Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

PACS implementation in a university hospital in Tuscany.

We describe cost/benefit analysis for the implementation of a picture archiving and communication system in the Department of radiology of University of Siena, Italy. We also highlight technical and functional innovations provided by information of several department units: cost conversion, workflow optimisation and operator development. We analysed: operator costs, paper costs, film costs, chemical costs, costs of optical disks and location rent for hardware and software of RIS/PACS. The results show that advantages provided by PACS implementation derive from a workflow optimization and saving of human resources rather than from a reduction in films and chemicals. Moreover, better management of radiological unit provides improved handling of clinical information, resulting in reduced time to initiate clinical action, with reduction in average length of patient stay and improvements in overall health outcome

Preoperative MDCT assessment for lymphatic gastric cancer spread in the era of neoadjuvant treatment

Purpose: To validate the feasibility and accuracy of MDCT for the preoperative lymphatic gastric cancer spread. Material and Methods: 104 patients with primary gastric cancer (mean age 68.67 years) who consecutively underwent MDCT scan followed by radical surgical treatment were prospectively evaluated. Regional lymph nodes were considered involved when the short-axis diameter was >5mm for the lymph nodes of group 1 and >8mm for the lymph nodes of other group according to the Japanese Classification of Gastric Carcinoma. All patients underwent a radical lymph node dissection (D2-D3) according to Japanese Research Society for Gastric Cancer (JRSGC) guidelines. The removal of nodal stations was always preceded by Indian-ink injection in the lesser and greater curvature of the stomach; after operation, single lymph nodes were retrieved on the fresh specimen by the surgeon, and classified in JRSGC nodal stations for pathological examination. Results: Lymph node invasion was found in 85 cases (81.73%) with a MDCT sensitivity and specificity of 89% and 85%, respectively. The rate of understaging was higher (15%) than that of overstaging (8%). Lymph node status of early forms was correctly staged in all cases. Furthermore, all N3 cases were correctly staged. Conclusion: MDCT is a useful technique in the preoperative assessment of lymphatic cancer spread and could have a positive impact in clinical decision making in the era of neoadjuvant treatment

Differences in perfusion CT parameter values with commercial software upgrades: algorithm consistency and stability.

Computed tomographic (CT) perfusion imaging is a widely applied technique for the evaluation of acute ischemic stroke patients and to assess other brain diseases, including tumors. It is also a promising technique that realizes functional imaging, as an adjunct to a morfologic CT examination, that can be used as an aid to carefully evaluate the response to therapy in oncologic patients, especially with the new therapies. This technique has increased in the past few years, thanks to the diffusion of commercial Perfusion CT software platforms that are now integrated into a clinical reporting workstation. Although one of the advantages of CT perfusion imaging is it's ability to allow quantitative results, it has been reported that the CT perfusion imaging maps and relative quantitative results were significantly different among commercial software programs, provided by various CT manufacturers, using different algorithms, even when using identical source data, presumably because of differences in tracerdelay sensitivity and between different versions of the same software platform. V. Goh et al have demonstrated that upgrades of the same software (version 3.0 and 4.0 of perfusion software GE Healthcare Technologies) may alter the derived parameter values in colorectal cancer because of the introduction of T0, the time difference in arrival of contrast within the input vessel and the tissue of interest in version 4.0. Beyond this previous study and considering the recent introduction of the new version of upgrades of a widely-used commercial software platform (Perfusion 4.0 to 4D; GE Healthcare Technologies), the aim of our study was to determine how commercial software upgrades impact on algorithm consistency and stability among the three version upgrades of the same software platform (versions 3.0, 4.0 and 4D; Perfusion CT software, GE Healthcare Technologies)