Akhtardouglas3316

Apolipoprotein E gene (APOE) ɛ4 allele increases the risk for Alzheimer's disease (AD). Furthermore, among patients with cognitive impairment, longer sleep duration is associated with worse cognitive performance. To date, literature examining the associations between APOE ɛ4 allele and objective sleep duration is limited.

Our aim was to assess the association between APOE ɛ4 and objective sleep duration, among patients with mild cognitive impairment (MCI) and AD. A sub-sample of 89 patients with AD (n = 49) and MCI (n = 40) were recruited from a large, population-based cohort of 3,140 elders (>60 years) residing on Crete, Greece.

All participants underwent medical history/physical examination, extensive neuropsychiatric and neuropsychological evaluation, 3-day 24 h actigraphy and APOE ɛ4 allele genotyping. Comparisons of sleep duration variables between APOE ɛ4 allele carriers and non-carriers were assessed using ANCOVA, controlling for confounders.

The sample included 18 APOE ɛ4 carriers and 71 no pre-clinical marker associated with worse prognosis in elderly with cognitive impairment.

Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer's disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold on the one hand, non-modifiable factors and on the other, modifiable.

This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD).

This research is based on an observational-analytical design. read more Two research groups were established MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD.

Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier.

ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

With the more widespread use of 18F-radioligand-based amyloid-β (Aβ) PET-CT imaging, we evaluated Aβ binding and the utility of neocortical 18F-Flutemetamol standardized uptake value ratio (SUVR) as a biomarker.

18F-Flutemetamol SUVR was used to differentiate 1) mild cognitive impairment (MCI) from Alzheimer's disease (AD), and 2) MCI from other non-AD dementias (OD).

109 patients consecutively recruited from a University memory clinic underwent clinical evaluation, neuropsychological test, MRI and 18F-Flutemetamol PET-CT. The diagnosis was made by consensus of a panel consisting of 1 neuroradiologist and 2 geriatricians. The final cohort included 13 subjective cognitive decline (SCD), 22 AD, 39 MCI, and 35 OD. Quantitative analysis of 16 region-of-interests made by Cortex ID software (GE Healthcare).

The global mean 18F-Flutemetamol SUVR in SCD, MCI, AD, and OD were 0.50 (SD-0.08), 0.53 (SD-0.16), 0.76 (SD-0.10), and 0.56 (SD-0.16), respectively, with SUVR in SCD and MCI and OD being significantly lo current guidelines proposed by Amyloid Imaging Task Force.

Hope for future treatments to prevent or slow down dementia motivates researchers to strive for ever-earlier diagnoses of Alzheimer's disease (AD) based on biomarkers, even before symptoms occur. But is a biomarker-based early diagnosis desirable in clinical practice?

This study explores the ethical considerations that shape current clinical practice regarding early AD diagnostics and the use of biomarkers.

In this qualitative study, Dutch physicians were interviewed. Topics included physicians' views concerning early AD diagnosis in persons with no or mild cognitive impairment, physicians' considerations regarding current and expected future practices of early AD diagnosis, the use of biomarkers, and the use of the concepts preclinical and prodromal AD. We analyzed the transcripts using directed content analysis.

15 general practitioners, neurologists, and geriatricians in the Netherlands were interviewed. Most of them interpreted an early AD diagnosis with an early diagnosis of dementia. We identifil.Traffic-related air pollution is ubiquitous and almost impossible to avoid. It is important to understand the role that traffic-related air pollution may play in neurodegenerative diseases, such as dementia, Alzheimer's disease, and Parkinson's disease, particularly among older populations and at-risk groups. There is a growing interest in this area among the environmental epidemiology literature and the body of evidence identifying this role is emerging and strengthening. This review focuses on the principal components of traffic-related air pollutants (particulate matter and nitrogen oxides) and the epidemiological evidence of their contribution to common neurodegenerative diseases. All studies reported are currently observational in nature and there are mixed findings depending on the study design, assessment of traffic-related air pollutant levels, assessment of the neurodegenerative disease outcome, time period of assessment, and the role of confounding environmental factors and at-risk genetic characteristics. All current studies have been conducted in income-rich countries where traffic-related air pollution levels are relatively low. Additional longer-term studies are needed to confirm the levels of risk, consider other contributing environmental factors and to be conducted in settings where air pollution exposures are higher and at-risk populations reside and work. Better understanding of these relationships will help inform the development of preventive measures and reduce chronic cognitive and physical health burdens (cost, quality of life) at personal and societal levels.