Rodent Aβ Modulates the Solubility and Distribution of Amyloid Deposits in Transgenic Mice

The amino acid sequence of amyloid precursor protein (APP) is highly conserved, and age-related Abeta aggregates have been described in a variety of vertebrate animals, with the notable exception of mice and rats. Three amino acid substitutions distinguish mouse and human Abeta that might contribute to their differing properties in vivo. To examine the amyloidogenic potential of mouse Abeta, we studied several lines of transgenic mice overexpressing wild-type mouse amyloid precursor protein (moAPP) either alone or in conjunction with mutant PS1 (PS1dE9). Neither overexpression of moAPP alone nor co-expression with PS1dE9 caused mice to develop Alzheimer-type amyloid pathology by 24 months of age. We further tested whether mouse Abeta could accelerate the deposition of human Abeta by crossing the moAPP transgenic mice to a bigenic line expressing human APPswe with PS1dE9. The triple transgenic animals (moAPP x APPswe/PS1dE9) produced 20% more Abeta but formed amyloid deposits no faster and to no greater extent than APPswe/PS1dE9 siblings. Instead, the additional mouse Abeta increased the detergent solubility of accumulated amyloid and exacerbated amyloid deposition in the vasculature. These findings suggest that, although mouse Abeta does not influence the rate of amyloid formation, the incorporation of Abeta peptides with differing sequences alters the solubility and localization of the resulting aggregates

In vivo microdialysis reveals age-dependent decrease of brain interstitial fluid tau levels in P301S human tau transgenic mice

Although tau is a cytoplasmic protein, it is also found in brain extracellular fluids, e.g., CSF. Recent findings suggest that aggregated tau can be transferred between cells and extracellular tau aggregates might mediate spread of tau pathology. Despite these data, details of whether tau is normally released into the brain interstitial fluid (ISF), its concentration in ISF in relation to CSF, and whether ISF tau is influenced by its aggregation are unknown. To address these issues, we developed a microdialysis technique to analyze monomeric ISF tau levels within the hippocampus of awake, freely moving mice. We detected tau in ISF of wild-type mice, suggesting that tau is released in the absence of neurodegeneration. ISF tau was significantly higher than CSF tau and their concentrations were not significantly correlated. Using P301S human tau transgenic mice (P301S tg mice), we found that ISF tau is fivefold higher than endogenous murine tau, consistent with its elevated levels of expression. However, following the onset of tau aggregation, monomeric ISF tau decreased markedly. Biochemical analysis demonstrated that soluble tau in brain homogenates decreased along with the deposition of insoluble tau. Tau fibrils injected into the hippocampus decreased ISF tau, suggesting that extracellular tau is in equilibrium with extracellular or intracellular tau aggregates. This technique should facilitate further studies of tau secretion, spread of tau pathology, the effects of different disease states on ISF tau, and the efficacy of experimental treatments

Developmental trajectories of adolescent risky drinking: Predictors from the Drug Education in Victoria Schools (DEVS) study

With alcohol misuse one of the leading causes of disability among young Australians, determination of potential predictors of risky drinking trajectories of young people is crucial. This study aimed to identify risky drinking trajectories from early to mid-adolescence and to determine if membership of a harm minimization intervention, alcohol knowledge, attitudes towards alcohol and prevalence of alcohol harms would predict trajectory group membership. Longitudinal data from 1,746 students were used to identify alcohol consumption trajectory groups for both intervention and control students. Higher baseline knowledge predicted a higher, increasing, consumption trajectory for controls, whereas, safer attitudes at baseline was not associated with a higher, increasing trajectory. All other alcohol harms at baseline were strongly associated with higher consumption trajectories. The intervention group had fewer increasing trajectories and a lower level of consumption at the end of the program, suggesting the drug education program reduced the number of students who substantially increased their consumption over time, while at the same time reducing their level of consumption in relative terms. The consistency of better intervention student outcomes across all trajectories provides evidence that the drug education program was influential with all types of student drinkers and is suitable for universal deliver

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes