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

Dual Role of Insulin-Like Growth Factor (IGF)-I in American Tegumentary Leishmaniasis

Background. Cytokines and growth factors involved in the tissue inflammatory process influence the outcome of Leishmania infection. Insulin-like growth factor (IGF-I) constitutively present in the skin may participate in the inflammatory process and parasite-host interaction. Previous work has shown that preincubation of Leishmania (Leishmania) amazonensis with recombinant IGF-I induces accelerated lesion development. However, in human cutaneous leishmaniasis (CL) pathogenesis, it is more relevant to the persistent inflammatory process than progressive parasite proliferation. In this context, we aimed to investigate whether IGF-I was present in the CL lesions and if this factor may influence the lesions’ development acting on parasite growth and/or on the inflammatory/healing process. Methodology. Fifty-one CL patients’ skin lesion samples from endemic area of L. (Viannia) braziliensis infection were submitted to histopathological analysis and searched for Leishmania and IGF-I expression by immunohistochemistry. Results. In human CL lesions, IGF-I was observed preferentially in the late lesion (more than 90 days), and the percentage of positive area for IGF-I was positively correlated with duration of illness (r=0.42, P<0.05). IGF-I was highly expressed in the inflammatory infiltrate of CL lesions from patients evolving with good response to therapy (2.8%±2.1%; median=2.1%; n=18) than poor responders (1.3%±1.1%; median: 1.05%; n=6; P<0.05). Conclusions. It is the first time that IGF-I was detected in lesions of infectious cutaneous disease, specifically in American tegumentary leishmaniasis. IGF-I was related to chronicity and good response to treatment. We may relate this finding to the efficient anti-inflammatory response and the known action of IGF-I in wound repair. The present data highlight the importance of searching nonspecific factors besides adaptive immune elements in the study of leishmaniasis’ pathogenesis

Normal bone density and trabecular bone score, but high serum sclerostin in congenital generalized lipodystrophy.

CONTEXT: Berardinelli-Seip Congenital Lipodystrophy (BSCL) is a rare autosomal recessive syndrome characterized by a difficulty in storing lipids in adipocytes, low body fat mass, hypoleptinemia, and hyperinsulinemia. Sclerostin is a product of SOST gene that blocks the Wnt/β-catenin pathway, decreasing bone formation and enhancing adipogenesis. There are no data about sclerostin in people with BSCL. OBJECTIVE: We aimed to evaluate serum sclerostin, bone mineral density (BMD), and L1-L4 Trabecular Bone Score (TBS) in BSCL patients, generating new knowledge about potential mechanisms involved in the bone alterations of these patients. DESIGN, SETTING, AND PATIENTS: In this cross-sectional study, we included 11 diabetic patients with BSCL (age 24.7±8.1years; 6 females). Sclerostin, leptin, L1-L4 TBS, BMD were measured. Potential pathophysiological mechanisms have been suggested. RESULTS: Mean serum sclerostin was elevated (44.7±13.4pmol/L) and was higher in men than women (55.3±9.0 vs. 35.1±8.4pmol/L, p=0.004). Median of serum leptin was low [0.9ng/mL (0.5-1.9)]. Seven out of 11 patients had normal BMD, while four patients had high bone mass (defined as Z-score\u3e+2.5SD). Patients on insulin had lower sclerostin (37.3±9.2 vs. 52.6±13.4pmol/L, p=0.05). The mean TBS was 1.402±0.106, and it was higher than 1.300 in nine patients. CONCLUSIONS: Patients with lipoatrophic diabetes (BSCL) have high serum concentrations of sclerostin, normal or high BMD, and reasonable trabecular bone mass measured by TBS. This is the first report of high sclerostin and good bone microarchitecture (TBS) in BSCL patients

Causes of death in patients with Berardinelli-Seip congenital generalized lipodystrophy - Fig 3

<p>(A) Causes of death (n) stratified according to the age group of patients. (B) Patients’ mean age according to the cause of death.</p

Age at death (years-old) of Berardinelli-Seip Congenital Lipodystrophy patients according to the year of death.

<p>Age at death (years-old) of Berardinelli-Seip Congenital Lipodystrophy patients according to the year of death.</p

Main causes of death in Berardinelli-Seip Congenital Lipodystrophy patients.

<p>Results divided by gender: female (B), and male (C).</p

Causes of death and individual clinical features of patients with Berardinelli-Seip Congenital Lipodystrophy.

<p>Causes of death and individual clinical features of patients with Berardinelli-Seip Congenital Lipodystrophy.</p