Prognostic implications of the ID1 expression in acute myeloid leukemia patients treated in a resource-constrained setting

Introduction: The aberrant expression of the inhibitor of DNA binding (ID1) gene has been frequently associated with the leukemogenesis and prognostication acute myeloid leukemia (AML), although its clinical importance has never been investigated in patients treated outside well-controlled clinical trials. Methods: Using quantitative real-time polymerase chain reaction, we investigated the role of the ID1 expression in the clinical outcomes of non-selected patients with acute myeloid leukemia treated in a real-life setting. Results: Overall, 128 patients were enrolled. Patients with high ID1 expression had a lower 3-year overall survival (OS) rate of 9%, with the 95% confidence interval (95%CI) at 3 to 20%, compared to patients with a low ID1 expression (22%, 95%CI: 11 - 34%) (p = 0.037), although these findings did not retain significance after adjustment (hazard ratio (HR): 1.5, 95%CI: 0.98 - 2.28; p = 0.057). The ID1 expression had no impact on post-induction outcomes (disease-free survival, p = 0.648; cumulative incidence of relapse, p = 0.584). Conclusions: Although we are aware thar our data are confronted with many variables that cannot be fully controlled, including drug unavailability, risk-adapted treatment, comorbidities and the time from diagnosis to treatment initiation, we are firm believers that such an initiative can provide more realistic data on understudied populations, in particular those from low- and middle-income countries.</p

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

Elaboration of A Fire Prevention and Fire Fighting Project in the Manaus-amazonas City

There is a known shortage in the technical literature regarding fire prevention and fire fighting, which is often not even a constant subject in the curriculum of engineering courses. In Manaus, fire projects are regulated by the Fire Brigade Technical Standards, so there is often a technical deficiency in the preparation of these projects. In this context this work is inserted, aiming to provide a logical and normative roadmap for the correct design of fire prevention and fire prevention facilities. The object of study adopted is a Shed of 2,112.00 square meters, located in the West Zone of Manaus. The design determined a mixed fire fighting system between extinguishers and fire hydrants, requiring the adoption of elevated reservoir systems and fire pumps

Prognostic implications of the ID1 expression in acute myeloid leukemia patients treated in a resource-constrained setting

Introduction: The aberrant expression of the inhibitor of DNA binding (ID1) gene has been frequently associated with the leukemogenesis and prognostication acute myeloid leukemia (AML), although its clinical importance has never been investigated in patients treated outside well-controlled clinical trials. Methods: Using quantitative real-time polymerase chain reaction, we investigated the role of the ID1 expression in the clinical outcomes of non-selected patients with acute myeloid leukemia treated in a real-life setting. Results: Overall, 128 patients were enrolled. Patients with high ID1 expression had a lower 3-year overall survival (OS) rate of 9%, with the 95% confidence interval (95%CI) at 3 to 20%, compared to patients with a low ID1 expression (22%, 95%CI: 11 - 34%) (p = 0.037), although these findings did not retain significance after adjustment (hazard ratio (HR): 1.5, 95%CI: 0.98 - 2.28; p = 0.057). The ID1 expression had no impact on post-induction outcomes (disease-free survival, p = 0.648; cumulative incidence of relapse, p = 0.584). Conclusions: Although we are aware thar our data are confronted with many variables that cannot be fully controlled, including drug unavailability, risk-adapted treatment, comorbidities and the time from diagnosis to treatment initiation, we are firm believers that such an initiative can provide more realistic data on understudied populations, in particular those from low- and middle-income countries.</p