Coordinated analysis of age, sex, and education effects on change in MMSE scores

Objectives. We describe and compare the expected performance trajectories of older adults on the Mini-Mental Status Examination (MMSE) across six independent studies from four countries in the context of a collaborative network of longitudinal studies of aging. A coordinated analysis approach is used to compare patterns of change conditional on sample composition differences related to age, sex, and education. Such coordination accelerates evaluation of particular hypotheses. In particular, we focus on the effect of educational attainment on cognitive decline.Method. Regular and Tobit mixed models were fit to MMSE scores from each study separately. The effects of age, sex, and education were examined based on more than one centering point.Results. Findings were relatively consistent across studies. On average, MMSE scores were lower for older individuals and declined over time. Education predicted MMSE score, but, with two exceptions, was not associated with decline in MMSE over time.Conclusion. A straightforward association between educational attainment and rate of cognitive decline was not supported. Thoughtful consideration is needed when synthesizing evidence across studies, as methodologies adopted and sample characteristics, such as educational attainment, invariably differ. © 2012 The Author

Response to commentary “Zinc is decreased in prostate cancer: an established relationship of prostate cancer!”

10.1007/s00775-010-0737-8Journal of Biological Inorganic Chemistry1619-13JJBC

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research

Identification and quantification of microplastics in wastewater using focal plane array-based reflectance micro-FT-IR imaging

Microplastics (<5 mm) have been documented in environmental samples on a global scale. While these pollutants may enter aquatic environments via wastewater treatment facilities, the abundance of microplastics in these matrices has not been investigated. Although efficient methods for the analysis of microplastics in sediment samples and marine organisms have been published, no methods have been developed for detecting these pollutants within organic-rich wastewater samples. In addition, there is no standardized method for analyzing microplastics isolated from environmental samples. In many cases, part of the identification protocol relies on visual selection before analysis, which is open to bias. In order to address this, a new method for the analysis of microplastics in wastewater was developed. A pretreatment step using 30% hydrogen peroxide (H2O2) was employed to remove biogenic material, and focal plane array (FPA)-based reflectance micro-Fourier-transform (FT-IR) imaging was shown to successfully image and identify different microplastic types (polyethylene, polypropylene, nylon-6, polyvinyl chloride, polystyrene). Microplastic-spiked wastewater samples were used to validate the methodology, resulting in a robust protocol which was nonselective and reproducible (the overall success identification rate was 98.33%). The use of FPA-based micro-FT-IR spectroscopy also provides a considerable reduction in analysis time compared with previous methods, since samples that could take several days to be mapped using a single-element detector can now be imaged in less than 9 h (circular filter with a diameter of 47 mm). This method for identifying and quantifying microplastics in wastewater is likely to provide an essential tool for further research into the pathways by which microplastics enter the environment.This work is funded by a NERC (Natural Environment Research Council) CASE studentship (NE/K007521/1) with contribution from industrial partner Fera Science Ltd., United Kingdom. The authors would like to thank Peter Vale, from Severn Trent Water Ltd, for providing access to and additionally Ashley Howkins (Brunel University London) for providing travel and assistance with the sampling of the Severn Trent wastewater treatment plant in Derbyshire, UK. We are grateful to Emma Bradley and Chris Sinclair for providing helpful suggestions for our research

Study on COgnition and Prognosis in the Elderly (SCOPE)

Blood Press. 1999;8(3):177-83. Study on COgnition and Prognosis in the Elderly (SCOPE). Hansson L, Lithell H, Skoog I, Baro F, Bánki CM, Breteler M, Carbonin PU, Castaigne A, Correia M, Degaute JP, Elmfeldt D, Engedal K, Farsang C, Ferro J, Hachinski V, Hofman A, James OF, Krisin E, Leeman M, de Leeuw PW, Leys D, Lobo A, Nordby G, Olofsson B, Zanchetti A, et al. University of Uppsala, Department of Public Health, Sweden. Abstract The Study on COgnition and Prognosis in the Elderly (SCOPE) is a multicentre, prospective, randomized, double-blind, parallel-group study designed to compare the effects of candesartan cilexetil and placebo in elderly patients with mild hypertension. The primary objective of the study is to assess the effect of candesartan cilexetil on major cardiovascular events. The secondary objectives of the study are to assess the effect of candesartan cilexetil on cognitive function and on total mortality, cardiovascular mortality, myocardial infarction, stroke, renal function, hospitalization, quality of life and health economics. Male and female patients aged between 70 and 89 years, with a sitting systolic blood pressure (SBP) of 160-179 mmHg and/or diastolic blood pressure (DBP) of 90-99 mmHg, and a Mini-Mental State Examination (MMSE) score of 24 or above, are eligible for the study. The overall target study population is 4000 patients, at least 1000 of whom are also to be assessed for quality of life and health economics data. After an open run-in period lasting 1-3 months, during which patients are assessed for eligibility and those who are already on antihypertensive therapy at enrolment are switched to hydrochlorothiazide 12.5 mg o.d., patients are randomized to receive either candesartan cilexetil 8 mg once daily (o.d.) or matching placebo o.d. At subsequent study visits, if SBP remains >160 mmHg, or has decreased by 85 mmHg, study treatment is doubled to candesartan cilexetil 16 mg o.d. or two placebo tablets o.d. Recruitment was completed in January 1999. At that time 4964 patients had been randomized. All randomized patients will be followed for an additional 2 years. If the event rate is lower than anticipated, the follow-up will be prolonged. PMID: 10595696 [PubMed - indexed for MEDLINE

Toxicogenomic Profiling of 28 Nanomaterials in Mouse Airways

Toxicogenomics opens novel opportunities for hazard assessment by utilizing computational methods to map molecular events and biological processes. In this study, the transcriptomic and immunopathological changes associated with airway exposure to a total of 28 engineered nanomaterials (ENM) are investigated. The ENM are selected to have different core (Ag, Au, TiO2, CuO, nanodiamond, and multiwalled carbon nanotubes) and surface chemistries (COOH, NH2, or polyethylene glycosylation (PEG)). Additionally, ENM with variations in either size (Au) or shape (TiO2) are included. Mice are exposed to 10 mu g of ENM by oropharyngeal aspiration for 4 consecutive days, followed by extensive histological/cytological analyses and transcriptomic characterization of lung tissue. The results demonstrate that transcriptomic alterations are correlated with the inflammatory cell infiltrate in the lungs. Surface modification has varying effects on the airways with amination rendering the strongest inflammatory response, while PEGylation suppresses toxicity. However, toxicological responses are also dependent on ENM core chemistry. In addition to ENM-specific transcriptional changes, a subset of 50 shared differentially expressed genes is also highlighted that cluster these ENM according to their toxicity. This study provides the largest in vivo data set currently available and as such provides valuable information to be utilized in developing predictive models for ENM toxicity.Peer reviewe

Liquid-infiltrated photonic crystals - enhanced light-matter interactions for lab-on-a-chip applications

Optical techniques are finding widespread use in analytical chemistry for chemical and bio-chemical analysis. During the past decade, there has been an increasing emphasis on miniaturization of chemical analysis systems and naturally this has stimulated a large effort in integrating microfluidics and optics in lab-on-a-chip microsystems. This development is partly defining the emerging field of optofluidics. Scaling analysis and experiments have demonstrated the advantage of micro-scale devices over their macroscopic counterparts for a number of chemical applications. However, from an optical point of view, miniaturized devices suffer dramatically from the reduced optical path compared to macroscale experiments, e.g. in a cuvette. Obviously, the reduced optical path complicates the application of optical techniques in lab-on-a-chip systems. In this paper we theoretically discuss how a strongly dispersive photonic crystal environment may be used to enhance the light-matter interactions, thus potentially compensating for the reduced optical path in lab-on-a-chip systems. Combining electromagnetic perturbation theory with full-wave electromagnetic simulations we address the prospects for achieving slow-light enhancement of Beer-Lambert-Bouguer absorption, photonic band-gap based refractometry, and high-Q cavity sensing.Comment: Invited paper accepted for the "Optofluidics" special issue to appear in Microfluidics and Nanofluidics (ed. Prof. David Erickson). 11 pages including 8 figure