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  • 1
    Electronic Resource
    Electronic Resource
    New York, NY [u.a.] : Wiley-Blackwell
    Applied Organometallic Chemistry 8 (1994), S. 563-570 
    ISSN: 0268-2605
    Keywords: Methylmercury ; human hair ; capillary GC-electron capture detection ; Chemistry ; Organic Chemistry
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: An analytical procedure was developed for the determination of methylmercury in human hair using capillary gas chromatography with electron capture detection (GC-ECD). Hair samples are digested in alkaline solution/toluene in an ultrasonic bath at about 50°C. After cooling and treatment with hydrochloric acid (6 mol I-1) and a saturated solution of copper sulphate, the organic phase is extracted with a cysteine solution. Methylmercury is back-extracted in toluene by adding copper sulphate and potassium bromide and analysed by GC-ECD using a DB17 capillary column. The practical detection limit of the method for methylmercury is 50 ng g-1 using 100 mg of hair sample. Column performances and injection reproducibility have been evaluated. Results on 13 samples of human hair are presented and related to the total mercury concentration. The method, consisting of the optimization of similar analytical procedures through improvements in the various steps, allows for sensitive and reliable quantitation of methylmercury in hair with good precision and accuracy.
    Additional Material: 2 Ill.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2021-03-18
    Description: The most frequent form of dementia is Alzheimer’s Disease (AD), a severe progressive neurological pathology in which the main cognitive functions of an individual are compromised. Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in the cognitive decline associated with AD. Therefore, understanding social behaviours of AD patients is crucial to promote sociability, thus delaying cognitive decline, preserving independence, and providing a good quality of life. In this work, we analyze the localization data of AD patients living in assisted care homes to gather insights about the social dynamics among them. We use localization data collected by a system based on iBeacon technology comprising two components: a network of antennas scattered throughout the facility and a Bluetooth bracelet worn by the patients. We redefine the Relational Index to capture wandering and casual encounters, these being common phenomena among AD patients, and use the notions of Relational and Popularity Indexes to model, visualize and understand the social behaviour of AD patients. We leverage the data analyses to build predictive tools and applications to enhance social activities scheduling and sociability monitoring and promotion, with the ultimate aim of providing patients with a better quality of life. Predictions and visualizations act as a support for caregivers in activity planning to maximize treatment effects and, hence, slow down the progression of Alzheimer’s disease. We present the Community Behaviour Prediction Table (CBPT), a tool to visualize the estimated values of sociability among patients and popularity of places within a facility. Finally, we show the potential of the system by analyzing the Coronavirus Disease 2019 (COVID-19) lockdown time-frame between February and June 2020 in a specific facility. Through the use of the indexes, we evaluate the effects of the pandemic on the behaviour of the residents, observing no particular impact on sociability even though social distancing was put in place.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
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