Antibiofilm Activity of LL-37 Peptide and D-Amino Acids Associated with Antibiotics Used in Regenerative Endodontics on an Ex Vivo Multispecies Biofilm Model

The antimicrobial peptide LL-37 and D-amino acids (D-AAs) have been proposed as antibiofilm agents. Therefore, this study aimed to test the antimicrobial effect of antibiofilm agents associated with antibiotics used in regenerative endodontic procedures (the triple antibiotic paste-TAP: ciprofloxacin + metronidazole + minocycline). An endodontic-like biofilm model grown on bovine dentin discs was used in this study. After 21-day growth, the biofilms were treated with 1 mg/mL TAP, 10 μM LL-37, an association of LL-37 + TAP, 40 mM D-AAs solution, an association of D-AAs + TAP, and phosphate-buffered saline (negative control). Colony forming unit (CFU) data were analyzed by two-way ANOVA and Tukey's multiple comparison test (p < 0.05). LL-37 + TAP showed the best antibacterial activity (7-log10 CFU/mL ± 0.5), reaching a 1 log reduction of cells in relation to the negative control (8-log10 CFU/mL ± 0.7) (p < 0.05). In turn, no significant reduction in bacterial cells was observed with TAP, LL-37, D-AAs, and D-AAs + TAP compared to the negative control. In conclusion, the combination of antibiotics and LL-37 peptide showed mild antibacterial activity, while the combination of antibiotics and D-AAs showed no activity against complex biofilms. Keywords: D-amino acids; antibiofilm agents; antimicrobial peptides; oral biofilms; regenerative endodontics; triple antibiotic past

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Proposed validation framework for WSUD stormwater harvesting systems.

<p>Proposed validation framework for WSUD stormwater harvesting systems.</p

5^th, 50^th and 95^th percentile concentrations of micropollutant interpolated from literature and the corresponding drinking water guidelines: Australia [33]; USEPA [35], WHO [34] and EU[36].

<p>5^th, 50^th and 95^th percentile concentrations of micropollutant interpolated from literature and the corresponding drinking water guidelines: Australia [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125979#pone.0125979.ref033" target="_blank">33</a>]; USEPA [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125979#pone.0125979.ref035" target="_blank">35</a>], WHO [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125979#pone.0125979.ref034" target="_blank">34</a>] and EU[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125979#pone.0125979.ref036" target="_blank">36</a>].</p