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The social isolation imposed by COVID-19 pandemic can have a major impact on the mental health of dementia patients and their caregivers.

We aim to evaluate the neurological decline of patients with dementia and the caregivers' burden during the pandemic.

We performed a cross-sectional study. Caregivers of dementia patients following in the outpatient clinic were included. A structured telephone interview composed of the Neuropsychiatric Inventory Questionnaire (NPI-Q), Zarit Burden Interview (ZBI), Beck Depression (BDI) and Anxiety (BAI) Inventories to address cognitive, behavioral, and functional changes associated with social distancing during the Sars-Cov-2 outbreak. Patients were divided in two groups according to caregivers' report with perceived Altered Cognition (AC) and Stable Cognition (SC).

A total of 58 patients (median age 57 years [21-87], 58.6%females) and caregivers (median age 76.5 years [55-89], 79.3%females) were included. Metformin Cognitive decline was shown by most patients (53.4%), as well as behavioral symptoms (48.3%), especially apathy/depression (24.1%), and functional decline (34.5%). The AC group (n = 31) presented increased behavioral (67.7%versus 25.9%, p = 0.002) and functional (61.3%versus 3.7%, p < 0.001) changes when compared to the SC group. In the AC group, ZBI, BDI, NPI-Q caregiver distress, and NPI-Q patient's severity of symptoms scores were worse than the SC group (p < 0.005 for all).

Patients' neuropsychiatric worsening and caregiver burden were frequent during the pandemic. Worsening of cognition was associated with increased caregivers' psychological distress.

Patients' neuropsychiatric worsening and caregiver burden were frequent during the pandemic. Worsening of cognition was associated with increased caregivers' psychological distress.

Alzheimer's disease (AD) is the most common form of dementia and biomarkers are essential to help in the diagnosis of this disease. Image techniques and cerebrospinal fluid (CSF) biomarkers are limited in their use because they are expensive or invasive. Thus, the search for blood-borne biomarkers is becoming central to the medical community.

The main objective of this study is the evaluation of three serum proteins as potential biomarkers in AD patients.

We recruited 27 healthy controls, 19 mild cognitive impairment patients, and 17 AD patients. Using the recent A/T/N classification we split our population into two groups (AD and control). We used ELISA kits to determine Aβ42, tau, and p-tau in CSF and clusterin, PKR, and RAGE in serum.

The levels of serum clusterin, PKR, and RAGE were statistically different in the AD group compared to controls. These proteins showed a statistically significant correlation with CSF Aβ42. So, they were selected to generate an AD detection model showing an AUC-ROC of 0.971 (CI 95%, 0.931-0.998).

The developed model based on serum biomarkers and other co-variates could reflect the AD core pathology. So far, not one single blood-biomarker has been described, with effectiveness offering high sensitivity and specificity. We propose that the complexity of AD pathology could be reflected in a set of biomarkers also including clinical features of the patients.

The developed model based on serum biomarkers and other co-variates could reflect the AD core pathology. So far, not one single blood-biomarker has been described, with effectiveness offering high sensitivity and specificity. We propose that the complexity of AD pathology could be reflected in a set of biomarkers also including clinical features of the patients.

Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD) from cognitive normal (CN). This can make it challenging for clinicians to make efficient and objective clinical diagnoses. It is possible to reduce the number of variables needed to make a reasonably accurate classification using machine learning.

The goal of this study was to develop a deep learning algorithm to identify a few significant neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for diagnosis.

Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer's Disease Neuroimaging Initiative database. A neural network algorithm was trained on data split 90% for training and 10% testing using 10-fold cross-validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. We also evaluated five different feature selection methods.

The five feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes, Delayed total recall, Modified Preclinical Alzheimer Cognitive Composite with Trails test, Modified Preclinical Alzheimer Cognitive Composite with Digit test, and Mini-Mental State Examination. The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset.

The deep-learning algorithm and simplified risk score accurately classifies EMCI, LMCI, AD and CN patients using a few common neurocognitive tests.

The deep-learning algorithm and simplified risk score accurately classifies EMCI, LMCI, AD and CN patients using a few common neurocognitive tests.

Alzheimer's disease (AD) is a neurodegenerative pathology covering about 70%of all cases of dementia. Adenosine, a ubiquitous nucleoside, plays a key role in neurodegeneration, through interaction with four receptor subtypes. The A2A receptor is upregulated in peripheral blood cells of patients affected by Parkinson's and Huntington's diseases, reflecting the same alteration found in brain tissues. However, whether these changes are also present in AD pathology has not been determined.

In this study we verified any significant difference between AD cases and controls in both brain and platelets and we evaluated whether peripheral A2A receptors may reflect the status of neuronal A2A receptors.

We evaluated the expression of A2A receptors in frontal white matter, frontal gray matter, and hippocampus/entorhinal cortex, in postmortem AD patients and control subjects, through [3H]ZM 241385 binding experiments. The same analysis was performed in peripheral platelets from AD patients versus controls.

The expression of A2A receptors in frontal white matter, frontal gray matter, and hippocampus/entorhinal cortex, revealed a density (Bmax) of 174±29, 219±33, and 358±84 fmol/mg of proteins, respectively, in postmortem AD patients in comparison to 104±16, 103±19, and 121±20 fmol/mg of proteins in controls (p < 0.