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Background: Attitudes toward aging influence many health outcomes, yet their relationship with cognition and Alzheimer’s disease (AD) remains unknown. To better understand their impact on cognition and AD risk, we examined whether positive attitudes predict better cognition and diminished risk on AD biomarkers. Methods: A subsample of older adults with a family history of AD (n=54; women=39) from the McGill PREVENT-AD cohort participated in this study. Participants completed the Attitudes to Ageing Questionnaire (AAQ-24), providing three scores: psychosocial loss, psychological growth and physical change. Participants underwent cognitive testing (Rey Auditory Verbal Learning Test, RAVLT; Delis-Kaplan Executive Function System-Color Word Interference Test, D-KEFS-CWIT), and AD blood-based biomarker assessments (p-tau217, Aβ42/40). Regression models tested associations, adjusting for covariates (age, sex, education, depression, APOE4), and were Bonferroni corrected. Results: Positive attitudes were associated with better recall and recognition (RAVLT) and improved word reading, colour naming, switching, and inhibition (D-KEFS-CWIT) (p<0.00077), while negative attitudes showed the opposite pattern. Negative attitudes were correlated with lower Aβ42/40 ratios, while positive attitudes were linked to lower p-tau217 (p<0.0167). Conclusions: These findings demonstrate that positive attitudes predict better cognition and a lower risk profile for AD biomarkers, suggesting that life outlook may be an early disease feature or a risk factor.
Modifications of the external surface area and the two types of microporosity of sepiolite (structural microporosity and inter-fiber porosity) were examined as a function of the temperature of a vacuum thermal treatment to 500°C. The methods used included: reciprocal thermal analysis, N2 and Ar low-temperature adsorption microcalorimetry, gas adsorption volumetry (for N2, Ar, and Kr at 77 K and CO2 at 273 and 293 K), water-vapor adsorption gravimetry, and immersion microcalorimetry into liquid water at 303 K. If the sample was not heated >100°C, only 20% of the structural microporosity was available to N2, whereas 52% was available to CO2 at 293 K. In both experiments, the channels filled at very low relative pressures. At >350°C, the structure transformed to anhydrous sepiolite, which showed no structural microporosity. The inter-fiber microporosity decreased from 0.031 to 0.025 cm3g (as seen with N2), and the external specific surface area decreased from 120 to 48 m2/g. The water adsorption isotherms showed a lower and lower affinity of the external surface of fibers for water as the temperature of thermal treatment increased. The thickness of the bound water on the external surface was estimated to be ≤ 3.5 monolayers, i.e., less than 10 Å.
Ensuring that life-saving antimicrobials remain available as effective treatment options in the face of rapidly rising levels of antimicrobial resistance will require a massive and coordinated global effort. Setting a collective direction for progress is the first step towards aligning global efforts on AMR. This process would be greatly accelerated by adopting a unifying global target — a well-defined global target that unites all countries and sectors. The proposed pandemic instrument — with its focus on prevention, preparedness and response — represents an ideal opportunity to develop and adopt a unifying global target that catalyzes global action on AMR. We propose three key characteristics of a unifying global target for AMR that — if embedded within the pandemic preparedness instrument — could rally public support, funding, and political commitment commensurate with the scale of the AMR challenge.
Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day.
Methods:
Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.
Results:
237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.
Conclusions:
AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.
Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility models, ARCH, GARCH, and vector autoregressive models. The authors have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based and hierarchical specifications, models based upon latent variable representations, and mixture and time series specifications. MCMC methods are discussed and illustrated in detail - from introductory applications to those at the current research frontier - and MATLAB® computer programs are provided on the website accompanying the text. Suitable for graduate study in economics, the text should also be of interest to students studying statistics, finance, marketing, and agricultural economics.
People with major mental disorders are more likely to be violent than other members of the general population. What is contoversial is the influence of the patients’ environnemental violence as regards their aggressive behaviors.
The aim of the study was to assess the violence of patients with psychotic disorders regarding the crime rate in the patients’ community.
Method
We have led a prospective multicentre study in 9 French cities-each of them having different crime rates. Eligible patients were psychotic involuntary patients hospitalized in the cities’acute admission psychiatric wards. During their treatments, any kind of the patients’aggressive behavior has been reported by the OAS (Overt Aggresion Scale).
Results
From June 2010 to May 2011, 95 patients have been included. Seventy-nine per cent of the patients were violent during their hospitalizations. The patients’violence was mostly verbal (65%). In a bivariate analysis, the patients’violence was significantly associated to different factors: male gender, the patients's violence history, substance abuse, manic or mixed disorder, the symptoms severity measured by the BPRS, the insight degree and the crime rate in the city. In a multivariate analysis, the only significant factors associated with the patients’violence were substance abuse, the symptoms severity and the patients’cities’crime rates.
Discussion
The results are in accordance with the literature on the risk factors of violent behaviors.The environnemental factor-wich was until now not so much studied-also appears highly associated to this risk.
Conclusion
These results suggest that the violence within the psychotic patients’environnement could represent a risk of violence during the treatment.