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

The use of serum alkaline phosphatase as a choledocholithiasis marker to mitigate the cost of magnetic resonance cholangiography

ABSTRACT Objective To assess the predictive value of preoperative serum laboratory test results for identifying choledocholithiasis and reduce the use of cholangioresonance and its inherent costs. Methods Patients aged 21-69 years who underwent preoperative cholangioresonance examination at our institute were included. Patients with a history of fluctuating jaundice or biliary pancreatitis, bile duct dilatation on ultrasonography, and elevated levels of canalicular enzymes (alkaline phosphatase >100U/L and gamma-glutamyl transferase >50U/L) underwent cholangioresonance-guided surgery. Cases of choledocholithiasis confirmed by cholangioresonance were compared with those without choledocholithiasis. Serum laboratory data were evaluated and the diagnostic capabilities of these examinations were analyzed. Results A total of 104 patients were included. For detecting choledocholithiasis using alkaline phosphatase, the cut-off point was 78U/L, sensitivity was 97.6% (95%CI: 87.4-99.9), and specificity was 72.6% (95%CI: 59.8-83.1). In the binary logistic regression analysis, age (OR= 0.92; 95%CI: 0.86-0.98) and alkaline phosphatase level (OR= 1.02; 95%CI: 1.01-1.05) were selected for the final model. Conclusion Serum alkaline phosphatase levels may aid preoperative diagnosis of asymptomatic choledocholithiasis. After a global clinical assessment of the patient, serum laboratory findings may contribute to a reduction in cholangioresonance-related heathcare costs