Differentially transcribed genes in skeletal muscle of lambs

The objective of this study was to compare gene transcription profiles in Longissimus dorsi muscle of the following four hair sheep genetic groups, Morada Nova (MO), Brazilian Somali (SO), Santa Inˆes (SI) and 1⁄2Dorper  1⁄2Morada Nova (F1). These groups all display different postnatal muscle growth. The transcriptomes of the skeletal muscle of the lambs (at 200 days of age) were profiled by using oligonucleotide microarrays and reverse transcription- quantitative real-time PCR (RT-qPCR). The microarray experiment identified 262 transcripts that were differentially expressed when transcription levels were compared between the different breeds. A total of 23 transcripts among which those involved in skeletal muscle development (MyoD1 and IGFBP4), lipogenesis and adipogenesis (C/EBP d , PPAR g and PGDS) were differentially expressed in at least in one comparison. Clustering analysis showed that there is greater similarity in gene expression between the MO and SI breeds and between F1 and SO genetic groups. The SO breed has the most distinct expression pattern. The RT-qPCR results confirmed the findings from the microarray study. A positive correlation was observed between the expression of MyoD1 and the cold carcass yield. The negative correlations between the weight and yield of cold carcass with the expression of C/EBP d mean that the selection for adipogenesis could lead to a lower carcass weight. The GLUT3 and PYGL gene transcripts were negatively correlated with fat thickness, but ATP5G1 was positively correlated with this trait. Interestingly, many genes negatively correlated with PUFA were positively correlated with cold carcass yield. In conclusion, the present work demonstrated that there are breed-specific expression patterns in Brazilian hair sheep genetic groups. The differences in gene expression among genetic groups were consistent with their phenotypic differences. The positive correlation of the MyoD1 expression with the cold carcass yield suggests that this gene is important for tissue growth in sheep. Thepositive correlation of the C/EBP d expression with PUFA provides an opportunity to select for lipid deposition in meat animals

Transmural remission improves clinical outcomes up to 5 years in Crohn's disease

IntroductionEvidence supporting transmural remission (TR) as a long-term treatment target in Crohn's disease (CD) is still unavailable. Less stringent but more reachable targets such as isolated endoscopic (IER) or radiologic remission (IRR) may also be acceptable options in the long-term. MethodsMulticenter retrospective study including 404 CD patients evaluated by magnetic resonance enterography and colonoscopy. Five-year rates of hospitalization, surgery, use of steroids, and treatment escalation were compared between patients with TR, IER, IRR, and no remission (NR). Results20.8% of CD patients presented TR, 23.3% IER, 13.6% IRR and 42.3% NR. TR was associated with lower risk of hospitalization (odds-ratio [OR] 0.244 [0.111-0.538], p < 0.001), surgery (OR 0.132 [0.030-0.585], p = 0.008), steroid use (OR 0.283 [0.159-0.505], p < 0.001), and treatment escalation (OR 0.088 [0.044-0.176], p < 0.001) compared to no NR. IRR resulted in lower risk of hospitalization (OR 0.333 [0.143-0.777], p = 0.011) and treatment escalation (OR 0.260 [0.125-0.540], p < 0.001), while IER reduced the risk of steroid use (OR 0.442 [0.262-0.745], p = 0.002) and treatment escalation (OR 0.490 [0.259-0.925], p = 0.028) compared to NR. ConclusionsTR improved clinical outcomes over 5 years of follow-up in CD patients. Distinct but significant benefits were seen with IER and IRR. This suggests that both endoscopic and radiologic remission should be part of the treatment targets of CD.info: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 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

Mesalamine-induced myopericarditis: a case report

Myopericarditis has occasionally been reported as a side effect of mesalamine in patients with inflammatory bowel disease. We present a 20-year-old woman with ulcerative colitis admitted with chest pain. After thorough investigation she was diagnosed with myopericarditis potentially related to mesalamine. There was complete clinical and laboratorial recovery following drug withdrawal. Although uncommon, the possibility of myopericarditis should be considered in patients with inflammatory bowel disease presenting with cardiac complaints. Early recognition can avoid potential life-threatening complications