentiating patients who underwent toe amputation from conservatively treated Yellow stars – indicate genes that were associated with bacterial virulent factors, biofilm formation and resistant mechanisms – all were more prevalent in patients who underwent toe amputation (with p values<> Conclusion Molecular sequencing tools uncover the complex biodiversity of IDFU and emphasize the high prevalence of anaerobes and Gram-negative bacteria in these ulcers. Furthermore, sequencing results highlighted the possible association between certain genera, species, and bacterial functional genes to clinical outcomes Disclosures Yossi Paitan, PhD, Ilex Medical Ltd (Employee, Other Financial or Material Support, As of 01.01.2021 I am the Laboratories Manager of Ilex Labs)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.336
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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