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

Sarcopenia in sedentary elderly and relation with functionality and inflammatory markers (IL-6 and IL-10)

INTRODUCTION: Sarcopenia is progressive loss of skeletal muscle mass related to aging. A sedentary lifestyle is a major factor related to the imbalance between synthesis and protein degradation, especially in the elderly. OBJECTIVE: To investigate the prevalence of sarcopenia in a group of sedentary elderly and its relationship to physical strength and inflammatory markers. METHODS: were conducted in 53 elderly collection of anthropometric data, assessment of body composition, functional test (Timed Up and Go), test of strength (grip) and collecting blood sample for evaluation of inflammatory markers. RESULTS: The prevalence of sarcopenia in the sample was 50% men and 54% among women. The average age was 71.3 years old in sarcopenia group and 66.4 years in non sarcopenia group (p = 0.015). There was no statistically significant difference between the values found for IL-6 (p = 0.70) and IL-10 (p = 0.14). CONCLUSION: There was a high prevalence of sarcopenia in sedentary elderly and muscle mass loss was associated with loss of muscle strength in men and women. There was no association with inflammatory markers IL-6 and IL-10.</p

Acupuncture Treatment in Elderly People with Sarcopenia: Effects on the Strength and Inflammatory Mediators

Introduction. Sarcopenia is defined as the progressive loss of skeletal muscle mass, associated with aging. A multidisciplinary approach has been increasingly prioritized in elderly care. A technique that has been widely used by the seniors is acupuncture. Objectives. To analyse the effects of acupuncture in muscle strength and in inflammatory markers of older people with sarcopenia. Methods. The sample was composed by 53 elderly people, aged over 60 years. Inclusion criteria were as follows: male and female seniors, sedentary and who were not under acupuncture treatment during the survey period. Assessment of body composition, handgrip strength, and functional test and IL-6, IL-10, and TNF-α cytokines analyses were performed. After verification of the physical examination, the subjects were divided into two groups (sarcopenic and nonsarcopenic). The first group was then randomized (by drawing lot) to be further divided into two subgroups: G1, composed of sarcopenic elderly people who received acupuncture intervention, and G2, composed of sarcopenic elderly people who did not receive intervention. The nonsarcopenic elderly people composed the group 3 (G3) and did not receive acupuncture intervention. ANOVA Split Plot was performed for intergroup comparison. For intragroup evaluation, ANOVA was conducted for repeated measures. For the delta values, ANCOVA was performed with the pretest as covariant. A p<0.05 significance level was adopted. Results. 26 older people concluded the collections. There was no statistically significant difference between the G1 group and the other ones regarding the assessed variables (muscle mass, muscle strength, functionality, and inflammatory markers). Conclusion. The results allow us to infer that it is possible that the conducted intervention protocol has not produced any significant effects in the studied population. UTN number: RBR-8df2h4