Bibliographic record
Abstract
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.040 | 0.028 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".