Biocompatible Nanocomplexes for Molecular Targeted MRI Contrast Agent

Accurate diagnosis in early stage is vital for the treatment of Hepatocellular carcinoma. The aim of this study was to investigate the potential of poly lactic acid–polyethylene glycol/gadolinium–diethylenetriamine-pentaacetic acid (PLA–PEG/Gd–DTPA) nanocomplexes using as biocompatible molecular magnetic resonance imaging (MRI) contrast agent. The PLA–PEG/Gd–DTPA nanocomplexes were obtained using self-assembly nanotechnology by incubation of PLA–PEG nanoparticles and the commercial contrast agent, Gd–DTPA. The physicochemical properties of nanocomplexes were measured by atomic force microscopy and photon correlation spectroscopy. The T1-weighted MR images of the nanocomplexes were obtained in a 3.0 T clinical MR imager. The stability study was carried out in human plasma and the distribution in vivo was investigated in rats. The mean size of the PLA–PEG/Gd–DTPA nanocomplexes was 187.9 ± 2.30 nm, and the polydispersity index was 0.108, and the zeta potential was −12.36 ± 3.58 mV. The results of MRI test confirmed that the PLA–PEG/Gd–DTPA nanocomplexes possessed the ability of MRI, and the direct correlation between the MRI imaging intensities and the nano-complex concentrations was observed (r = 0.987). The signal intensity was still stable within 2 h after incubation of the nanocomplexes in human plasma. The nanocomplexes gave much better image contrast effects and longer stagnation time than that of commercial contrast agent in rat liver. A dose of 0.04 mmol of gadolinium per kilogram of body weight was sufficient to increase the MRI imaging intensities in rat livers by five-fold compared with the commercial Gd–DTPA. PLA–PEG/Gd–DTPA nanocomplexes could be prepared easily with small particle sizes. The nanocomplexes had high plasma stability, better image contrast effect, and liver targeting property. These results indicated that the PLA–PEG/Gd–DTPA nanocomplexes might be potential as molecular targeted imaging contrast agent

Synthesis of Fluorine-18 Functionalized Nanoparticles for use as in vivo Molecular Imaging Agents

Nanoparticles containing fluorine-18 were prepared from block copolymers made by ring opening metathesis polymerization (ROMP). Using the fast initiating ruthenium metathesis catalyst (H_2IMes)(pyr)_2(Cl)_2Ru=CHPh, low polydispersity amphiphilic block copolymers were prepared from a cinnamoyl-containing hydrophobic norbornene monomer and a mesyl-terminated PEG-containing hydrophilic norbornene monomer. Self-assembly into micelles and subsequent cross-linking of the micelle cores by light-activated dimerization of the cinnamoyl groups yielded stable nanoparticles. Incorporation of fluorine-18 was achieved by nucleophilic displacement of the mesylates by the radioactive fluoride ion with 31% incorporation of radioactivity. The resulting positron-emitting nanoparticles are to be used as in vivo molecular imaging agents for use in tumor imaging

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

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

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

Development and validation of a fluorimetric method to determine curcumin in lipid and polymeric nanocapsule suspensions

A simple, rapid, and sensitive fluorimetric method was developed and validated to quantify curcumin in lipid and polymeric nanocapsule suspensions, using acetonitrile as a solvent. The excitation and emission wavelengths were set at 397 nm and 508 nm, respectively. The calibration graph was linear from 0.1 to 0.6 µg/mL with a correlation coefficient of 0.9982. The detection and quantitation limits were 0.03 and 0.10 µg/mL, respectively. The validation results confirmed that the developed method is specific, linear, accurate, and precise for its intended use. The current method was successfully applied to the evaluation of curcumin content in lipid and polymeric nanocapsule suspensions during the early stage of formulation development.Um método fluorimétrico simples, rápido e sensível foi desenvolvido e validado para quantificação da curcumina em suspensões de nanocápsulas lipídicas e poliméricas, usando acetonitrila como solvente. Os comprimentos de onda de excitação e emissão foram 397 nm e 508 nm, respectivamente. Nas condições testadas, a curva de calibração demonstrou-se linear na faixa de 0,1 a 0,6 µg/mL, exibindo coeficiente de correlação de 0,9982. Os limites de detecção e quantificação foram 0,03 e 0,10 µg/mL, respectivamente. Os resultados da validação confirmaram que o método desenvolvido é específico, linear, exato e preciso para o uso proposto. O presente método foi aplicado com sucesso para a avaliação do teor de curcumina nas suspensões de nanocápsulas lipídicas e poliméricas durante o estágio inicial do desenvolvimento da formulação