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Record W4221052134 · doi:10.1038/s41598-022-07953-3

Assessment of persistent antimicrobial and anti-biofilm activity of p-HEMA hydrogel loaded with rifampicin and cefixime

2022· article· en· W4221052134 on OpenAlexfundno aff
Ola Tarawneh, Hadeel Abu Mahfouz, Lama Hamadneh, Ahmad A. Deeb, Iyad Al-Sheikh, Wasan Alwahsh, Abdulqader Fadhil Abed

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsnot available
FundersAl-Zaytoonah University of JordanUniversity of JordanQueen's University
KeywordsBiofilmAntimicrobialRifampicinCefiximeMedicineMicrobiologyAntibioticsBiologyBacteriaCephalosporin

Abstract

fetched live from OpenAlex

Catheter-associated urinary tract infections (CAUTIs) are nosocomial infections causing more than one million hospital cases annually. The progress of CAUTIs leads to severe health complications. Infections result in blockage of the medical device due to biofilm formation, which necessitates the replacement of the device. The objective of this study is to improve urological biomaterials to minimize microbial growth and reduce the incidence of CAUTIs. Challenges from mixed biofilm are crucial and need to be addressed in the development of new coating materials. Herein, an investigation highlighted the reduction of mixed biofilm overgrowth and attachment tendency on poly-2-hydroxyethyl methacrylate (p-HEMA) surface by loading the hydrogel with rifampicin (RIF), cefixime trihydrate (CFX), and combined ratios of RIF and CFX. Mixed biofilm-formation ability in (3:1) RIF: CFX-loading p-HEMA (F6) surface showed best tendency to resist form biofilm. Persistent antimicrobial activity increased in p-HEMA loaded with combined ratios of RIF and CFX surface compared to p-HEMA alone, antimicrobial activity lasted for 8 days. All fabricated films exhibited %cell viability higher than 75% on HEK 293 cells. The addition of RIF and CFX may improve the duration of urological device employment before replacement.

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)0.001
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 score0.315
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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

Citations34
Published2022
Admission routes1
Has abstractyes

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