Estimating loss of Brucella abortus antibodies from age-specific serological data in elk

Serological data are one of the primary sources of information for disease monitoring in wildlife. However, the duration of the seropositive status of exposed individuals is almost always unknown for many free-ranging host species. Directly estimating rates of antibody loss typically requires difficult longitudinal sampling of individuals following seroconversion. Instead, we propose a Bayesian statistical approach linking age and serological data to a mechanistic epidemiological model to infer brucellosis infection, the probability of antibody loss, and recovery rates of elk (Cervus canadensis) in the Greater Yellowstone Ecosystem. We found that seroprevalence declined above the age of ten, with no evidence of disease-induced mortality. The probability of antibody loss was estimated to be 0.70 per year after a five-year period of seropositivity and the basic reproduction number for brucellosis to 2.13. Our results suggest that individuals are unlikely to become re-infected because models with this mechanism were unable to reproduce a significant decline in seroprevalence in older individuals. This study highlights the possible implications of antibody loss, which could bias our estimation of critical epidemiological parameters for wildlife disease management based on serological data

A blood-based signature of cerebrospinal fluid A beta(1-42) status

It is increasingly recognized that Alzheimer's disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1-42 (Aβ1-42) may be an earlier indicator of Alzheimer's disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual's CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1-42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1-42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1-42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1-42 levels and that the resulting model also validates reasonably across PET Aβ1-42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1-42 status, the earliest risk indicator for AD, with high accuracy