RESUMO
(1) Background: The increasing life expectancy brings an increase in geriatric syndromes, specifically frailty. The literature shows that exercise is a key to preventing, or even reversing, frailty in community-dwelling populations. The main objective is to demonstrate how an intervention based on multicomponent exercise produces an improvement in frailty and pre-frailty in a community-dwelling population. (2) Methods: a prospective observational study of a multicomponent exercise program for geriatric revitalization with people aged over 65 holding Barthel Index scores equal to, or beyond, 90. The program was developed over 30 weeks, three times a week, in sessions lasting 45-50 min each. Frailty levels were registered by the Short Physical Performance Battery, FRAIL Questionnaire Screening Tool, and Timed "Up & Go" at the beginning of the program, 30 weeks later (at the end of the program), and following 13 weeks without training; (3) Results: 360 participants completed the program; a greater risk of frailty was found before the program started among older women living in urban areas, with a more elevated fat percentage, more baseline pathologies, and wider baseline medication use. Furthermore, heterogeneous results were observed both in training periods and in periods without physical activity. However, they are consistent over time and show improvement after training. They show a good correlation between TUG and SPPB; (4) Conclusions: A thirty-week multicomponent exercise program improves frailty and pre-frailty status in a community-dwelling population with no functional decline. Nevertheless, a lack of homogeneity is evident among the various tools used for measuring frailty over training periods and inactivity periods.
RESUMO
In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented.
RESUMO
Seven aromatic polyamides and copolyamides were synthesized from two different aromatic diamines: 4,4'-(Hexafluoroisopropylidene)bis(p-phenyleneoxy)dianiline (HFDA) and 2,4-Aminobenzenesulfonic acid (DABS). The synthesis was carried out by polycondensation using isophthaloyl dichloride (1SO). The effect of an increasing molar concentration of the sulfonated groups, from DABS, in the copolymer properties was evaluated. Inherent viscosity tests were carried out to estimate molecular weights. Mechanical tests were carried out under tension, maximum strength ( σ max), Young's modulus (E), and elongation at break (εmax) to determine their mechanical properties. Tests for water sorption and ion exchange capacity (IEC) were carried out. Proton conductivity was measured using electrochemical impedance spectroscopy (EIS). The results indicate that as the degree of sulfonation increase, the greater the proton conductivity. The results obtained showed conductivity values lower than the commercial membrane Nafion 115 of 0.0065 S cm-1. The membrane from copolyamide HFDA/DABS/1S0-70/30 with 30 mol DABS obtained the best IEC, with a value of 0.747 mmol g-1 that resulted in a conductivity of 2.7018 × 10-4 S cm-1, lower than the data reported for the commercial membrane Nafion 115. According to the results obtained, we can suggest that further developments increasing IEC will render membranes based on aromatic polyamides that are suitable for their use in PEM fuel cells.
