Microemulsions Improve Topical Protoporphyrin IX (PpIX) Delivery for Photodynamic Therapy of Skin Cancer

We report here microemulsions (MEs) for topical delivery of protoporphyrin IX (PpIX) for Photodynamic Therapy (PDT) of skin cancers. Selected MEs consisting of Oil/Water (O/W) bicontinuous (BC) and Water/Oil (W/O) preparations were characterized as to pH, nanometric size, zeta potential, drug content, and viscosity. Sustained in vitro PpIX release was achieved from MEs 2A (O/W), 10B (BC) and 16B (W/O) through an artificial membrane for up to 24 h, characterizing MEs as drug delivery systems. None of these MEs showed permeation through the skin, demonstrating the required topical effect. After 4 h, in vitro retention of PpIX in the stratum corneum (SC) was higher from both ME 10B and control (PpIX at 60 µg/mL in PEG 300). However, in the Epidermis + Dermis ([Ep + D]), retention from ME 10B and ME 16B was ~40 times higher compared to control. Confocal Laser Scanning Microscopy (CLSM) showed higher fluorescence intensity in the SC for both control and ME 10B, whereas ME 10B fluorescence was higher in [Ep+D]. The results indicate that ME 10B is suitable for PpIX encapsulation, showing good characteristics and a localized effect for a potential delivery system for PDT-associated treatments of skin cancers

Reduced occurrence of programmed cell death and gliosis in the retinas of juvenile rabbits after short-term treatment with intravitreous bevacizumab

OBJECTIVE: Bevacizumab has been widely used as a vascular endothelial growth factor antagonist in the treatment of retinal vasoproliferative disorders in adults and, more recently, in infants with retinopathy of prematurity. Recently, it has been proposed that vascular endothelial growth factor acts as a protective factor for neurons and glial cells, particularly in developing nervous tissue. The purpose of this study was to investigate the effects of bevacizumab on the developing retinas of juvenile rabbits. METHODS: Juvenile rabbits received bevacizumab intravitreously in one eye; the other eye acted as an untreated control. Slit-lamp and fundoscopic examinations were performed both prior to and seven days after treatment. At the same time, retina samples were analyzed using immunohistochemistry to detect autophagy and apoptosis as well as proliferation and glial reactivity. Morphometric analyses were performed, and the data were analyzed using the Mann-Whitney U test. RESULTS: No clinical abnormalities were observed in either treated or untreated eyes. However, immunohistochemical analyses revealed a reduction in the occurrence of programmed cell death and increases in both proliferation and reactivity in the bevacizumab-treated group compared with the untreated group. CONCLUSIONS: Bevacizumab appears to alter programmed cell death patterns and promote gliosis in the developing retinas of rabbits; therefore, it should be used with caution in developing eye

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

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq