Behavioural and psychological symptoms of dementia in Down syndrome: Early indicators of clinical Alzheimer's disease?

Behavioural and Psychological Symptoms of Dementia (BPSD) are a core symptom of dementia and are associated with suffering, earlier institutionalization and accelerated cognitive decline for patients and increased caregiver burden. Despite the extremely high risk for Down syndrome (DS) individuals to develop dementia due to Alzheimer's disease (AD), BPSD have not been comprehensively assessed in the DS population. Due to the great variety of DS cohorts, diagnostic methodologies, sub-optimal scales, covariates and outcome measures, it is questionable whether BPSD have always been accurately assessed. However, accurate recognition of BPSD may increase awareness and understanding of these behavioural aberrations, thus enabling adaptive caregiving and, importantly, allowing for therapeutic interventions. Particular BPSD can be observed (long) before the clinical dementia diagnosis and could therefore serve as early indicators of those at risk, and provide a new, non-invasive way to monitor, or at least give an indication of, the complex progression to dementia in DS. Therefore, this review summarizes and evaluates the rather limited knowledge on BPSD in DS and highlights its importance and potential for daily clinical practice

Competitive tendering and deregulation in the British bus

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5-HT7 receptors in Alzheimer's disease

Even though the involvement of serotonin (5-hydroxytryptamine; 5-HT) and its receptors in Alzheimer’s disease (AD) is widely accepted, data on the expression and the role of 5-HT7 receptors in AD is relatively limited. Therefore, the objective of the present work was to study the expression of serotonergic 5-HT7 receptors in postmortem samples of AD brains and correlate it with neurotransmitter levels, cognition and behavior. The study population consisted of clinically well-characterized and neuropathologically confirmed AD patients (n = 42) and age-matched control subjects (n = 18). Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and high-performance liquid chromatography were performed on Brodmann area (BA) 7, BA10, BA22, BA24, hippocampus, amygdala, thalamus and cerebellum to measure mRNA levels of 5-HT7 receptors (HTR7), as well as the concentrations of various monoamine neurotransmitters and their metabolites. Decreased levels of HTR7 mRNA were observed in BA10. A significant association was observed between HTR7 levels in BA10 and BEHAVE-AD cluster B (hallucinations) (rs(28) = 0.444, P < 0.05). In addition, a negative correlation was observed between HTR7 levels in BA10 and both MHPG concentrations in this brain region (rs(45) = -0.311; P < 0.05), and DOPAC levels in the amygdala (rs(42) = -0.311; P < 0.05). Quite sur- prisingly, no association was found between HTR7 levels and cognitive status. Altogether, this study supports the notion of the involvement of 5-HT7 receptors in psychotic symptoms in AD, suggesting the interest of testing antagonist acting at this receptor to specifically treat psychotic symptoms in this illness

Age- and Disease-Specific Changes of the Kynurenine Pathway in Parkinson's and Alzheimer's Disease

The Kynurenine (Kyn) pathway, which regulates neuroinflammation and n-methyl-d-aspartate (NMDA) receptor activation, is implicated in Parkinson's disease (PD) and Alzheimer's disease (AD). Age-related changes in Kyn metabolism and altered cerebral Kyn uptake along large-neutral amino acid (LNAA) transporters, could contribute to these diseases. To gain further insight into the role and prognostic potential of the Kyn pathway in PD and AD, we investigated systemic and cerebral Kyn metabolite production and estimations of their transporter-mediated uptake in the brain. Kyn metabolites and LNAAs were retrospectively measured in serum and cerebrospinal fluid (CSF) of clinically well-characterized PD patients (n=33), AD patients (n=33) and age-matched controls (n=39) using solid-phase extraction-liquid chromatographic-tandem mass spectrometry. Aging was disease-independently associated with increased Kyn, kynurenic acid and quinolinic acid in serum and CSF. Concentrations of kynurenic acid were reduced in CSF of PD and AD patients (p=.001; p=.002) but estimations of Kyn brain uptake did not differ between diseased and controls. Furthermore, serum Kyn and quinolinic acid levels strongly correlated with their respective content in CSF and Kyn in serum negatively correlated with AD disease severity (p=.002). Kyn metabolites accumulated with aging in serum and CSF similarly in PD patients, AD patients and control subjects. In contrast, kynurenic acid was strongly reduced in CSF of PD and AD patients. Differential transporter-mediated Kyn uptake is unlikely to majorly contribute to these cerebral Kyn pathway disturbances. We hypothesize that the combination of age- and disease-specific changes in cerebral Kyn pathway activity could contribute to reduced neurogenesis and increased excitotoxicity in neurodegenerative disease. This article is protected by copyright. All rights reserved

Surface Modification and Planar Defects of Calcium Carbonates by Magnetic Water Treatment

Powdery calcium carbonates, predominantly calcite and aragonite, with planar defects and cation–anion mixed surfaces as deposited on low-carbon steel by magnetic water treatment (MWT) were characterized by X-ray diffraction, electron microscopy, and vibration spectroscopy. Calcite were found to form faceted nanoparticles having 3x () commensurate superstructure and with well-developed {} and {} surfaces to exhibit preferred orientations. Aragonite occurred as laths having 3x () commensurate superstructure and with well-developed () surface extending along [100] direction up to micrometers in length. The (hkil)-specific coalescence of calcite and rapid lath growth of aragonite under the combined effects of Lorentz force and a precondensation event account for a beneficial larger particulate/colony size for the removal of the carbonate scale from the steel substrate. The coexisting magnetite particles have well-developed {011} surfaces regardless of MWT

Prevalence and correlates of frailty in an older rural African population:findings from the HAALSI cohort study

Background: Frailty is a key predictor of death and dependency, yet little is known about frailty in sub-Saharan Africa despite rapid population ageing. We describe the prevalence and correlates of phenotypic frailty using data from the Health and Aging in Africa: Longitudinal Studies of an INDEPTH Community cohort. Methods: We analysed data from rural South Africans aged 40 and over. We used low grip strength, slow gait speed, low body mass index, and combinations of self-reported exhaustion, decline in health, low physical activity and high self-reported sedentariness to derive nine variants of a phenotypic frailty score. Each frailty category was compared with self-reported health, subjective wellbeing, impairment in activities of daily living and the presence of multimorbidity. Cox regression analyses were used to compare subsequent all-cause mortality for non-frail (score 0), pre-frail (score 1–2) and frail participants (score 3+). Results: Five thousand fifty nine individuals (mean age 61.7 years, 2714 female) were included in the analyses. The nine frailty score variants yielded a range of frailty prevalences (5.4% to 13.2%). For all variants, rates were higher in women than in men, and rose steeply with age. Frailty was associated with worse subjective wellbeing, and worse self-reported health. Both prefrailty and frailty were associated with a higher risk of death during a mean 17 month follow up for all score variants (hazard ratios 1.29 to 2.41 for pre-frail vs non-frail; hazard ratios 2.65 to 8.91 for frail vs non-frail). Conclusions: Phenotypic frailty could be measured in this older South African population, and was associated with worse health, wellbeing and earlier death

Host sequence motifs shared by HIV predict response to antiretroviral therapy

<p>Abstract</p> <p>Background</p> <p>The HIV viral genome mutates at a high rate and poses a significant long term health risk even in the presence of combination antiretroviral therapy. Current methods for predicting a patient's response to therapy rely on site-directed mutagenesis experiments and <it>in vitro </it>resistance assays. In this bioinformatics study we treat response to antiretroviral therapy as a two-body problem: response to therapy is considered to be a function of both the host and pathogen proteomes. We set out to identify potential responders based on the presence or absence of host protein and DNA motifs on the HIV proteome.</p> <p>Results</p> <p>An alignment of thousands of HIV-1 sequences attested to extensive variation in nucleotide sequence but also showed conservation of eukaryotic short linear motifs on the protein coding regions. The reduction in viral load of patients in the Stanford HIV Drug Resistance Database exhibited a bimodal distribution after 24 weeks of antiretroviral therapy, with 2,000 copies/ml cutoff. Similarly, patients allocated into responder/non-responder categories based on consistent viral load reduction during a 24 week period showed clear separation. In both cases of phenotype identification, a set of features composed of short linear motifs in the reverse transcriptase region of HIV sequence accurately predicted a patient's response to therapy. Motifs that overlap resistance sites were highly predictive of responder identification in single drug regimens but these features lost importance in defining responders in multi-drug therapies.</p> <p>Conclusion</p> <p>HIV sequence mutates in a way that preferentially preserves peptide sequence motifs that are also found in the human proteome. The presence and absence of such motifs at specific regions of the HIV sequence is highly predictive of response to therapy. Some of these predictive motifs overlap with known HIV-1 resistance sites. These motifs are well established in bioinformatics databases and hence do not require identification via <it>in vitro </it>mutation experiments.</p

Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations

<p>Abstract</p> <p>Background</p> <p>Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.</p> <p>Results</p> <p>Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants.</p> <p>Conclusions</p> <p>We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.</p

Predicting Protein Phenotypes Based on Protein-Protein Interaction Network

BACKGROUND: Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins. METHODOLOGY/PRINCIPAL FINDINGS: Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%. CONCLUSIONS/SIGNIFICANCE: The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms