Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Genome-Wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up

Rationale: The evidence supporting an association between traffic-related air pollution exposure and incident childhood asthma is inconsistent and may depend on genetic factors. Objectives: To identify gene–environment interaction effects on childhood asthma using genome-wide single-nucleotide polymorphism (SNP) data and air pollution exposure. Identified loci were further analyzed at epigenetic and transcriptomic levels. Methods: We used land use regression models to estimate individual air pollution exposure (represented by outdoor NO2 levels) at the birth address and performed a genome-wide interaction study for doctors’ diagnoses of asthma up to 8 years in three European birth cohorts (n = 1,534) with look-up for interaction in two separate North American cohorts, CHS (Children’s Health Study) and CAPPS/SAGE (Canadian Asthma Primary Prevention Study/Study of Asthma, Genetics and Environment) (n = 1,602 and 186 subjects, respectively). We assessed expression quantitative trait locus effects in human lung specimens and blood, as well as associations among air pollution exposure, methylation, and transcriptomic patterns. Measurements and Main Results: In the European cohorts, 186 SNPs had an interaction P < 1 × 10−4 and a look-up evaluation of these disclosed 8 SNPs in 4 loci, with an interaction P < 0.05 in the large CHS study, but not in CAPPS/SAGE. Three SNPs within adenylate cyclase 2 (ADCY2) showed the same direction of the interaction effect and were found to influence ADCY2 gene expression in peripheral blood (P = 4.50 × 10−4). One other SNP with P < 0.05 for interaction in CHS, rs686237, strongly influenced UDP-Gal:betaGlcNAc β-1,4-galactosyltransferase, polypeptide 5 (B4GALT5) expression in lung tissue (P = 1.18 × 10−17). Air pollution exposure was associated with differential discs, large homolog 2 (DLG2) methylation and expression. Conclusions: Our results indicated that gene–environment interactions are important for asthma development and provided supportive evidence for interaction with air pollution for ADCY2, B4GALT5, and DLG2

A Novel Docetaxel-Loaded Poly (ε-Caprolactone)/Pluronic F68 Nanoparticle Overcoming Multidrug Resistance for Breast Cancer Treatment

Multidrug resistance (MDR) in tumor cells is a significant obstacle to the success of chemotherapy in many cancers. The purpose of this research is to test the possibility of docetaxel-loaded poly (ε-caprolactone)/Pluronic F68 (PCL/Pluronic F68) nanoparticles to overcome MDR in docetaxel-resistance human breast cancer cell line. Docetaxel-loaded nanoparticles were prepared by modified solvent displacement method using commercial PCL and self-synthesized PCL/Pluronic F68, respectively. PCL/Pluronic F68 nanoparticles were found to be of spherical shape with a rough and porous surface. The nanoparticles had an average size of around 200 nm with a narrow size distribution. The in vitro drug release profile of both nanoparticle formulations showed a biphasic release pattern. There was an increased level of uptake of PCL/Pluronic F68 nanoparticles in docetaxel-resistance human breast cancer cell line, MCF-7 TAX30, when compared with PCL nanoparticles. The cytotoxicity of PCL nanoparticles was higher than commercial Taxotere®in the MCF-7 TAX30 cell culture, but the differences were not significant (p > 0.05). However, the PCL/Pluronic F68 nanoparticles achieved significantly higher level of cytotoxicity than both of PCL nanoparticles and Taxotere®(p < 0.05), indicating docetaxel-loaded PCL/Pluronic F68 nanoparticles could overcome multidrug resistance in human breast cancer cells and therefore have considerable potential for treatment of breast cancer

Machine learning‐based classification of Alzheimer's disease and its at‐risk states using personality traits, anxiety, and depression

Background Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages. Methods In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). Results Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets. Conclusion Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages

Perivascular space enlargement accelerates in ageing and Alzheimer’s disease pathology: evidence from a three-year longitudinal multicentre study

Background Perivascular space (PVS) enlargement in ageing and Alzheimer’s disease (AD) and the drivers of such a structural change in humans require longitudinal investigation. Elucidating the effects of demographic factors, hypertension, cerebrovascular dysfunction, and AD pathology on PVS dynamics could inform the role of PVS in brain health function as well as the complex pathophysiology of AD. Methods We studied PVS in centrum semiovale (CSO) and basal ganglia (BG) computationally over three to four annual visits in 503 participants (255 females; meanage = 70.78 ± 5.78) of the ongoing observational multicentre “DZNE Longitudinal Cognitive Impairment and Dementia Study” (DELCODE) cohort. We analysed data from subjects who were cognitively unimpaired (n = 401), had amnestic mild cognitive impairment (n = 71), or had AD (n = 31). We used linear mixed-effects modelling to test for changes of PVS volumes in relation to cross-sectional and longitudinal age, as well as sex, years of education, hypertension, white matter hyperintensities, AD diagnosis, and cerebrospinal-fluid-derived amyloid (A) and tau (T) status (available for 46.71%; A-T-/A + T-/A + T + n = 143/48/39). Results PVS volumes increased significantly over follow-ups (CSO: B = 0.03 [0.02, 0.05], p  A-T-, pFDR = 0.004) or who were amyloid positive but tau negative (A + T +  > A + T-, pFDR = 0.07). CSO-PVS volumes increased at a faster rate with amyloid positivity as compared to amyloid negativity (A + T-/A + T +  > A-T-, pFDR = 0.021). Conclusion Our longitudinal evidence supports the relevance of PVS enlargement in presumably healthy ageing as well as in AD pathology. We further discuss the region-specific involvement of white matter hyperintensities and neurotoxic waste accumulation in PVS enlargement and the possibility of additional factors contributing to PVS progression. A comprehensive understanding of PVS dynamics could facilitate the understanding of pathological cascades and might inform targeted treatment strategies

A 6-items questionnaire (6-QMD) captures a Mediterranean like dietary pattern and is associated with memory performance and hippocampal volume in elderly and persons at risk for Alzheimer’s disease

BACKGROUND: There is evidence that adherence to Mediterranean-like diet reduces cognitive decline and brain atrophy in Alzheimer's disease (AD). However, lengthy dietary assessments, such as food frequency questionnaires (FFQs), discourage more frequent use. OBJECTIVE: Here we aimed to validate a 6-items short questionnaire for a Mediterranean-like diet (6-QMD) and explore its associations with memory performance and hippocampal atrophy in healthy elders and individuals at risk for AD. METHODS: We analyzed 938 participants (N = 234 healthy controls and N = 704 participants with an increased AD risk) from the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). The 6-QMD was validated against the Mediterranean Diet (MeDi) score and the Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) score, both derived from a detailed FFQ. Furthermore, associations between the 6-QMD and memory function as well as hippocampal atrophy were evaluated using linear regressions. RESULTS: The 6-QMD was moderately associated with the FFQ-derived MeDi adherence score (ρ = 0.25, p &lt; 0.001) and the MIND score (ρ = 0.37, p= &lt; 0.001). Higher fish and olive oil consumption and lower meat and sausage consumption showed significant associations in a linear regression, adjusted for diagnosis, age, sex and education, with memory function (β = 0.1, p = 0.008) and bilateral hippocampal volumes (left: β = 0.15, p &lt; 0.001); (right: β = 0.18, p &lt; 0.001)). CONCLUSIONS: The 6-QMD is a useful and valid brief tool to assess the adherence to MeDi and MIND diets, capturing associations with memory function and brain atrophy in healthy elders and individuals at increased AD dementia risk, making it a valid alternative in settings with time constraints

PEGylated systems in pharmaceutics

This review addresses the use of poly(ethylene glycol) (PEG) and PEG conjugation for the design of novel dosage forms and the modification of biomolecules. The peculiarities of PEGylated nanoparticles, liposomes, proteins, enzymes, and small drug and polyelectrolyte molecules and their influence on systemic drug delivery, including overcoming of various biological barriers and adhesion to mucosal tissues (mucoadhesion), are considered

The use of Brazilian vegetable oils in nanoemulsions: an update on preparation and biological applications

ABSTRACT Vegetable oils present important pharmacological properties, which gained ground in the pharmaceutical field. Its encapsulation in nanoemulsions is considered a promising strategy to facilitate the applicability of these natural compounds and to potentiate the actions. These formulations offer several advantages for topical and systemic delivery of cosmetic and pharmaceutical agents including controlled droplet size, protection of the vegetable oil to photo, thermal and volatilization instability and ability to dissolve and stabilize lipophilic drugs. For these reasons, the aim of this review is to report on some characteristics, preparation methods, applications and especially analyze recent research available in the literature concerning the use of vegetable oils with therapeutic characteristics as lipid core in nanoemulsions, specially from Brazilian flora, such as babassu (Orbignya oleifera), aroeira (Schinus molle L.), andiroba (Carapa guaianiensis), casca-de-anta (Drimys brasiliensis Miers), sucupira (Pterodon emarginatus Vogel) and carqueja doce (Stenachaenium megapotamicum) oils