Color stability evaluation of micro hybrid composite resins submitted to accelerated artificial aging

Objective: The aesthetics of dental materials is extremely important for the success of oral rehabilitation. Thus, in the present study we evaluated the color stability and the surface degradation of three micro hybrid composite resins after accelerated artificial aging process (AAA). Methods: Were prepared 24 specimens (n=8) for each material: Solidex, Artglass and Cesead, dimensions of Ø 15 mm by 2 mm in thickness. The samples were subjected to color analysis, before and after AAA, in a spectrophotometer according to the CIE L*a*b* parameters, and a sample of each material, was selected for morphological evaluation under scanning electron microscopy (SEM). The data were submitted to one-way ANOVA and Tukey test (α=0.05). Results: Artglass showed higher stability regarding the presence of red and yellow (p<0.05) when subjected to the AAA and fewer of these pigments (p<0.05) when compared to the Cesead and Solidex, which showed the highest luminance stability (p<0.05). ΔE Cesead was the most unstable (p<0.05). All resins analyzed by SEM showed superficial degradation when submitted to the AAA, mainly in resin Cesead. Conclusion: All materials analyzed demonstrate color change and surface degradation, Cesead resin showed the worse results

Analysis of physico-chemical characteristics of two surface treatments in dental mini-implants

Objetivos: A otimização das superfícies de titânio é essencial para acelerar o processo de osseointegração e viabilizar tratamentos reabilitadores em um curto período de tempo. O objetivo deste estudo foi avaliar comparativamente dois tratamentos de superfície do tipo nanométrico em mini-implantes dentários, sendo um comercial e outro experimental, quanto à caracterização morfológica e química. Materiais e métodos: Dois grupos foram avaliados: G1 – mini-implan­tes de titânio com tratamento de superfície Ossean® (Intra-Lock); e G2 – mini-implantes de titânio com tratamento de superfície experimental. Após o tratamento experimental, por meio de ataque com ácido fosfórico seguido de tratamen­to alcalino, os implantes foram avaliados por microscopia eletrônica de varredura (MEV), em dois modos diferentes: SE (elétron secundário), que analisa as alterações topográficas nas amostras, e BSE (elétron retroespalhado), que analisa as alterações ou flutuações de composição na superfície da amostra. A composição química foi verificada por um sistema de espectroscopia de energia dispersiva de raios X (EDS). Resultados: As imagens de MEV confirmaram diferenças nas superfícies G1 e G2, com presença de poros nanométricos no G2, enquanto a análise de EDS demonstrou a incorporação de elementos característicos da estimulação da neoformação óssea. Conclusões: O tratamento de superfície experimen­tal, por ser um processo químico, além de simples, foi eficaz na formação de uma superfície rugosa e com capacidade bioativa. DESCRITORES | Implantes dentais; Microscopia eletrônica de varredura; Osseointegração.Objectives: The optimiza­tion of titanium surfaces is essential to accelerate the process of osseointegration and to enable rehabilitative treatments in a short period of time. The objective of the present study was to evaluate comparatively two surface treatments of the nanometric type in dental mini-implants, being a commercial and another experimental, regarding the morphological and chemical characterization. Material and methods: Two groups were evaluated: G1 – titanium mini-implants with surface treatment Ossean® (Intra-Lock) and G2 – titanium mini-implants with experimental surface treatment. After the experimental treatment, by means of an attack with phosphoric acid followed by alkaline treatment, the implants were evaluated by scanning electron microscopy (SEM), in two different ways, SE (secondary electron) that analyzes the topographic changes in the samples and BSE (back-scattered electrons) that analyzes the composition changes or fluctuations in the sample surface. The chemical composition was analyzed by an X-ray dispersive energy spectroscopy (EDX) system. Results: SEM images confirmed differences in G1 and G2 surfaces, with the presence of nanometric pores in G2, whereas EDX analysis demonstrated the incorporation of elements characteristic of the stimulation of bone neoformation. Conclusions: The experimental surface treatment, as a chemical process, besides being simple, was effective in the formation of a rough surface and with bioactive capacity. DESCRIPTORS | Dental implants; Scanning electron microscopy; Osseointegration

In vitro analysis of the influence of surface treatment of dental implants on primary stability

Surface treatment interferes with the primary stability of dental implants because it promotes a chemical and micromorphological change on the surface and thus stimulates osseointegration. This study aimed to evaluate the effects of different surface treatments on primary stability by analyzing insertion torque (IT) and pullout force (PF). Eight samples of implants with different surface treatments (TS - external hexagon with acid surface treatment; and MS - external hexagon, machined surface), all 3.75 mm in diameter x 11.5 mm in length, were inserted into segments of artificial bones. The IT of each sample was measured by an electronic torquemeter, and then the pullout test was done with a universal testing machine. The results were subjected to ANOVA (p < 0.05), followed by Tukey's test (p < 0.05). The IT results showed no statistically significant difference, since the sizes of the implants used were very similar, and the bone used was not highly resistant. The PF values (N) were, respectively, TS = 403.75 +/- 189.80 and MS = 276.38 +/- 110.05. The implants were shown to be different in terms of the variables of maximum force (F = 4.401, p = 0.0120), elasticity in maximum flexion (F = 3.672, p = 0.024), and relative stiffness (F = 4.60, p = 0.01). In this study, external hexagonal implants with acid surface treatment showed the highest values of pullout strength and better stability, which provide greater indication for their use.National Council for Scientific and Technological Development [149531/2010-9]National Council for Scientific and Technological Developmen

Development of a novel resin with antimicrobial properties for dental application

The adhesion of biofilm on dental prostheses is a prerequisite for the occurrence of oral diseases. Objective: To assess the antimicrobial activity and the mechanical properties of an acrylic resin embedded with nanostructured silver vanadate (β-AgVO3). Material and Methods: The minimum inhibitory concentration (MIC) of β-AgVO3 was studied in relation to the species Staphylococcus aureus ATCC 25923, Streptococcus mutans ATCC 25175, Pseudomonas aeruginosa ATCC 27853, and Candida albicans ATCC 10231. The halo zone of inhibition method was performed in triplicate to determine the inhibitory effect of the modified self-curing acrylic resin Dencor Lay - Clássico®. The surface hardness and compressive strength were examined. The specimens were prepared according to the percentage of β-AgVO3 (0%-control, 0.5%, 1%, 2.5%, 5%, and 10%), with a sample size of 9x2 mm for surface hardness and antimicrobial activity tests, and 8x4 mm for the compression test. The values of the microbiologic analysis were compared and evaluated using the Kruskal-Wallis test (α=0.05); the mechanical analysis used the Shapiro-Wilk's tests, Levene's test, ANOVA (one-way), and Tukey's test (α=0.05). Results: The addition of 10% β-AgVO3 promoted antimicrobial activity against all strains. The antimicrobial effect was observed at a minimum concentration of 1% for P. aeruginosa, 2.5% for S. aureus, 5% for C. albicans, and 10% for S. mutans. Surface hardness and compressive strength increased significantly with the addition of 0.5% β-AgVO3 (p;0.05). Conclusions: The incorporation of β-AgVO3 has the potential to promote antimicrobial activity in the acrylic resin. At reduced rates, it improves the mechanical properties, and, at higher rates, it does not promote changes in the control

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

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