Immunogenicity of COVID-19 adsorbed inactivated vaccine (CoronaVac) and additional doses of mRNA BNT162b2 vaccine in immunocompromised adults compared with immunocompetent persons

Inactivated COVID-19 vaccines data in immunocompromised individuals are scarce. This trial assessed the immunogenicity of two CoronaVac doses and additional BNT162b2 mRNA vaccine doses in immunocompromised (IC) and immunocompetent (H) individuals. Adults with solid organ transplant (SOT), hematopoietic stem cell transplant, cancer, inborn immunity errors or rheumatic diseases were included in the IC group. Immunocompetent adults were used as control group for comparison. Participants received two CoronaVac doses within a 28-day interval. IC received two additional BNT162b2 doses and H received a third BNT162b2 dose (booster). Blood samples were collected at baseline, 28 days after each dose, pre-booster and at the trial end. We used three serological tests to detect antibodies to SARS-CoV-2 nucleocapsid (N), trimeric spike (S), and receptor binding domain (RBD). Outcomes included seroconversion rates (SCR), geometric mean titers (GMT) and GMT ratio (GMTR). A total of 241 IC and 100 H adults participated in the study. After two CoronaVac doses, IC had lower SCR than H: anti-N, 33.3% vs 79%; anti-S, 33.8% vs 86%, and anti-RBD, 48.5% vs 85%, respectively. IC also showed lower GMT than H: anti-N, 2.3 vs 15.1; anti-S, 58.8 vs 213.2 BAU/mL; and anti-RBD, 22.4 vs 168.0 U/mL, respectively. After the 3rd and 4th BNT162b2 doses, IC had significant anti-S and anti-RBD seroconversion, but still lower than H after the 3rd dose. After boosting, GMT increased in IC, but remained lower than in the H group. CoronaVac two-dose schedule immunogenicity was lower in IC than in H. BNT162b2 heterologous booster enhanced immune response in both groups

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