Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,004 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,017 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,028 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».