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

Influência dos níveis de prolactina e tamanho tumoral na função hipofisária pós-operatória em macroadenomas hipofisários clinicamente não-funcionantes Influence of hyperprolactinemia and tumoral size in the postoperative pituitary function in clinically nonfuncioning pituitary macroadenomas

OBJETIVO: Estudar a influência da hiperprolactinemia e de tamanho tumoral na função hipofisária em macroadenomas hipofisários clinicamente não funcionantes. MÉTODOS: Foram analisados 23 pacientes com macroadenomas hipofisários clinicamente não funcionantes, com exames de imagem (tomografia computadorizada ou ressonância magnética) e dosagens hormonais basais; 16 tinham provas de função hipotálamo-hipofisária (megateste) pré-operatórios. Todos os tumores tiveram diagnóstico histológico e em 17 foi realizado também estudo imuno-histoquímico para os hormônios adeno-hipofisários. A análise estatística foi feita por meio dos testes t de Student, qui-quadrado, exato de Fisher e de Mc Neman. O nível de significância adotado foi 5% (pOBJECTIVE: To study the influence of hyperprolactinemia and tumoral size in the pituitary function in clinically nonfuncioning pituitary macroadenomas. METHODS: Twenty three patients with clinically nonfuncioning pituitary macroadenomas were evaluated by image studies (computed tomography or magnetic resonance) and basal hormonal level; 16 had preoperative hypothalamus-hypophysial function tests (megatests). All tumors had histological diagnosis and in seventeen immunohistochemical study for adenohypophysial hormones was also performed. Student's t test, chi square test, exact test of Fisher and Mc Neman test were used for the statistics analysis . The level of significance adopted was 5% (p<0.05). RESULTS: Tumoral diameter varied of 1.1 to 4.7 cm (average=2.99 cm ± 1.04). In the preoperative, 5 (21.7%) patients did not show laboratorial hormonal deficit, 9 (39.1%) developed hyperprolactinemia, 13 (56,5%) normal levels of prolactin (PRL) and 1 (4.3%) subnormal; 18 (78.3%) patients developed hypopituitarism (4 pan-hypopituitarism). Nineteen patients (82.6%) underwent transsfenoidal approach, 3 (13%) craniotomy and 1 (4.4%) combined access. Only 6 patients had total tumoral resection. Of the 17 immunohistochemical studies, 5 tumours were immunonegatives, 1 compound, 1 LH+, 1 FSH +, 1 alpha sub-unit and 8 focal or isolated immunorreactivity for one of the pituitary hormones or sub-units; of the other six tumours, 5 were chromophobe and 1 chromophobe/acidophile. No significanct statistic difference was noted between tumoral size and preoperative PRL levels (p=0.82), nor between tumoral size and postoperative hormonal state, except in the GH and gonadal axis. Significant statistic was noted: between tumoral size and preoperative hormonal state (except in the gonadal axis); between normal PRL levels, associated to none or little preoperative hypophysial disfunction, and recovery of postoperative pituitary function. CONCLUSION: Isolated preoperative hyperprolactinemia and tumoral size have not been predictible for the recovery of postoperative pituitary function

Direct detection of Mycobacterium tuberculosis complex in bovine and bubaline tissues through nested-PCR

Post-mortem bacterial culture and specific biochemical tests are currently performed to characterize the etiologic agent of bovine tuberculosis. Cultures take up to 90 days to develop. A diagnosis by molecular tests such as PCR can provide fast and reliable results while significantly decreasing the time of confirmation. In the present study, a nested-PCR system, targeting rv2807, with conventional PCR followed by real-time PCR, was developed to detect Mycobacterium tuberculosis complex (MTC) organisms directly from bovine and bubaline tissue homogenates. The sensitivity and specificity of the reactions were assessed with DNA samples extracted from tuberculous and non-tuberculous mycobacteria, as well as other Actinomycetales species and DNA samples extracted directly from bovine and bubaline tissue homogenates. Regarding the analytical sensitivity, DNA of the M. bovis AN5 strain was detected up to 1.5 pg by nested-PCR, whereas DNA of M. tuberculosis H37Rv strain was detected up to 6.1 pg. The nested-PCR system showed 100% analytical specificity for MTC when tested with DNA of reference strains of non-tuberculous mycobacteria and closely-related Actinomycetales. A clinical sensitivity level of 76.7% was detected with tissues samples positive for MTC by means of the culture and conventional PCR. A clinical specificity of 100% was detected with DNA from tissue samples of cattle with negative results in the comparative intradermal tuberculin test. These cattle exhibited no visible lesions and were negative in the culture for MTC. The use of the nested-PCR assay to detect M. tuberculosis complex in tissue homogenates provided a rapid diagnosis of bovine and bubaline tuberculosis