Neuroticism and white matter hyperintensities.

Neuroticism is a major risk factor for neurodegenerative disorders, such as Alzheimer's disease and related dementias. This study investigates whether neuroticism is associated with white matter hyperintensities and whether this measure of brain integrity is a mediator between neuroticism and cognitive function. Middle-aged and older adults from the UK Biobank (N = 40,602; aged 45-82 years, M = 63.97, SD = 7.66) provided information on demographic and health covariates, completed measures of neuroticism and cognition, and underwent magnetic resonance imaging from which the volume of white matter hyperintensities was derived. Regression analyses that included age and sex as covariates found that participants who scored higher on neuroticism had more white matter hyperintensities (ß = 0.024, 95% CI 0.015 to 0.032; p < .001), an association that was consistent across peri-ventricular and deep brain regions. The association was reduced by about 40% when accounting for vascular risk factors (smoking, obesity, diabetes, high blood pressure, heart attack, angina, and stroke). The association was not moderated by age, sex, college education, deprivation index, or APOE e4 genotype, and remained unchanged in sensitivity analyses that excluded individuals with dementia or those younger than 65. The mediation analysis revealed that white matter hyperintensities partly mediated the association between neuroticism and cognitive function. These findings identify white matter integrity as a potential neurobiological pathway that accounts for a small proportion of the association between neuroticism and cognitive health.

Facets of Personality and Risk of Cognitive Impairment: Longitudinal Findings in a Rural Community from Sardinia.

BACKGROUND: Few studies have examined the associations between personality facets and dementia risk and rarely included individuals from rural settings or with low education. OBJECTIVE: To examine the association between personality and the risk of cognitive impairment. METHODS: Participants (N = 1,668; age 50 to 94 at baseline; 56.4% women; 86.5% less than high school diploma) were from a rural region of Sardinia (Italy) who completed the Revised NEO Personality Inventory (NEO-PI-R) during the first wave (2001-2004) and the Mini-Mental State Examination (MMSE) at waves two to five (2005-2021). Cox regression was used to test personality and covariates as predictors of cognitive impairment based on MMSE education-adjusted cutoffs. RESULTS: During the up to 18-year follow-up (M = 10.38; SD = 4.76), 187 individuals (11.2%) scored as cognitively impaired. Participants with higher neuroticism (particularly the depression facet [HR = 1.22, 95% CI = 1.06-1.40]), and lower agreeableness (particularly the modesty facet [HR = 0.83, 95% CI = 0.71-0.97]) and lower conscientiousness (particularly the dutifulness facet [HR = 0.78, 95% CI = 0.67-0.92]) were at higher risk of cognitive impairment. Lower warmth ([HR = 0.75, 95% CI = 0.65-0.87], facet of extraversion) and ideas ([HR = 0.76, 95% CI = 0.65-0.89], facet of openness) were also associated with increased risk of impairment. These associations were virtually unchanged in models that accounted for other risk factors, including smoking, depression, obesity, hypertension, diabetes, and apolipoprotein E (APOE) ɛ4 carrier status. Across the five domains, sex and the APOE variant did not moderate the associations. CONCLUSION: In a sample with demographic characteristics underrepresented in dementia research, this study identifies personality domains and facets most relevant to the risk of cognitive impairment.

A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods.

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics.