Long COVID-19 syndrome associated with Omicron XBB.1.5 infection: a case report.

BACKGROUND: There is interest in lingering non-specific symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, referred to as Long coronavirus disease 2019 (Long COVID-19). It remains unknown whether the risk of Long COVID-19 is associated with pre-existing comorbidities or initial COVID-19 severity, including infections due to new Omicron lineages which predominated in 2023. OBJECTIVES: The aim of this case report was to characterize the clinical features of acute XBB.1.5 infection followed by Long COVID-19. METHODS: We followed a 73-year old female resident of Rio de Janeiro with laboratory-confirmed SARS-CoV-2 during acute infection and subsequent months. The SARS-CoV-2 lineage was determined by genome sequencing. FINDINGS: The participant denied comorbidities and had completed a two-dose vaccination schedule followed by two booster doses eight months prior to SARS-CoV-2 infection. Primary infection by viral lineage XBB.1.5. was clinically mild, but the participant subsequently reported persistent fatigue. MAIN CONCLUSIONS: This case demonstrates that Long COVID-19 may develop even after mild disease due to SARS-CoV-2 in fully vaccinated and boosted individuals without comorbidities. Continued monitoring of new SARS-CoV-2 lineages and associated clinical outcomes is warranted. Measures to prevent infection should continue to be implemented including development of new vaccines and antivirals effective against novel variants

Post-acute COVID-19 syndrome after reinfection and vaccine breakthrough by the SARS-CoV-2 Gamma variant in Brazil.

We describe a case of prolonged COVID-19 caused by the SARS-CoV-2 Gamma variant in a fully vaccinated healthcare worker, 387 days after an infection caused by lineage B.1.1.33. Infections were confirmed by whole-genome sequencing and corroborated by the detection of neutralizing antibodies in convalescent serum samples. Considering the permanent exposure of this healthcare worker to SARS-CoV-2, the waning immunity after the first infection, the low efficacy of the inactivated vaccine at preventing COVID-19, the immune escape of the Gamma variant (VOC), and the burden of post-COVID syndrome, this individual would have benefited from an additional dose of a heterologous vaccine

Profile of Central and Effector Memory T Cells in the Progression of Chronic Human Chagas Disease

Chagas disease is a parasitic infection caused by protozoan Trypanosoma cruzi that affects approximately 11 million people in Latin America. The involvement of the host's immune response on the development of severe forms of Chagas disease has not been fully elucidated. Studies on the immune response against T. cruzi infection show that the immunoregulatory mechanisms are necessary to prevent the deleterious effect of excessive immune response stimulation and consequently the fatal outcome of the disease. A recall response against parasite antigens observed in in vitro peripheral blood cell culture clearly demonstrates that memory response is generated during infection. Memory T cells are heterogeneous and differ in both the ability to migrate and exert their effector function. This heterogeneity is reflected in the definition of central (TCM) and effector memory (TEM) T cells. Our results suggest that a balance between regulatory and effectors T cells may be important for the progression and development of the disease. Furthermore, the high percentage of central memory CD4+ T cells in indeterminate patients after stimulation suggests that these cells may modulate host's inflammatory response by controlling cell migration to tissues and their effector role during chronic phase of the disease

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