KIT/PDGFRA Variant Allele Frequency as Prognostic Factor in Gastrointestinal Stromal Tumors (GISTs): Results From a Multi-Institutional Cohort Study

Background: The patient selection for optimal adjuvant therapy in gastrointestinal stromal tumors (GISTs) is provided by nomogram based on tumor size, mitotic index, tumor location, and tumor rupture. Although mutational status is not currently used to risk assessment, tumor genotype showed a prognostic influence on natural history and tumor relapse. Innovative measures, such as KIT/PDGFRA-mutant-specific variant allele frequency (VAF) levels detection from next-generation sequencing (NGS), may act as a surrogate of tumor burden and correlate with prognosis and overall survival of patients with GIST, helping the choice for adjuvant treatment. Patients and methods: This was a multicenter, hospital-based, retrospective/prospective cohort study to investigate the prognostic role of KIT or PDGFRA-VAF of GIST in patients with radically resected localized disease. In the current manuscript, we present the results from the retrospective phase of the study. Results: Two-hundred (200) patients with GIST between 2015 and 2022 afferent to 6 Italian Oncologic Centers in the EURACAN Network were included in the study. The receiver operating characteristic (ROC) curves analysis was used to classify "low" vs. "high" VAF values, further normalized on neoplastic cellularity (nVAF). When RFS between the low and high nVAF groups were compared, patients with GIST with KIT/PDGFRA nVAF > 50% showed less favorable RFS than patients in the group of nVAF ≤ 50% (2-year RFS, 72.6% vs. 93%, respectively; P = .003). The multivariable Cox regression model confirmed these results. In the homogeneous sub-population of intermediate-risk, patients with KIT-mutated GIST, the presence of nVAF >50% was statistically associated with higher disease recurrence. Conclusion: In our study, we demonstrated that higher nVAF levels were independent predictors of GIST prognosis and survival in localized GIST patients with tumors harboring KIT or PDGFRA mutations. In the cohort of intermediate-risk patients, nVAF could be helpful to improve prognostication and the use of adjuvant imatinib

Aplicação de tratamento estatístico em amostras de pH - um estudo prático voltado ao ensino de engenharia: Application of statistical treatment in pH samples - a practical study aimed at engineering teaching

O objetivo deste trabalho foi apresentar um tratamento estatístico realizado em três amostras durante um dia de trabalho, no tocante à medição do pH da água de um sistema de tratamento de efluentes de uma empresa do Estado de Pernambuco. A metodologia aplicada consistiu na verificação de normalidade, verificação de outliers, estabelecimento do tamanho mínimo da amostra, determinação do intervalo de confiança e a expressão do resultado de medição (incluindo a incerteza de medição). O aspecto mais relevante deste trabalho diz respeito à aplicação de uma metodologia em um problema real, visando a confiabilidade nos resultados, sendo aplicada por alunos de engenharia, no decorrer da disciplina de estatística. Portanto, além do passo a passo deste trabalho poder ser aplicado a quaisquer grandezas, há ainda o objetivo de disseminação desta prática em outros cursos de graduação e pós-graduação em engenharia, para a disciplina de estatística

Water Availability Is the Main Climate Driver of Neotropical Tree Growth

• Climate models for the coming century predict rainfall reduction in the Amazonian region, including change in water availability for tropical rainforests. Here, we test the extent to which climate variables related to water regime, temperature and irradiance shape the growth trajectories of neotropical trees. • We developed a diameter growth model explicitly designed to work with asynchronous climate and growth data. Growth trajectories of 205 individual trees from 54 neotropical species censused every 2 months over a 4-year period were used to rank 9 climate variables and find the best predictive model. • About 9% of the individual variation in tree growth was imputable to the seasonal variation of climate. Relative extractable water was the main predictor and alone explained more than 60% of the climate effect on tree growth, i.e. 5.4% of the individual variation in tree growth. Furthermore, the global annual tree growth was more dependent on the diameter increment at the onset of the rain season than on the duration of dry season. • The best predictive model included 3 climate variables: relative extractable water, minimum temperature and irradiance. The root mean squared error of prediction (0.035 mm.d–1) was slightly above the mean value of the growth (0.026 mm.d–1). • Amongst climate variables, we highlight the predominant role of water availability in determining seasonal variation in tree growth of neotropical forest trees and the need to include these relationships in forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest

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

The response of tropical rainforests to drought : lessons from recent research and future prospects

Key message: we review the recent findings on the influence of drought on tree mortality, growth or ecosystem functioning in tropical rainforests. Drought plays a major role in shaping tropical rainforests and the response mechanisms are highly diverse and complex. The numerous gaps identified here require the international scientific community to combine efforts in order to conduct comprehensive studies in tropical rainforests on the three continents. These results are essential to simulate the future of these ecosystems under diverse climate scenarios and to predict the future of the global earth carbon balance. - Context: tropical rainforest ecosystems are characterized by high annual rainfall. Nevertheless, rainfall regularly fluctuates during the year and seasonal soil droughts do occur. Over the past decades, a number of extreme droughts have hit tropical rainforests, not only in Amazonia but also in Asia and Africa. The influence of drought events on tree mortality and growth or on ecosystem functioning (carbon and water fluxes) in tropical rainforest ecosystems has been studied intensively, but the response mechanisms are complex.- Aims: herein, we review the recent findings related to the response of tropical forest ecosystems to seasonal and extreme droughts and the current knowledge about the future of these ecosystems. - Results: this review emphasizes the progress made over recent years and the importance of the studies conducted under extreme drought conditions or in through-fall exclusion experiments in understanding the response of these ecosystems. It also points to the great diversity and complexity of the response of tropical rainforest ecosystems to drought. - Conclusion: the numerous gaps identified here require the international scientific community to combine efforts in order to conduct comprehensive studies in tropical forest regions. These results are essential to simulate the future of these ecosystems under diverse climate scenarios and to predict the future of the global earth carbon balance

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

Functional capacity and its relationship with daytime sleepiness in nursing staff who perform night shifts in a clinic in Lima, April - May 2019.

El objetivo del estudio fue determinar la relación entre la capacidad funcional y la somnolencia diurna en el personal de enfermería que realizan guardias nocturnas La muestra estuvo conformada por 53 enfermeros que realizan guardias nocturnas. Se realizóTesi