Age and growth of Zapteryx brevirostris (Elasmobranchii: Rhinobatidae) in southern Brazil

Age and growth studies are fundamental to successful fisheries management. Zapteryx brevirostris (Muller & Henle, 1841) is distributed off the Brazilian continental shelf and this species is assessed as "Vulnerable" in the Red List of the International Union for the Conservation of Nature (IUCN). Thus, the objective of this study was to present previously unknown information about the age and growth of Z. brevirostris that can be used for its management, conservation, and fisheries. A total of 162 specimens were sampled, with total lengths (TL) varying between 35.7 cm and 56 cm. The vertebrae were embedded in resin, sectioned in cuts with 0.5 mm thickness and the growth bands of the vertebrae were read under a light microscope. In the studied area, Z. brevirostris ages were estimated from 4 to 10 years according to vertebrae patterns. The species reaches its maximum asymptotic size (Linf) around 56 cm (56 cm for females and 50.37 cm for males). This is the first estimate of age and growth for a species of the Zapteryx genus, and the results support the hypothesis that this ray requires future management conservation, particularly due to its slow growth rate and consequent susceptibility to overexploitation.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES); Portuguese Foundation for Science and Technology (FCT, Fundacao para a Ciencia e Tecnologia) [IF/00253/2014

Promising self-emulsifying drug delivery system loaded with lycopene from red guava (Psidium guajava L.): in vivo toxicity, biodistribution and cytotoxicity on DU-145 prostate cancer cells

Background Self-emulsifying drug delivery systems (SEDDSs) have attracted attention because of their effects on solubility and bioavailability of lipophilic compounds. Herein, a SEDDS loaded with lycopene purified from red guava (nanoLPG) was produced. The nanoemulsion was characterized using dynamic light scattering (DLS), zeta potential measurement, nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), lycopene content quantification, radical scavenging activity and colloidal stability in cell culture medium. Then, in vivo toxicity and tissue distribution in orally treated mice and cytotoxicity on human prostate carcinoma cells (DU-145) and human peripheral blood mononuclear cells (PBMC) were evaluated. Results NanoLPG exhibited physicochemical properties with a size around 200 nm, negative zeta-potential, and spherical morphology. The size, polydispersity index, and zeta potential parameters suffered insignificant alterations during the 12 month storage at 5 °C, which were associated with lycopene stability at 5 °C for 10 months. The nanoemulsion showed partial aggregation in cell culture medium at 37 °C after 24 h. NanoLPG at 0.10 mg/mL exhibited radical scavenging activity equivalent to 0.043 ± 0.002 mg Trolox/mL. The in vivo studies did not reveal any significant changes in clinical, behavioral, hematological, biochemical, and histopathological parameters in mice orally treated with nanoLPG at 10 mg/kg for 28 days. In addition, nanoLPG successfully delivered lycopene to the liver, kidney and prostate in mice, improved its cytotoxicity against DU-145 prostate cancer cells—probably by pathway independent on classical necrosis and apoptosis—and did not affect PBMC viability. Conclusions Thus, nanoLPG stands as a promising and biosafe lycopene delivery system for further development of nanotechnology-based health products

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