Parasitemia and antibody response to benznidazole treatment in a cohort of patients with chronic Chagas disease

BackgroundEvaluating the effectiveness of Chagas disease treatment poses challenges due to the lack of biomarkers for disease progression and therapeutic response. In this study, we aimed to assess the clearance of Trypanosoma cruzi (T. cruzi) parasites in a group of benznidazole (BNZ)-treated chronic Chagas disease patients using high-sensitivity quantitative PCR (qPCR) and track T. cruzi antibody levels through a semiquantitative chemiluminescent assay.MethodsA total of 102 T. cruzi seropositive patients with previous PCR-positive results were enrolled in the study. We collected samples 30 days before treatment (T-30d), on the day before initiating BNZ treatment (T0d), and at follow-up visits 60 days (T60d), 6 months (T6M), 12 months (T12M), and 36 months (T36M) after treatment initiation. Treatment efficacy was assessed by testing of serial samples using a target-capture qPCR assay specific to satellite T. cruzi DNA and the ORTHO T. cruzi ELISA Test System for antibody quantitation.ResultsOf the enrolled individuals, 87 completed at least 50% of the treatment course, and 86 had PCR results at follow-up visits T6M, T12M, and T36M. PCR results exhibited fluctuations before and after treatment, but levels were significantly lower post-treatment. Only 15 cases consistently tested PCR-negative across all post-treatment visits. Notably, nearly all participants demonstrated a declining antibody trajectory, with patients who tested PCR-negative at T36M exhibiting an earlier and more pronounced decline compared to PCR-positive cases at the same visit.ConclusionOur study suggests that serial PCR results pose challenges in interpretation. In contrast, serial antibody levels may serve as an ancillary, or even a more reliable indicator of parasite decline following BNZ treatment. Monitoring antibody levels can provide valuable insights into the efficacy of treatment and the persistence of parasites in Chagas disease patients

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

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements