Comparative distribution of azithromycin in lung tissue of patients given oral daily doses of 500 and 1000 mg

OBJECTIVES: The administration of antibacterial agents should be optimized on the basis of their distribution to enhance drug exposure and obtain bacterial eradication. This study examines the pharmacokinetics of azithromycin in plasma, lung tissue and bronchial washing in patients after oral administration of 500 mg versus 1000 mg once daily for 3 days. PATIENTS AND METHODS: Samples of plasma, lung tissue and bronchial washing were obtained from a cohort of 48 patients during open-chest surgery for lung resection up to 204 h after the last drug dose, and assayed for antibiotic concentrations. RESULTS: Azithromycin was widely distributed within the lower respiratory tract and sustained levels of the drug were detectable at the last sampling time in lung tissue. Doubling the dose of the antibiotic resulted in a proportional increase in lung area under the curve (AUC, 1245.4 versus 2514.2 h x mg/kg) and peak tissue concentration (Cmax, 8.93 +/- 2.05 versus 18.6 +/- 2.20 mg/kg). The pharmacodynamic parameter AUC/MIC for susceptible and intermediate strains of Streptococcus pneumoniae (MICs 0.5 and 2 mg/L, respectively) increased after administration of the 1000 mg schedule compared with 500 mg (AUC/MIC0.5 2414 versus 1144 and AUC/MIC2 2112 versus 814.1 h x mg/kg, respectively) in pulmonary tissue. CONCLUSIONS: Lung exposure to azithromycin is increased proportionally by doubling the dose, which results in a predictable pharmacokinetic behaviour of the drug in the lower respiratory tract

MWCNT/rGO/natural rubber latex dispersions for innovative, piezo‐resistive and cement‐based composite sensors

The present study is focused on the development and characterization of innovative cementitious-based composite sensors. In particular, multifunctional cement mortars with enhanced piezoresistive properties are realized by exploiting the concept of confinement of Multiwall Carbon Nanotubes (MWCNTs) and reduced Graphene Oxide (rGO) in a three-dimensional percolated network through the use of a natural-rubber latex aqueous dispersion. The manufactured cement-based composites were characterized by means of Inelastic Neutron Scattering to assess the hydration reactions and the interactions between natural rubber and the hydrated-cement phases and by Scanning Electron Microscopy and X-Ray diffraction to evaluate the morphological and mineralogical structure, respectively. Piezo-resistive properties to assess electro-mechanical behavior in strain condition are also measured. The results show that the presence of natural rubber latex allows to obtain a three-dimensional rGO/MWCNTs segregate structure which catalyzes the formation of hydrated phases of the cement and increases the piezo-resistive sensitivity of mortar composites, representing a reliable approach in developing innovative mortar-based piezoresistive strain sensors

Frailty trajectories in community-dwelling older adults during COVID-19 pandemic: The PRESTIGE study

Background Frailty has been recognized as potential surrogate of biological age and relevant risk factor for COVID-19 severity. Thus, it is important to explore the frailty trajectories during COVID-19 pandemic and understand how COVID-19 directly and indirectly impacts on frailty condition. Methods We enrolled 217 community-dwelling older adults with available information on frailty condition as assessed by multidimensional frailty model both at baseline and at one-year follow-up using Multidimensional Prognostic Index (MPI) tools. Pre-frail/frail subjects were identified at baseline as those with MPI score >0.33 (MPI grades 2-3). Frailty worsening was defined by MPI difference between 12 months follow-up and baseline >= 0.1. Multivariable logistic regression was modelled to identify predictors of worsening of frailty condition. Results Frailer subjects at baseline (MPI grades 2-3 = 48.4%) were older, more frequently female and had higher rates of hospitalization and Sars-CoV-2 infection compared to robust ones (MPI grade 1). Having MPI grades 2-3 at baseline was associated with higher risk of further worsening of frailty condition (adjusted odd ratio (aOR): 13.60, 95% confidence interval (CI): 4.01-46.09), independently by age, gender and Sars-CoV-2 infection. Specifically, frail subjects without COVID-19 (aOR: 14.84, 95% CI: 4.26-51.74) as well as those with COVID-19 (aOR: 12.77, 95% CI: 2.66-61.40, p = 0.001) had significantly higher risk of worsening of frailty condition. Conclusions Effects of COVID-19 pandemic among community-dwelling frailer individuals are far beyond the mere infection and disease, determining a significant deterioration of frailty status both in infected and non-infected subjects

Development and validation of an art-inspired multimodal interactive technology system for a multi-component intervention for older people: a pilot study

IntroductionThe World Health Organization (WHO) acknowledges the presence of a significant body of research on the positive effects of the arts on health, considering a variety of factors including physical well-being, quality of life, and social and community impact. The model that underlies cultural welfare puts the performing arts, visual arts, and cultural heritage at the service of people personal and societal well-being. The potential connections between movements of the body and artistic content have been extensively studied over time, considering movement as a non-verbal language with a universal character.MethodsThis pilot study presents the results of the validation of an innovative multimodal system, the DanzArTe-Emotional Wellbeing Technology, designed to support active and participative experience of older people providing physical and cognitive activation through a full-body physical interaction with a traditional visual work of art of religious subject. DanzArTe supports a replicable treatment protocol for multidimensional frailty, administered through a low cost and scalable technological platform capable of generating real-time visual and auditory feedback (interactive sonification) from the automated analysis of individual as well as joint movement expressive qualities. The study involved 45 participants, 23 of whom participated in the DanzArTe program and 22 who were included in the control group.ResultsThe two groups were similar in terms of age (p = 0.465) and gender (p = 0.683). The results showed that the DanzArTe program had a positive impact on participants' self-perceived psychological health and well-being (Mean Psychological General Well-Being Index—Short T1 = 19.6 ± 4.3 Vs. T2 = 20.8 ± 4.9; p = 0.029). The same trend was not observed in the control group (p = 0.389).DiscussionThe findings suggest that such programs may have a significant impact particularly on the mental and social well-being of older adults and could be a valuable tool for promoting healthy aging and improving quality of life

Relationship between Multidimensional Prognostic Index (MPI) and incident depressive symptoms in older people: findings from the Irish Longitudinal Study on Ageing

Objectives: The Multidimensional Prognostic Index (MPI) is a useful prognostic tool for evaluating adverse health outcomes in older individuals. However, the association between MPI and depressive symptoms has never been explored, despite depression being a common condition in older people. We therefore aimed to evaluate whether MPI may predict incident depressive symptoms. Methods: Longitudinal, cohort study, with two years of follow‐up (W1: October 2009‐February 2011; W2: April 2012‐January 2013), including people aged ≥65 years without depressive symptoms at baseline. A comprehensive geriatric assessment including information on functional, nutritional, cognitive status, mobility, comorbidities, medications, and cohabitation status was used to calculate the MPI dividing the participants into low, moderate, or severe risk. Those who scored ≥16/60 with the Center of Epidemiology Studies Depression (CES‐D) tool were considered to have depressive symptoms. Multivariable logistic regression models were built to explore the association between MPI and incident depressive symptoms. Results: The sample consisted of 1854 participants (mean age: 72.8 ± SD 5.1 years; females: 52.1%). The prevalence of incident depressive symptoms by MPI tertiles at baseline were: low 2.5%, moderate 3.9%, and severe 6.7%. In multivariable analyses, baseline MPI values were significantly associated with incident depressive symptoms (increase in 0.1 points in MPI: odds ratio, OR = 1.47; 95% confidence intervals, CI: 1.17‐1.85; MPI tertile severe vs low: OR = 2.96; 95%CI: 1.50‐5.85). Conclusion: Baseline MPI values were associated with incident depressive symptoms indicating that multidimensional assessment of older people may lead to early identification of individuals at increased risk of depression onset

Application of Genetics in the Elderly: Development, Integration, Analyses - AGE-DIAmond: development of a model based on clinical and genetic determinants to predict clinical outcome

BACKGROUND: The burden of age-related chronic diseases, such as neurodegenerative disorders, led to an increasing interest in the risk factors for physical, psychological and functional decline. Frailty is a geriatric syndrome of decreased resistance to stressors and increased risk for adverse health outcomes that can be defined based on a multidimensional approach, such as the Multidimensional Prognostic Index (MPI). To date there is no available instrument to predict the clinical outcomes in the elderly that comprises also constitutional, biological and genetic factors. AIMS: The goal of this work was to design a real-world clinical protocol aimed to develop a predictive multidimensional model based on clinical, biological and genetic data, including biomarkers associated with frailty, ageing and cognitive decline. METHODS: The project team surveyed and critically appraised the current clinical procedures and the recommended protocols (including geriatric assessment, neuropsychological assessment and genetic counselling). The study design and the clinical protocol were revised until consensus was reached. RESULTS: A cross-sectional study was designed. All consecutive patients referred to the geriatrics unit for cognitive decline and/or frailty syndrome are eligible. The primary outcome measure is MPI. Secondary outcomes will include the longitudinal change of MPI, cognitive decline, mortality, hospitalization, pharmacological and non-pharmacological treatments response. Clinical assessment will be provided by a multidisciplinary team and will include geriatric clinical evaluation, neuropsychological examination and genetic counselling. Patients will be asked to fill a questionnaire for family history collection. A blood sample will be collected and stored for the biological and genetic investigations. For each patient, at least one follow-up visit will be performed. The study protocol was approved by the Regional Ethics Committee. Patients enrolment is expected to start in June 2017. CONCLUSIONS: The AGE-DIAmond study is envisaged to develop in a real-world setting a model which will validly predict clinical outcome in ageing individuals. Once an accurate and robust model is established, personalised preventive and therapeutic procedures can be successfully accomplished. The availability of valid biomarkers and of a robust predictive model may influence the design of clinical trials for innovative treatments. No conflict of interest declared. The work was partially granted by the University of Genova (Fondi Ricerca Ateneo 2015) to EDM

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults.

BackgroundFalls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults.MethodsCommunity-dwelling subjects aged ≥ 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed "Up & Go" test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls-on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameters-were identified by means of a cross-validation method.ResultsOf the 100 subjects initially enrolled, 96 (62 females, mean age 77.17±.49 years) completed the follow-up and were included. Within one year, 32 participants (33%) experienced at least one fall ("fallers"), while 64 (67%) did not ("non-fallers"). The best classifier model to emerge from cross-validated fall-risk estimation included eight clinical variables (age, sex, history of falling in the previous 12 months, TUG, Tinetti, SPPB, Low GS, number of drugs) and 20 robotic parameters, and displayed an area under the receiver operator characteristic (ROC) curve of 0.81 (95% CI: 0.72-0.90). Notably, the model that included only three of these clinical variables (age, history of falls and low GS) plus the robotic parameters showed similar accuracy (ROC AUC 0.80, 95% CI: 0.71-0.89). In comparison with the best classifier model that comprised only clinical parameters (ROC AUC: 0.67; 95% CI: 0.55-0.79), both models performed better in predicting fall risk, with an estimated Net Reclassification Improvement (NRI) of 0.30 and 0.31 (p = 0.02), respectively, and an estimated Integrated Discrimination Improvement (IDI) of 0.32 and 0.27 (pConclusionA multifactorial fall-risk assessment that includes clinical and hunova robotic variables significantly improves the accuracy of predicting the risk of falling in community-dwelling older people. Our data suggest that combining clinical and robotic assessments can more accurately identify older people at high risk of falls, thereby enabling personalized fall-prevention interventions to be undertaken