Retrobulbar Cryptococcoma Mimicking Tumor in a Labrador Retriever

Background: Cryptococcosis is a systemic mycotic disease caused by encapsulated yeasts C. neoformans and C. gattii. Cryptococcus neoformans is predominantly found in soils and feces of pigeons and psittacids. Infection occurs mainly through the respiratory tract, through the inspiration of fungal propagules (basidiospores and/or desiccated yeast cells). The main lesions observed are in the nasal cavity and lungs, but in dogs, the central nervous system and eyes are widely affected. Despite some previously mentioned reports, the retrobulbar form has not been described in the literature. Therefore, the purpose of this report is to describe a case of retrobulbar cryptococcoma mimetizing a tumor in a young bitch.  Case: A 2-year-old female Labrador Retriever,  was admitted to one Veterinary Hospital with exophthalmia on the left eye for 15 days and other signs included negative retropulsion, mydriasis, and abscence of menace and pupillary reflexes. Ocular ultrasound imaging revealed a hyperecotic and heterogeneous retrobulbar mass in the mid-dorsal region compressing the optic nerve. Computed tomography of the skull showed the presence of proliferation of neoplastic tissue in a locally invasive retrobulbar region promoting      moderate rostrolateral displacement of the left ocular bulb, discrete osthelysis in maxillary bone, palatine, medial wall of the orbital bone and frontal bone, with destruction of cribiform plate adjacent to the dorsal region of the orbital wall and presence of mild contrast uptake in the region of the left olfactory bulb lobe, characterizing a picture compatible with neoplasia with malignancy and locally invasive characteristics. Exenteration and excision of part of the frontal bone were performed and histopathological examination revealed granulation with the presence of fibroblasts and  numerous typical blastoconidia of Cryptococcus neoformans. The patient was treated with Itraconazole [10 mg/kg, v.o, SID, for 90 days] and one year after diagnosis, X-ray was performed to control the lesion and radiographic aspects were within normal limits.Discussion: Cryptococcus sp. is an environmental fungus that has the potential to be pathogenic to humans and animals. Fungus present as a basidiospores in pigeon droppings. The patient described had a history of contact with free-living pigeons, making it a risk factor for the occurrence of cryptococcosis, being the possible cause of the infection. In dogs, the disease is mainly described in immunosuppressed animals, which was not the case of the patient, who presented clinical and laboratory results within the normal range and without a previous history of use of immunosuppressants. The alterations described in the computed tomography, such as destruction of the cribriform plate adjacent to the dorsal region of the left orbital wall and the presence of slight contrast uptake in the left olfactory bulb lobe region, are compatible with the main entry point for propagules of Cryptococcus sp. In the present case, no periocular and ocular alterations were observed as described in the literature, and the lesion was restricted to the retrobulbar space. Ultrasonography and computed tomography revealed a neoformation mimicking a malignant neoplasm and the diagnosis of cryptococcoma was revealed by histopathology.  Based on the present case, cytology through aspiration of retrobulbal neoformations is imperative as a diagnostic method, especially in endemic areas for fungal diseases that can mimic ocular neoplasms. Keywords: cryptococcosis, fungus, Cryptococcus sp., dog. Descritores: criptococose, fungo, Cryptococcus sp., cão

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