TLR2, TLR4 and the MYD88 Signaling Pathway Are Crucial for Neutrophil Migration in Acute Kidney Injury Induced by Sepsis

The aim of this study was to investigate the role of TLR2, TLR4 and MyD88 in sepsis-induced AKI. C57BL/6 TLR2(-/-), TLR4(-/-) and MyD88(-/-) male mice were subjected to sepsis by cecal ligation and puncture (CLP). Twenty four hours later, kidney tissue and blood samples were collected for analysis. the TLR2(-/-), TLR4(-/-) and MyD88(-/-) mice that were subjected to CLP had preserved renal morphology, and fewer areas of hypoxia and apoptosis compared with the wild-type C57BL/6 mice (WT). MyD88(-/-) mice were completely protected compared with the WT mice. We also observed reduced expression of proinflammatory cytokines in the kidneys of the knockout mice compared with those of the WT mice and subsequent inhibition of increased vascular permeability in the kidneys of the knockout mice. the WT mice had increased GR1(+low) cells migration compared with the knockout mice and decreased in GR1(+high) cells migration into the peritoneal cavity. the TLR2(-/-), TLR4(-/-), and MyD88(-/-) mice had lower neutrophil infiltration in the kidneys. Depletion of neutrophils in the WT mice led to protection of renal function and less inflammation in the kidneys of these mice. Innate immunity participates in polymicrobial sepsis-induced AKI, mainly through the MyD88 pathway, by leading to an increased migration of neutrophils to the kidney, increased production of proinflammatory cytokines, vascular permeability, hypoxia and apoptosis of tubular cells.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)National Institute of Science and Technology (INCT)Universidade Federal de São Paulo, Dept Med, Disciplina Nefrol, São Paulo, BrazilUniv São Paulo, Dept Imunol, Lab Imunobiol Transplantes, São Paulo, BrazilHosp Israelita Albert Einstein, IIEP, São Paulo, BrazilUniv Fed Triangulo Mineiro, Uberaba, BrazilUniversidade Federal de São Paulo, Dept Med, Disciplina Nefrol, São Paulo, BrazilFAPESP: 07/07139-3Web of Scienc

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Effect Of Ultrafiltration On Pulmonary Function And Interleukins In Patients Undergoing Cardiopulmonary Bypass.

To evaluate the effect of ultrafiltration on interleukins, TNF-α levels, and pulmonary function in patients undergoing coronary artery bypass grafting (CABG). Prospective, randomized, controlled trial. University hospital. Forty patients undergoing CABG were randomized into a group assigned to receive ultrafiltration (UF) during cardiopulmonary bypass (CPB) or into another group (control) that underwent the same procedure but without ultrafiltration. Interleukins and TNF-α levels, pulmonary gas exchange, and ventilatory mechanics were measured in the preoperative, intraoperative, and postoperative periods. Interleukins and TNF-α also were analyzed in the perfusate of the test group. There were increases in IL-6 and IL-8 at 30 minutes after CPB and 6, 12, 24, and 36 hours after surgery, along with an increase in TNF-α at 30 minutes after CPB and 24, 36, and 48 hours after surgery in both groups. IL-1 increased at 30 minutes after CPB and 12 hours after surgery, while IL-6 increased 24 and 36 hours after surgery in the UF group. The analysis of the ultrafiltrate showed the presence of TNF-α and traces of IL-1β, IL-6, and IL-8. There were alterations in the oxygen index, alveolar-arterial oxygen difference, deadspace, pulmonary static compliance and airway resistance after anesthesia and sternotomy, as well as in airway resistance at 6 hours after surgery in both groups, with no difference between them. Ultrafiltration increased the serum level of IL-1 and IL-6, while it did not interfere with gas exchange and pulmonary mechanics in CABG