Distress in neurocognitive disorders due to Alzheimer's disease and stroke

Positive irrational beliefs are associated with hypomanic personality

Primary irrational beliefs, such as demanding about attaining personal goals, are a common trans-diagnostic factor involved in many emotional disorders. Although Bipolar Disorder (BPD) is a severe emotional disorder, little is known about the role of primary irrational beliefs in the risk of BPD. Given that the risk for mania is related to responses to positive rather than adverse events, we developed a measure of irrational beliefs in response to cues of positive events. This is the first study that examines the relationship between positive primary irrational beliefs and the risk of BPD. 119 participants completed an online survey including measures for the risk of BPD, irrational beliefs, positive irrational beliefs, mania-related cognitions, and mood measures (depressive and manic mood). Results revealed significant associations between the risk of BPD and positive primary irrational beliefs, irrational beliefs, positive generalization, and mood. Regression analyses revealed that positive primary irrational beliefs, such as demanding to attain significant goals in response to cues for positive events, uniquely predict the risk for BPD independently of mood, mania-related cognitions and irrational beliefs. These findings encourage the treatment approaches focused on restructuring primary irrational beliefs in response to positive situations to reduce the risk of BPD.

The Analysis of Oxidative Stress Markers May Increase the Accuracy of the Differential Diagnosis of Alzheimer's Disease with and without Depression

Introduction

The aim of work is to assess the usefulness of oxidative stress parameters in the differential diagnosis of dementia of the Alzheimer’s type and dementia of the Alzheimer’s type with coexisting depression.

Methods

The study involved three groups of people: patients with Alzheimer’s disease (AD) (AD; N=27), patients with Alzheimer’s disease and depression (D) (AD+D; N=30), and a control group that consisted of people without dementia and without depression (C; N=24). The assessment of cognitive functioning was carried out using among alia, Auditory Verbal Learning Test and Verbal Fluency Test. Furthermore, we determined the activity of superoxide dismutase (SOD-1) and superoxide anion radical.

Results

Multiple models with different combinations of independent variables showed that SOD together with Rey delayed recall were the best significant predictors of AD with the area under curve (AUC) of 0.893 (p = 0.001) and superoxide anion radical (O2^•−) together with verbal fluency – sharp objects were the best significant predictors of AD +D diagnosis with the AUC of 0.689 (p = 0.034).

Conclusion

This study confirmed the value of neuropsychological diagnosis and analysis of oxidative stress markers in the diagnosis of AD and major depressive disorder (MDD) in the course of AD. The combination of the use of biochemical markers and neuropsychological tests seems particularly important for differential diagnosis.

Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network

One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.

Effects of Anti-Inflammatory Treatment and Surgical Intervention on Endothelial Glycocalyx, Peripheral and Coronary Microcirculatory Function and Myocardial Deformation in Inflammatory Bowel Disease Patients: A Two-Arms Two-Stage Clinical Trial

Sixty inflammatory bowel disease (IBD) patients (45 Crohn disease and 15 ulcerative colitis, 40 ± 13 years, 53% male) were examined at baseline and 4 months after intervention (surgical (35 patients) or anti-TNFa treatment (25 patients)). IBD severity, using Mayo score, Harvey–Bradshaw Index (HBI) and biomarkers, was correlated with cardiovascular markers. At baseline, the disease severity, the white blood cells (WBC) values and the reducing power (RP) were significantly correlated with the aortic pulse wave velocity (PWV) (r = 0.4, r = 0.44 and r = 0.48, p < 0.05) and the lateral mitral E’ velocity (r = 0.35, p < 0.05 and r = 0.3, p < 0.05). Four months after intervention, there was a reduction of WBC (1962.8/mm³ ± 0.425/mm³, p < 0.001), C-reactive protein (CRP) (8.1 mg/L ± 1.7 mg/L, p < 0.001), malondialdehyde (MDA) (0.81 nmol/mg ± 0.37, p < 0.05) and glycocalyx perfused boundary region (PBR 5-25) (0.24 μm ± 0.05 μm, p < 0.01). Moreover, the brachial flow mediated dilatation (FMD), the coronary flow reserve (CFR) and the left ventricle global longitudinal strain (LV GLS) were significantly improved for both groups (4.5% ± 0.9%, 0.55 ± 0.08, 1.4% ± 0.35%, p < 0.01), while a more significant improvement of PWV/GLS was noticed in the anti-TNFa group. IBD severity is associated with vascular endothelial, cardiac diastolic, and coronary microcirculatory dysfunction. The systemic inflammatory inhibition and the local surgical intervention lead to significant improvement in endothelial function, coronary microcirculation and myocardial deformation.

Association between Serum Concentrations of Apolipoprotein A-I (ApoA-I) and Alzheimer's Disease: Systematic Review and Meta-Analysis

BACKGROUND

A wealth of experimental and epidemiological evidence suggest that Apolipoprotein A-I (ApoA-I), the main protein constituent of high-density lipoprotein (HDL), may protect against Alzheimer disease (AD). To investigate this potential role, we conducted a meta-analysis of the published studies on the relationship between serum ApoA-I and AD occurrence.

METHODS

We screened MEDLINE, EMBASE, Web of Science, and Scopus, for cross-sectional studies published from inception to 1 March 2021, comparing the ApoA-I serum levels between patients with AD and cognitively normal controls.

RESULTS

From an initial screening of 245 articles, 5 studies, including 397 AD patients (mean age 75.0 years, 234 females) and 367 controls (mean age 69.2 years, 182 females), met the inclusion criteria. Compared to healthy controls, AD subjects had a lower ApoA-I serum level. The pooled weighted mean difference from a random-effects model was -0.31 g/L (p < 0.0001) (95% Confidence Interval: [-0.62-0.01], with high heterogeneity (I2 = 100%). The Egger's test confirmed an absence of publication bias (t = 0.62, p = 0.576).

CONCLUSIONS

Our study showed that AD patients present lower serum levels of ApoA-I compared to cognitively normal individuals. Further studies on large population samples are required to support this finding.

A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.