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

Randomized, phase 1/2, double-blind pioglitazone repositioning trial combined with antifungals for the treatment of cryptococcal meningitis – PIO study

Background: Cryptococcosis affects more than 220,000 patients/year, with high mortality even when the standard treatment [amphotericin B (AMB), 5-flucytosin (5-FC) and fluconazole] is used. AMB presents high toxicity and 5-FC is not currently available in Brazil. In a pre-clinical study, pioglitazone (PIO - an antidiabetic drug) decreased AMB toxicity and lead to an increased mice survival, reduced morbidity and fungal burden in brain and lungs. The aim of this trial is to evaluate the efficacy and safety of PIO combined with standard antifungal treatment for human cryptococcosis. Methods: A phase 1/2, randomized, double blind, placebo-controlled trial will be performed with patients from Belo Horizonte, Brazil. They will be divided into three groups (placebo, PIO 15 mg/day or PIO 45 mg/day) and will receive an additional pill during the induction phase of cryptococcosis’ treatment. Our hypothesis is that treated patients will have increased survival, so the primary outcome will be the mortality rate. Patients will be monitored for survival, side effects, fungal burden and inflammatory mediators in blood and cerebrospinal fluid. The follow up will occur for up 60 days. Conclusions: We expect that PIO will be an adequate adjuvant to the standard cryptococcosis’ treatment. Trial registration: ICTRP/WHO (and International Clinical Trial Registry Plataform (ICTRP/WHO) (http://apps.who.int/trialsearch/Trial2.aspx?TrialID=RBR-9fv3f4), RBR-9fv3f4 (http://www.ensaiosclinicos.gov.br/rg/RBR-9fv3f4). UTN Number: U1111-1226-1535. Ethical approvement number: CAAE 17377019.0.0000.5149

The Omicron Lineages BA.1 and BA.2 (<i>Betacoronavirus</i> SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation