Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.KWF Kankerbestrijding ; Netherlands Organization for Scientific Research (NWO) ; Swedish Research Council European Commission ; Swedish Cancer Society ; Swedish eScience Research Center ; Ake Wiberg Foundation ; Prostatacancerforbundet ; Academy of Finland ; Cancer Foundation Finland ; Google Incorporated ; MICCAI board challenge working group ; Verily Life Sciences ; EIT Health ; Karolinska Institutet ; MICCAI 2020 satellite event team ; ERAPerMe

Seagrass and submerged aquatic vegetation (VAS) habitats off the Coast of Brazil: state of knowledge, conservation and main threats

Seagrass meadows are among the most threatened ecosystems on earth, raising concerns about the equilibrium of coastal ecosystems and the sustainability of local fisheries. The present review evaluated the current status of the research on seagrasses and submerged aquatic vegetation (SAV) habitats off the coast of Brazil in terms of plant responses to environmental conditions, changes in distribution and abundance, and the possible role of climate change and variability. Despite an increase in the number of studies, the communication of the results is still relatively limited and is mainly addressed to a national or regional public; thus, South American seagrasses are rarely included or cited in global reviews and models. The scarcity of large-scale and long-term studies allowing the detection of changes in the structure, abundance and composition of seagrass habitats and associated species still hinders the investigation of such communities with respect to the potential effects of climate change. Seagrass meadows and SAV occur all along the Brazilian coast, with species distribution and abundance being strongly influenced by regional oceanography, coastal water masses, river runoff and coastal geomorphology. Based on these geomorphological, hydrological and ecological features, we characterised the distribution of seagrass habitats and abundances within the major coastal compartments. The current conservation status of Brazilian seagrasses and SAV is critical. The unsustainable exploitation and occupation of coastal areas and the multifold anthropogenic footprints left during the last 100 years led to the loss and degradation of shoreline habitats potentially suitable for seagrass occupation. Knowledge of the prevailing patterns and processes governing seagrass structure and functioning along the Brazilian coast is necessary for the global discussion on climate change. Our review is a first and much-needed step toward a more integrated and inclusive approach to understanding the diversity of coastal plant formations along the Southwestern Atlantic coast as well as a regional alert the projected or predicted effects of global changes on the goods and services provided by regional seagrasses and SAV

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

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

Kidney carcinoma associated with Xp11.2 translocation / TFE3 (ASPL-TFE3) gene fusion

We report the case of a 58-year old patient, showing a solid image in the right kidney, who underwent radical nephrectomy that revealed neoplasia, whose pathological study led to the diagnosis of kidney carcinoma associated with Xp11.2 translocation / TFE3 (ASPL-TFE3) gene fusion. The authors discuss aspects related to this lesion, such as frequency, pathogenesis, clinical presentation, histopathology and outcome, as observed in the literature

Cholesteatoma of the upper urinary tract

We report the case of a 57-year old patient with complex cystic image in right kidney. Following radical nephrectomy, the pathological study established the diagnosis of renal cholesteatoma. We discuss the frequency, pathogenesis, clinical presentation, propedeutics, histological findings and proposes for intervention observed in the literature