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BackgroundAdverse effects of COVID-19 vaccination on human menstrual cycle characteristics have been observed, but limited data are available on the relationship between COVID-19 vaccination status and birth rates.ObjectivesTherefore, we used nationwide data from the Czech Republic to examine rates of successful conceptions (SCs), that is, conceptions leading to live births 9 months later, for women who were either vaccinated or unvaccinated against COVID-19 before SC.MethodsSummary monthly COVID-19 vaccination and birth data for women in the Czech Republic aged 18-39 years were retrieved for the period from January 2021 to December 2023. The numbers of SCs per month per 1000 women were calculated for preconception-vaccinated or unvaccinated women, respectively, as well as the number of SCs per month per 1000 women for all women aged 18-39 years.ResultsDuring the study period, there were approximately 1,300,000 women aged 18-39 years in the Czech Republic, and the proportion of COVID-19-vaccinated women increased from January 2021 until reaching a steady state of around 70% by the end of 2021. At least from June 2021, SCs per 1000 women were considerably lower for women who were vaccinated, compared to those that were unvaccinated, before SC. Furthermore, SC rates for the vaccinated group were much lower than expected based on their proportion of the total population.ConclusionsIn the Czech Republic, SC rates were substantially lower for women vaccinated against COVID-19 before SC than for those who were not vaccinated. These hypothesis-generating and preliminary results call for further studies of the potential influence of COVID-19 vaccination on human fecundability and fertility.
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http://dx.doi.org/10.1177/09246479251353384 | DOI Listing |
Clin Transplant
September 2025
Cardiac Transplant Unit, La Timone Hospital, Aix-Marseille University, Marseille, France.
Pediatr Infect Dis J
September 2025
From the School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia.
Background: Obesity was a risk factor for severe COVID-19 in children during early outbreaks of ancestral SARS-CoV-2 and the Delta variant. However, the relationship between obesity and COVID-19 severity during the Omicron wave remains unclear.
Methods: This multicenter, observational study included polymerase chain r eaction-confirmed SARS-CoV-2-infected children and adolescents from Australia, Brazil, Italy, Portugal, Switzerland, Thailand, the United Kingdom and the United States hospitalized between January 1, 2020, and March 31, 2022.
Pediatr Infect Dis J
September 2025
From the Department of Pediatric Intensive Care Unit, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Background: Antiviral drugs and coronavirus disease 2019 (COVID-19) vaccines have significantly reduced COVID-19-related hospitalizations and deaths in infected children. However, COVID-19 continues to pose a major mortality risk in young children. High-sensitive cardiac troponin (Hs-cTn) is a specific marker of myocardial cell damage.
View Article and Find Full Text PDFBioinformatics
September 2025
Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, United Kingdom.
Summary: In Bayesian phylogenetic and phylodynamic studies it is common to summarise the posterior distribution of trees with a time-calibrated summary phylogeny. While the maximum clade credibility (MCC) tree is often used for this purpose, we here show that a novel summary tree method-the highest independent posterior subtree reconstruction, or HIPSTR-contains consistently higher supported clades over MCC. We also provide faster computational routines for estimating both summary trees in an updated version of TreeAnnotator X, an open-source software program that summarizes the information from a sample of trees and returns many helpful statistics such as individual clade credibilities contained in the summary tree.
View Article and Find Full Text PDFJ Clin Invest
September 2025
The University of Texas at Austin, Austin, United States of America.
Background: Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which debilitating symptoms persist for at least three months. Elucidating biologic underpinnings of LC could identify therapeutic opportunities.
Methods: We utilized machine learning methods on biologic analytes provided over 12-months after hospital discharge from >500 COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor", trained on patient-reported physical function survey scores.