Neoplasia maligna de glândula anexial cutânea em couro cabeludo: um caso de dúvida diagnóstica entre carcinoma metastático de mama e tumor primário de glândula sudoríparas

The authors describe the case of a 71-year-old female patient who initially went to the dermatologist to assess a scalp skin tumor. This lesion was submitted to an incisional biopsy, performed by dermatologist herself, and the result of the anatomopathological examination and immunohistochemical study showed a preliminary diagnosis of metastasis of breast carcinoma. The patient had no history of breast cancer and, in view of this result, she was referred to consult with the mastologist, who carried out an investigation of breast nodules in search of the possible primary focus of the carcinoma, through imaging exams, biopsies and mammotomy, without finding any possible primary focus on the breasts. Finally, the lesion on the scalp was removed in its entirety by the plastic surgeon and a new exam of pathological anatomy and an immunohistochemical study confirmed the diagnosis of metastasis of breast carcinoma. In view of these results, the authors discuss the difficulty in diagnosing differentiation from primary or metastatic neoplasm of the scalp, with the resources currently available, until the conclusion that it was a primary carcinoma of the sweat gland.Os autores descrevem o caso de uma paciente do sexo feminino, de 71 anos de idade e que inicialmente procurou a dermatologista para avaliação de um tumor de pele em couro cabeludo. Esta lesão foi submetida a uma biópsia incisional, realizada pela própria dermatologista, e o resultado do exame anatomopatológico e do estudo imuno-histoquímico mostrou um diagnóstico preliminar de metástase de carcinoma de mama. A paciente não tinha histórico de câncer de mamas e, perante este resultado, foi encaminhada para consultar com à mastologista, que realizou investigação de nódulos mamários na procura do possível foco primário do carcinoma, através de exames de imagem, biópsias e mamotomia, sem ser encontrado nenhum possível foco primário nas mamas. Finalmente a lesão do couro cabeludo foi retirada em sua totalidade, pelo cirurgião plástico e novo exame de anatomia patológica e do estudo imuno-histoquímico confirmou o diagnóstico de metástase de carcinoma mamário. Perante estes resultados os autores discutem sobre a dificuldade na diferenciação diagnóstica de neoplasia primária ou metastática do couro cabeludo, com os recursos disponíveis atualmente, até a conclusão de que se tratava de carcinoma primário de glândula sudorípara

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

Characterization, Oxidative Stability and Antioxidant Potential of Linseed (Linum usitatissimum L.) and Chia (Salvia hispanica L.) Oils

The aim of the present study was to assess the composition and oxidative stability of linseed and chia commercial oils, in addition to determining the kinetics of oxidation at temperatures of 100, 110, 120 and 130°C, as well as the quality parameters, acid value (AV), moisture and ash content. The data of oxidative stability index (OSI), moisture, acid value and ash content were acquired according to the methods: AOCS Cd 12b-92, EN ISO 8534 and AOAC, respectively. The fatty acid composition was assessed by gas chromatography coupled to flame ionization detector (FID). The antioxidant activity was assessed using the method of free radical scavenging of DPPH (2,2-diphenyl-1- picrylhydrazyl) and phenolic compounds using Folin-Ciocalteau reagent. The fatty acids identified in greater amount in the analyzed oils were the unsaturated acids linolenic, linoleic and oleic. Regarding the AV, linseed oil was more acid than chia oil. Chia oil offers better nutritional quality, resulting from the greater amount of unsaturations present in its composition, one of the factors that negatively affected its oxidative stability expressed as OSI. Regarding phenolic compounds and antioxidant potential, chia oil also showed better values, 319.12 mg g-1 and 149.57 µg mL-1, respectively. Linseed oil showed better oxidative stability with activation energy (Ea) and acceleration factor Q10 of 82.12 kJ mol-1 and 1.92, respectively, determined by kinetic studies for oxidative degradation performed using Rancimat method. DOI: http://dx.doi.org/10.17807/orbital.v11i4.1327 </p