Tratamento de superfície de nitinol por anodização eletroquímica

Universidade Federal do Rio Grande do SulEngenhariaDepositad

Autoimmune features caused by dengue fever: a case report

Dengue virus is the most important mosquito-borne viral disease in the world. Co-circulation of the four types of dengue viruses and expansion of dengue epidemic gave rise to infection enhancement and a big expansion of clinical aspects of the disease. Herein we report a case of a 25-year-old white woman with dengue fever and numerous associated autoimmune features. Our patient had proteinuria, an extensive right pleural effusion, a thin pericardial effusion and ascites. She had a low C3 level and positive antinuclear antibody; cryoglobulins were also positive. The numerous autoimmune features of this patient were a diagnostic challenge, since she was a young woman and could be easily mistaken for a rheumatologic patient in a newly open disease. Dengue infection probably was a triggering event causing an abnormal immune response. Therefore, dengue should be suspected in patients with hematological disorders and autoimmune features in endemic regions or those who have travelled to those regions.929

Autoimmune features caused by dengue fever:a case report

Dengue virus is the most important mosquito-borne viral disease in the world. Co-circulation of the four types of dengue viruses and expansion of dengue epidemic gave rise to infection enhancement and a big expansion of clinical aspects of the disease. Herein we report a case of a 25-year-old white woman with dengue fever and numerous associated autoimmune features. Our patient had proteinuria, an extensive right pleural effusion, a thin pericardial effusion and ascites. She had a low C3 level and positive antinuclear antibody; cryoglobulins were also positive. The numerous autoimmune features of this patient were a diagnostic challenge, since she was a young woman and could be easily mistaken for a rheumatologic patient in a newly open disease. Dengue infection probably was a triggering event causing an abnormal immune response. Therefore, dengue should be suspected in patients with hematological disorders and autoimmune features in endemic regions or those who have travelled to those regions. (C) 2012 Elsevier Editora Ltda. All rights reserved

Circulating Tumor DNA Detection in the Management of Anti-EGFR Therapy for Advanced Colorectal Cancer

Background: Anti-EGFR antibodies are a standard care for advanced KRAS-wild type colorectal cancers. Circulating tumor DNA (ctDNA) monitoring during therapy can detect emergence of KRAS mutant clones and early resistance to therapy.Case Presentation: We describe a 61-years-old man presenting a metastatic and recurrent rectal cancer treated with different chemotherapy regimens. His tumor was KRAS wild-type based on tissue analysis and he was treated sequentially with cetuximab-based chemotherapy, chemotherapy alone and panitumumab-based chemotherapy. We performed sequential analysis of ctDNA using droplet digital PCR (ddPCR) and a commercial assay designed for the detection of frequent KRAS mutations during his clinical follow-up. Prior to the first cetuximab-based chemotherapy ctDNA analysis demonstrated an absence of KRAS mutations. Emergence of KRAS mutations in ctDNA occurred ~3 months after treatment initiation and preceded clinical and imaging progression in about 2 months. Fractional abundance of KRAS mutation rapidly increased to 70.7% immediately before a chemotherapy alone regimen was initiated. Interestingly, KRAS mutation abundance decreased significantly during the first two months of chemotherapy, reaching a fractional abundance of 3.0%, despite minimal clinical benefit with this therapy. Re-challenge with a different anti-EGFR antibody was attempted as later line, but high levels of KRAS mutations in ctDNA before therapy correlated with an absence of clinical benefit.Conclusions: The monitoring of resistance mutations in KRAS using ctDNA during the treatment of KRAS wild-type advanced colorectal cancers can detect the emergence of resistant clones prior to clinical progression. Dynamics of resistant clones may alter during periods on and off anti-EGFR antibodies, detecting window of opportunities for a re-challenge with these therapies

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

An appraisal of drug development timelines in the Era of precision oncology.

The effects of incorporating a biomarker-based (personalized or precision) selection strategy on drug development timelines for new oncology drugs merit investigation. Here we accessed documents from the Food and Drug Administration (FDA) database for anticancer agents approved between 09/1998 and 07/2014 to compare drugs developed with and without a personalized strategy. Sixty-three drugs were included (28 [44%] personalized and 35 [56%] non-personalized). No differences in access to FDA-expedited programs were observed between personalized and non-personalized drugs. A personalized approach for drug development was associated with faster clinical development (Investigational New Drug [IND] to New Drug Application [NDA] submission; median = 58.8 months [95% CI 53.8-81.8] vs. 93.5 months [95% CI 73.9-112.9], P =.001), but a similar approval time (NDA submission to approval; median=6.0 months [95% CI 5.5-8.4] vs. 6.1 months [95% CI 5.9-8.3], P = .756) compared to a non-personalized strategy. In the multivariate model, class of drug stratified by personalized status (targeted personalized vs. targeted non-personalized vs. cytotoxic) was the only independent factor associated with faster total time of clinical drug development (clinical plus approval phase, median = 64.6 vs 87.1 vs. 112.7 months [cytotoxic], P = .038). Response rates (RR) in early trials were positively correlated with RR in registration trials (r = 0.63, P = <.001), and inversely associated with total time of drug development (r = -0.29, P = .049). In conclusion, targeted agents were developed faster than cytotoxic agents. Shorter times to approval were associated, in multivariate analysis, with a biomarker-based clinical development strategy