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Record W4385981881 · doi:10.3389/fcimb.2023.1271026

Editorial: Women in biofilms vol. II

2023· editorial· en· W4385981881 on OpenAlexaboutno aff
Carolina H. Pohl, Ângela França

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2023
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsnot available
Fundersnot available
KeywordsBiofilmMicrobiologyBiologyBacteriaGenetics

Abstract

fetched live from OpenAlex

Following the success of the first Women in Biofilm Research Topic, it is important to provide an additional opportunity for women involved in various aspects of biofilm research to publish their work. Female authors in this Research Topic contribute to the 33% of female researchers in STEM subjects worldwide (UNESCO, 2021) and have made significant contributions to work on biofilms, ranging from the development of novel methodologies to novel antibiofilm agents.Biofilms are formed by many microbes including Archae, Bacteria (Penesyan et al. 2021) and microbes belonging to the Eukarya (Brake & Hisiotis, 2010). These multicellular structures play important roles in microbial ecology in hosts as well as the environment (Davey & O'Toole, 2000), with current estimates indicating that 80% of prokaryotes form biofilms (Penesyan et al. 2021). It is also true that biofilms often consist of more than one species, including members of different domains such as yeasts and bacteria, and Candida albicans and Streptococcus mutans (Pohl, 2022). This preferred mode of growth has many implications for the biology of the microbes, including their interaction with the abiotic environment (Brake & Hisiotis, 2010;Penesyan et al. 2021), the host (in the case of commensal or pathogenic microbes) (Vestby et al. 2020), as well as for antimicrobial resistance (Pierce et al., 2013;Bowler et al. 2020).Various models have been developed for the high throughput study of the growth, biology and inhibition of biofilms. Although the two most common approaches are the microplate method and the Calgary biofilm device, they do have certain limitations. The paper by Zaborskyte et al.provides a flexible and reusable model for biofilm formation. This 3D-printed FlexiPeg system was validated using Escherichia coli and Klebsiella pneumoniae biofilms and proved to be a simple, low cost and relevant model for the study of these bacterial biofilms.The interaction between C. albicans and S. mutans was studied further in the paper by Wu et al. who expanded on their previous work that showed that extracellular vesicles of S. mutans increase the ability of C. albicans to form biofilms (Wu et al. 2020). In this new study, they show that the vesicles also stimulate C. albicans carbohydrate metabolism and dentin demineralization, which may lead to increased caries formation. This was done using a range of biofilm models including several Gram-negative and Grampositive bacteria, as well as C. albicans. They showed that the more complex biofilm models are, the better they reflect real-life scenarios, producing biofilms with greater antiseptic tolerance although they also show greater variance. However, the most important finding relates to the use of antiseptics with low chlorine concentrations. They found that the observed antimicrobial action of these antiseptics is not due to inherent activity against microbes, but rather due to the rinsing effect obtained during application.One strategy explored during the search for new antibiofilm agents is drug repurposing and modification of existing drugs, for example non-steroidal anti-inflammatory drugs (NSAIDs) (Leão et al. 2020). This approach was adopted by Dumitrascu et al. who synthesized and characterized new carbazole derivatives based on the NSAID carprofen. They found that one of these derivatives could inhibit Gram-positive planktonic and biofilm growth and another was active against the Gram-negative Pseudomonas aeruginosa.This collection of articles echoes the sentiment expressed by Almeida and Bakaletz (2022) and presents additional examples of the excellent work performed by women in the study of biofilms of bacteria and yeasts.

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)0.006
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.040
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0060.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0040.001
Science and technology studies0.0040.003
Scholarly communication0.0100.006
Open science0.0040.003
Research integrity0.0170.015
Insufficient payload (model declined to judge)0.0400.028

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 head0.004
GPT teacher head0.206
Teacher spread0.202 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueFrontiers in Cellular and Infection MicrobiologySame topicBacterial biofilms and quorum sensingFrench-language works237,207