Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection.

Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson & Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around - 0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022.

Rate of brain aging and APOE ε4 are synergistic risk factors for Alzheimer's disease.

Advanced age and the APOE ε4 allele are the two biggest risk factors for Alzheimer's disease (AD) and declining cognitive function. We describe a universal gauge to measure molecular brain age using transcriptome analysis of four human postmortem cohorts (n = 673, ages 25-97) free of neurological disease. In a fifth cohort of older subjects with or without neurological disease (n = 438, ages 67-108), we show that subjects with brains deviating in the older direction from what would be expected based on chronological age show an increase in AD, Parkinson's disease, and cognitive decline. Strikingly, a younger molecular age (-5 yr than chronological age) protects against AD even in the presence of APOE ε4 An established DNA methylation gauge for age correlates well with the transcriptome gauge for determination of molecular age and assigning deviations from the expected. Our results suggest that rapid brain aging and APOE ε4 are synergistic risk factors, and interventions that slow aging may substantially reduce risk of neurological disease and decline even in the presence of APOE ε4.

Avoiding a lost generation of scientists.

By sharing their experiences, early-career scientists can help to make the case for increased government funding for researchers.