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Record W3124042321 · doi:10.1016/j.ijid.2020.09.113

Resistance profile and minimum inhibitory concentration versus minimum biofilm inhibitory concentration of biofilm positive Staphylococci

2020· article· en· W3124042321 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Infectious Diseases · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolism and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBiofilmMicrobiologyMinimum inhibitory concentrationBroth microdilutionAntibioticsVancomycinCefoxitinAgarAntibiotic resistanceBiologyStaphylococcus epidermidisStaphylococcus aureusBacteria

Abstract

fetched live from OpenAlex

Background: Staphylococcal spp. are recognized as one of the most frequent causes of biofilm associated infections. In routine microbiology laboratories, standard method for testing antibiotic susceptibility is Kirby-Bauer disc diffusion method and minimum inhibitory concentration (MIC) which determine the antibiotic susceptibilities in planktonic phase. However, isolates capable of producing biofilm exist in biofilm state. Thus,antibiotics prescribed based on routine methods fail to eradicate biofilms thereby causing persistent infection and possible treatment failure. Thus, this work was designed to study prevalence of biofilm producing staphylococci, its resistance profile and to perform MIC versus MBIC (Minimum biofilm inhibitory concentration) in representative biofilm positive isolates. Methods and materials: 335 clinical isolates of staphylococci included in the study were screened for biofilm formation phenotypically by Congo red agar (CRA) and Tissue culture plate method (TCPM) followed by detection of biofilm genes (icaAD,aap,atlE) by PCR. Antibiotic susceptibility testing was done for biofilm positive isolates. MIC versus MBIC testing was performed for representative biofilm positive isolates for cefoxitin and vancomycin. MIC was done by broth microdilution and MBIC testing was performed using Calgary Biofilm Device. Results were interpreted according to CLSI guidelines,2015. Results: 77/335 (30%) staphylococcal isolates were biofilm positive of which S.haemolyticus (n = 35, 45.5%) was predominant followed by S. aureus (n = 27, 35.1%) and S.epidermidis (n = 15, 19.5%). By CRA, 22 (28.6%) isolates produced black colonies after 24 hours. By TCPM, 4 (5.2%), 1 (1.3%) & 49 (63.6%) isolates showed strong, moderate and weak biofilm production respectively. Overall, 77 isolates harboured biofilm genes (47aap,15atlE,15aap+atlE,5icaAD+aap+atlE).43/77 (55.8%)isolates were methicillin resistant. Highest resistance was found towards ciprofloxacin (74%) followed by erythromycin (70.1%),cotrimoxazole (65%) and pristinamycin (65%) respectively. MIC and MBIC were compared for 25 representative biofilm positive isolates. Cefoxitin MIC and MBIC ranged between 2–1024 μg/mL and 8–2048 μg/mL respectively. Vancomycin MIC and MBIC ranged between 0.5–8 μg/mL and 2–512 μg/mL respectively. Both cefoxitin and vancomycin MBIC values were 2–64 folds higher than their respective MIC. Conclusion: The present study documents biofilm producing capability in 30% of isolates. The antibiotic sensitivities of organisms in planktonic phase tested by MIC were significantly higher than for the same organism in their biofilm state as tested by MBIC. The results of the study questions the use of vancomycin as last resort antibiotic for biofilm-associated staphylococcal infections.

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 score0.017
Threshold uncertainty score0.532

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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