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

L1 cell adhesion molecule as a potential therapeutic target in murine models of endometriosis using a monoclonal antibody approach.

BACKGROUND/AIMS: The neural cell adhesion molecule L1CAM is a transmembrane glycoprotein abnormally expressed in tumors and previously associated with cell proliferation, adhesion and invasion, as well as neurite outgrowth in endometriosis. Being an attractive target molecule for antibody-based therapy, the present study assessed the ability of the monoclonal anti-L1 antibody (anti-L1 mAb) to impair the development of endometriotic lesions in vivo and endometriosis-associated nerve fiber growth. METHODS AND RESULTS: Endometriosis was experimentally induced in sexually mature B6C3F1 (n=34) and CD-1 nude (n=21) mice by autologous and heterologous transplantation, respectively, of endometrial fragments into the peritoneal cavity. Transplantation was confirmed four weeks post-surgery by in vivo magnetic resonance imaging and laparotomy, respectively. Mice were then intraperitoneally injected with anti-L1 mAb or an IgG isotype control antibody twice weekly, over a period of four weeks. Upon treatment completion, mice were sacrificed and endometrial implants were excised, measured and fixed. Endometriosis was histologically confirmed and L1CAM was detected by immunohistochemistry. Endometriotic lesion size was significantly reduced in anti-L1-treated B6C3F1 and CD-1 nude mice compared to mice treated with control antibody (P<0.05). Accordingly, a decreased number of PCNA positive epithelial and stromal cells was detected in autologously and heterologously induced endometriotic lesions exposed to anti-L1 mAb treatment. Anti-L1-treated mice also presented a diminished number of intraperitoneal adhesions at implantation sites compared with controls. Furthermore, a double-blind counting of anti-neurofilament L stained nerves revealed significantly reduced nerve density within peritoneal lesions in anti-L1 treated B6C3F1 mice (P=0.0039). CONCLUSIONS: Local anti-L1 mAb treatment suppressed endometriosis growth in B6C3F1 and CD-1 nude mice and exerted a potent anti-neurogenic effect on induced endometriotic lesions in vivo. The findings of this preliminary study in mice provide a strong basis for further testing in in vivo models

The proliferative status of endometriotic cells in the induced endometriotic lesions assessed by PCNA immunostaining.

<p>Negative and positive (breast cancer) controls were included to determine the specificity of the immunostainings (magnification: x100 and x200).</p