Identification of clusters of asthma control: A preliminary analysis of the inspirers studies

This work was funded by ERDF (European Regional Development Fund) through the operations: POCI- -01-0145-FEDER-029130 (“mINSPIRERS—mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases - generalisation and evaluation of gamification, peer support and advanced image processing technologies”) co-funded by the COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia).© 2020, Sociedade Portuguesa de Alergologia e Imunologia Clinica. All rights reserved. Aims: To identify distinct asthma control clusters based on Control of Allergic Rhinitis and Asthma Test (CARAT) and to compare patients’ characteristics among these clusters. Methods: Adults and adolescents (≥13 years) with persistent asthma were recruited at 29 Portuguese hospital outpatient clinics, in the context of two observational studies of the INSPIRERS project. Demographic and clinical characteristics, adherence to inhaled medication, beliefs about inhaled medication, anxiety and depression, quality of life, and asthma control (CARAT, >24 good control) were collected. Hierarchical cluster analysis was performed using CARAT total score (CARAT-T). Results: 410 patients (68% adults), with a median (percentile 25–percentile 75) age of 28 (16-46) years, were analysed. Three clusters were identified [mean CARAT-T (min-max)]: cluster 1 [27(24-30)], cluster 2 [19(14-23)] and cluster 3 [10(2-13)]. Patients in cluster 1 (34%) were characterised by better asthma control, better quality of life, higher inhaler adherence and use of a single inhaler. Patients in clusters 2 (50%) and 3 (16%) had uncontrolled asthma, lower inhaler adherence, more symptoms of anxiety and depression and more than half had at least one exacerbation in the previous year. Further-more, patients in cluster 3 were predominantly female, had more unscheduled medical visits and more anxiety symp-toms, perceived a higher necessity of their prescribed inhalers but also higher levels of concern about taking these inhalers. There were no differences in age, body mass index, lung function, smoking status, hospital admissions or specialist physician follow-up time among the three clusters. Conclusion: An unsupervised method based on CARAT--T, identified 3 clusters of patients with distinct, clinically meaningful characteristics. The cluster with better asthma control had a cut-off similar to the established in the validation study of CARAT and an additional cut-off seems to distinguish more severe disease. Further research is necessary to validate the asthma control clusters identified.publishersversionpublishe

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

Serological survey for Chagas disease in the rural areas of Manaus, Coari, and Tefé in the Western Brazilian Amazon

INTRODUCTION: Deforestation, uncontrolled forest, human population migration from endemic areas, and the large number of reservoirs and wild vectors naturally infected by Trypanosoma cruzi promote the endemicity of Chagas disease in the Amazon region. METHODS: We conducted an initial serological survey (ELISA) in a sample of 1,263 persons; 1,095 (86.7%) were natives of the State of Amazonas, 666 (52.7%) were male, and 948 (75.1%) were over 20 years old. Serum samples that were found to be reactive, indeterminate, or inconclusive by indirect immunofluorescence (IFI) or positive with low titer by IFA were tested by Western blot (WB). Serologically confirmed patients (WB) were evaluated in terms of epidemiological, clinical, ECG, and echocardiography characteristics. RESULTS: Fifteen patients had serologically confirmed T. cruzi infection, and 12 of them were autochthonous to the state of Amazonas, for an overall seroprevalence of 1.2% and 0.9% for the state of Amazonas. Five of the 15 cases were males, and the average age was 47 years old; most were farmers with low education. One patient who was not autochthonous, having originated from Alagoas, showed right bundle branch block, bundle branch block, and anterosuperior left ventricular systolic dysfunction with an ejection fraction of 54%. CONCLUSIONS: The results of this study ratify the importance of monitoring CD cases in Amazonia, particularly in the state of Amazonas

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