Morphology of the interstitial cells of rat polycystic ovaries: an experimental study

PURPOSES: To evaluate the histomorphometry of ovarian interstitial cells, as well as the blood sex steroid concentrations of female rats with polycystic ovaries induced by continuous light. METHODS: Twenty female rats were divided into two groups: Control Group - in the estrous phase (CtrlG), and a group of rats with polycystic ovaries induced by continuous illumination (POG). CtrlG animals were maintained on a light period from 07:00 a.m. to 07:00 p.m., and POG animals with continuous illumination (400 Lux) for 60 days. After this period all animals were anesthetized and blood was collected for the determination of serum estradiol (E2), progesterone (P4), and testosterone (T), followed by removal of the ovaries that were fixed in 10% formalin and processed for paraffin embedding. Five-µm histological sections were stained with hematoxylin and eosin and used for histomorphometric analysis. Morphological analyses, cyst count, determination of concentration and of the nuclear volume of interstitial cells were performed with the aid of a light microscope adapted to a high resolution camera (AxioCam), whose images were transmitted to and analyzed by the computer using AxioVision Rel 4.8 software (Carl Zeiss). Data were analyzed statistically by the Student's t-test (pCtrlG=73.2±6.5, pCtrlG=80.6±3.9, pPOG=4.2±1.5, pCtrlG=63.6±16.5, pCtrlG=6.9±3.2, pGCtrl=73,2±6,5; pGCtrl=80,6±3,9; pGOP=4,2±1,5; pGCtrl=63,6±16,5; pGCtrl=6,9±3,2; p<0,05) em relação aos animais do GCtrl. CONCLUSÃO: As células intersticiais do ovário policístico da rata provavelmente provêm dos cistos ovarianos devido degeneração das células da granulosa e diferenciação das células da teca interna. As elevações dos níveis séricos de testosterona e de estradiol provavelmente provêm do aumento significativo da atividade celular e da área ocupada pelas células intersticiais.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de Morfologia e GenéticaUniversidade de São Paulo Faculdade de Medicina Departamento de Obstetrícia e GinecologiaUNIFESP, EPM, Depto. de Morfologia e GenéticaSciEL

Increasing the chances of natural conception: opinion statement from the the brazilian federation of gynecology and obstetrics associations - FEBRASGO - committee of gynecological endocrinology

Considering that myths and misconceptions regarding natural procreation spread rapidly in the era of easy access to information and to social networks, adequate counseling about natural fertility and spontaneous conception should be encouraged in any kind of health assistance. Despite the fact that there is no strong-powered evidence about any of the aspects related to natural fertility, literature on how to increase the chances of a spontaneous pregnancy is available. In the present article, the Brazilian Federation of Gynecology and Obstetrics Associations (FEBRASGO, in the Portuguese acronym) Committee on Endocrine Gynecology provides suggestions to optimize counseling for non-infertile people attempting spontaneous conception41318319

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