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Preclinical tests for evaluating potential drug candidates using conventional protocols can be exhaustive and high-cost processes. Microfluidic technologies that can speed up this process and allow fast screening of drugs are promising alternatives. This work presents the design, concept, and operational conditions of a simple, modular, and reversible sealing microdevice useful for drug screening. This microdevice allows for the operation of 4 parallel simultaneous conditions and can also generate a diffusive concentration gradient in sextuplicates. We used laminated polydimethylsiloxane (PDMSLAM) and glass as building materials as proof of concept. The PDMSLAM parts can be reused since they can be easily sterilized. We cultured MCF-7 (Michigan Cancer Foundation-7) breast cancer cells. Cells were exposed to a doxorubicin diffusive concentration gradient for 3 h. They were monitored by automated microscopy, and after data processing, it was possible to determine cell viability as a function of doxorubicin concentration. The reversible sealing enabled the recovery of the tested cells and image acquisition. Therefore, this microdevice is a promising tool for drug screening that allows assessing the cellular behavior in dynamic conditions and the recovery of cells for afterward processing and imaging.

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Sarcopenia and sarcopenic obesity research in low- and middle- income countries (LMICs) is limited. We investigated sarcopenia and sarcopenic obesity prevalence and sociodemographic, bio-clinical and lifestyle factors in LMICs settings. For the purposes of this study, the 10/66 Dementia Research Group follow-up wave information from individuals aged 65 and over in Cuba, Dominican Republic, Peru, Mexico, Puerto Rico, China, was employed and analysed (n = 8.694). Based on indirect population formulas, we calculated body fat percentage (%BF) and skeletal muscle mass index (SMI). Sarcopenia prevalence ranged from 12.4% (Dominican Republic) to 24.6% (rural Peru); sarcopenic obesity prevalence ranged from 3.0% (rural China) to 10.2% (rural Peru). Odds ratios (OR) with 95% confidence intervals (CI) for sarcopenia were higher for men 2.82 (2.22-3.57) and those with higher %BF 1.08 (1.07-1.09), whereas higher number of assets was associated with a decreased likelihood 0.93 (0.87-1.00). OR of sarcopenic obesity were higher for men 2.17 (1.70-2.76), those reporting moderate alcohol drinking 1.76 (1.21-2.57), and those with increased number of limiting impairments 1.54 (1.11-2.14). We observed heterogeneity in the prevalence of sarcopenia and sarcopenic obesity in the 10/66 settings. We also found a variety of factors to be associated with those. Our results reveal the need for more research among the older population of LMICs.

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BACKGROUND: A growing number of studies have explored how features of the neighbourhood environment can be related to cognitive health in later life. Yet few have focused on low- and middle-income countries and compared the results across different settings. The aim of this study is to investigate the cross-sectional associations between neighbourhood amenities and dementia in older people from high-, middle- and low-income countries. METHODS: This study was based on two population-based cohort studies of people aged≥65: the Cognitive Function and Ageing Study II (CFAS II) in UK (N = 4955) and a subset of the 10/66 study in China, Dominican Republic and Mexico (N = 3386). In both cohorts, dementia was assessed using the Geriatric Mental State-Automated Geriatric Examination for Computer Assisted Taxonomy (GMS-AGECAT) algorithm. The 10/66 dementia diagnostic algorithm was also used as an additional criterion in the 10/66 study. Publicly accessible databases, Google Maps and Open Street Map, were used to obtain geographic information system data on distance to neighbourhood amenities, including lifestyle (cafés, libraries, movie theatres, parks), daily life (post offices, convenience stores), healthcare (hospitals, pharmacies) and percentages of local green and blue spaces within 400 and 800 m of participants' residences. Multilevel logistic regression was used to investigate the associations between these environmental features and dementia adjusting for sociodemographic factors and self-rated health. RESULTS: Living far from daily life amenities was associated with higher odds of dementia in both CFAS II (1.47; 95% CI: 0.96, 2.24) and the 10/66 study (1.53; 95% CI: 1.15, 2.04), while living far from lifestyle (1.50; 95% CI: 1.13, 1.99) and healthcare amenities (1.32; 95% CI: 0.93, 1.87) was associated with higher odds of dementia only in the 10/66 study. A high availability of local green and blue spaces was not associated with dementia in either cohort yet living far from public parks was associated with lower odds of dementia in CFAS II (0.64; 95% CI: 0.41, 1.00). CONCLUSIONS: The different relationships across cohorts may indicate a varying role for local amenities in diverse settings. Future research may investigate mechanisms related to these differences and social, cultural and historical influences on the interaction between neighbourhood amenities and older people.

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BACKGROUND: Research has suggested the positive impact of physical activity on health and wellbeing in older age, yet few studies have investigated the associations between physical activity and heterogeneous trajectories of healthy ageing. We aimed to identify how physical activity can influence healthy ageing trajectories using a harmonised dataset of eight ageing cohorts across the world. METHODS: Based on a harmonised dataset of eight ageing cohorts in Australia, USA, Mexico, Japan, South Korea, and Europe, comprising 130,521 older adults (Mage = 62.81, SDage = 10.06) followed-up up to 10 years (Mfollow-up = 5.47, SDfollow-up = 3.22), we employed growth mixture modelling to identify latent classes of people with different trajectories of healthy ageing scores, which incorporated 41 items of health and functioning. Multinomial logistic regression modelling was used to investigate the associations between physical activity and different types of trajectories adjusting for sociodemographic characteristics and other lifestyle behaviours. RESULTS: Three latent classes of healthy ageing trajectories were identified: two with stable trajectories with high (71.4%) or low (25.2%) starting points and one with a high starting point but a fast decline over time (3.4%). Engagement in any level of physical activity was associated with decreased odds of being in the low stable (OR: 0.18; 95% CI: 0.17, 0.19) and fast decline trajectories groups (OR: 0.44; 95% CI: 0.39, 0.50) compared to the high stable trajectory group. These results were replicated with alternative physical activity operationalisations, as well as in sensitivity analyses using reduced samples. CONCLUSIONS: Our findings suggest a positive impact of physical activity on healthy ageing, attenuating declines in health and functioning. Physical activity promotion should be a key focus of healthy ageing policies to prevent disability and fast deterioration in health.

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BACKGROUND: The rapid growth of the size of the older population is having a substantial effect on health and social care services in many societies across the world. Maintaining health and functioning in older age is a key public health issue but few studies have examined factors associated with inequalities in trajectories of health and functioning across countries. The aim of this study was to investigate trajectories of healthy ageing in older men and women (aged ≥45 years) and the effect of education and wealth on these trajectories. METHODS: This population-based study is based on eight longitudinal cohorts from Australia, the USA, Japan, South Korea, Mexico, and Europe harmonised by the EU Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) consortium. We selected these studies from the repository of 17 ageing studies in the ATHLOS consortium because they reported at least three waves of collected data. We used multilevel modelling to investigate the effect of education and wealth on trajectories of healthy ageing scores, which incorporated 41 items of physical and cognitive functioning with a range between 0 (poor) and 100 (good), after adjustment for age, sex, and cohort study. FINDINGS: We used data from 141 214 participants, with a mean age of 62·9 years (SD 10·1) and an age range of 45-106 years, of whom 76 484 (54·2%) were women. The earliest year of baseline data was 1992 and the most recent last follow-up year was 2015. Education and wealth affected baseline scores of healthy ageing but had little effect on the rate of decrease in healthy ageing score thereafter. Compared with those with primary education or less, participants with tertiary education had higher baseline scores (adjusted difference in score of 10·54 points, 95% CI 10·31-10·77). The adjusted difference in healthy ageing score between lowest and highest quintiles of wealth was 8·98 points (95% CI 8·74-9·22). Among the eight cohorts, the strongest inequality gradient for both education and wealth was found in the Health Retirement Study from the USA. INTERPRETATION: The apparent difference in baseline healthy ageing scores between those with high versus low education levels and wealth suggests that cumulative disadvantage due to low education and wealth might have largely deteriorated health conditions in early life stages, leading to persistent differences throughout older age, but no further increase in ageing disparity after age 70 years. Future research should adopt a lifecourse approach to investigate mechanisms of health inequalities across education and wealth in different societies. FUNDING: European Union Horizon 2020 Research and Innovation Programme.

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Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.

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Objective: The objective of this study was to estimate healthy life expectancies in eight low- and middle-income countries (LMICs), using two indicators: disability-free life expectancy (DFLE) and dependence-free life expectancy (DepFLE). Method: Using the Sullivan method, healthy life expectancy was calculated based on the prevalence of dependence and disability from the 10/66 cohort study, which included 16,990 people aged 65 or above in China, Cuba, Dominican Republic, India, Mexico, Peru, Puerto Rico, and Venezuela, and country-specific life tables from the World Population Prospects 2017. Results: DFLE and DepFLE declined with older age across all sites and were higher in women than men. Mexico reported the highest DFLE at age 65 for men (15.4, SE = 0.5) and women (16.5, SE = 0.4), whereas India had the lowest with (11.5, SE = 0.3) in men and women (11.7, SE = 0.4). Discussion: Healthy life expectancy based on disability and dependency can be a critical indicator for aging research and policy planning in LMICs.

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Projections show that the number of people above 60 years old will triple by 2050 in Mexico. Nevertheless, ageing is characterised by great variability in the health status. In this study, we aimed to identify trajectories of health and their associations with lifestyle factors in a national representative cohort study of older Mexicans. We used secondary data of 14,143 adults from the Mexican Health and Aging Study (MHAS). A metric of health, based on the conceptual framework of functional ability, was mapped onto four waves (2001, 2003, 2012, 2015) and created by applying Bayesian multilevel Item Response Theory (IRT). Conditional Growth Mixture Modelling (GMM) was used to identify latent classes of individuals with similar trajectories and examine the impact of physical activity, smoking and alcohol on those. Conditional on sociodemographic and lifestyle behaviour four latent classes were suggested: high-stable, moderate-stable, low-stable and decliners. Participants who did not engage in physical activity, were current or previous smokers and did not consume alcohol at baseline were more likely to be in the trajectory with the highest deterioration (i.e. decliners). This study confirms ageing heterogeneity and the positive influence of a healthy lifestyle. These results provide the ground for new policies.

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