Detection and quantification of Aβ−3–40 (APP669‐711) in cerebrospinal fluid

Neurochemical biomarkers can support the diagnosis of Alzheimer’s disease and may facilitate clinical trials. In blood plasma, the ratio of the amyloid-β (Aβ) peptides Aβ−3– 40/Aβ1–42 can predict cerebral amyloid-β pathology with high accuracy (Nakamura et al., 2018). Whether or not Aβ−3–40 (aka. amyloid precursor protein (APP) 669– 711) is also present in cerebrospinal fluid (CSF) is not clear. Here, we investigated whether Aβ−3–40 can be detected in CSF and to what extent the CSF Aβ−3–40/Aβ42 ratio is able to differentiate between individuals with or without amyloid-β positron emission tomography (PET) evidence of brain amyloid. The occurrence of Aβ−3–40 in human CSF was assessed by immunoprecipitation followed by mass spectrometry. For quantifying the CSF concentrations of Aβ−3–40 in 23 amyloid PET- negative and 17 amyloid PET- positive subjects, we applied a sandwich-type immunoassay. Our findings provide clear evidence of the presence of Aβ−3–40 and Aβ−3–38 in human CSF. While there was no statistically significant difference in the CSF concentration of Aβ−3–40 between the two diagnostic groups, the CSF Aβ−3–40/Aβ42 ratio was increased in the amyloid PET- positive individuals. We conclude that Aβ−3– 40 appears to be a regular constituent of CSF and may potentially serve to accentuate the selec- tive decrease in CSF Aβ42 in Alzheimer's disease

A novel deterministic forecast model for COVID-19 epidemic based on a single ordinary integro-differential equation

In this paper we present a new approach to deterministic modelling of COVID-19 epidemic. Our model dynamics is expressed by a single prognostic variable which satisfies an integro-differential equation. All unknown parameters are described with a single, time-dependent variable R(t). We show that our model has similarities to classic compartmental models, such as SIR, and that the variable R(t) can be interpreted as a generalized effective reproduction number. The advantages of our approach are the simplicity of having only one equation, the numerical stability due to an integral formulation and the reliability since the model is formulated in terms of the most trustable statistical data variable: the number of cumulative diagnosed positive cases of COVID-19. Once this dynamic variable is calculated, other non-dynamic variables, such as the number of heavy cases (hospital beds), the number of intensive-care cases (ICUs) and the fatalities, can be derived from it using a similarly stable, integral approach. The formulation with a single equation allows us to calculate from real data the values of the sample effective reproduction number, which can then be fitted. Extrapolated values of R(t) can be used in the model to make reliable forecasts, though under the assumption that measures for reducing infections are maintained. We have applied our model to more than 15 countries and the ongoing results are available on a web-based platform [1]. In this paper, we focus on the data for two exemplary countries, Italy and Germany, and show that the model is capable of reproducing the course of the epidemic in the past and forecasting its course for a period of four to five weeks with a reasonable numerical stability