Background: A higher protein intake is suggested to preserve muscle mass during aging and may therefore reduce the risk of sarcopenia.Objectives: We explored whether the amount and type (animal or vegetable) of protein intake were associated with 5-y change in mid-thigh muscle cross-sectional area (CSA) in older adults (n = 1561).Methods: Protein intake was assessed at year 2 by a Block foodfrequency questionnaire in participants (aged 70–79 y) of the Health, Aging, and Body Composition (Health ABC) Study, a prospective cohort study. At year 1 and year 6 mid-thigh muscle CSA in square centimeters was measured by computed tomography. Multiple linearregression analysis was used to examine the association between energy-adjusted protein residuals in grams per day (total, animal, and vegetable protein) and muscle CSA at year 6, adjusted for muscle CSA at year 1 and potential confounders including prevalent health conditions, physical activity, and 5-y change in fat mass.Results: Mean (95% CI) protein intake was 0.90 (0.88, 0.92) g ·kg–1 · d–1 and mean (95% CI) 5-y change in muscle CSA was −9.8 (−10.6, −8.9) cm2. No association was observed between energyadjusted total (β = −0.00; 95% CI: −0.06, 0.06 cm2; P = 0.982), animal (β = −0.00; 95% CI: −0.06, 0.05 cm2; P = 0.923), or plant(β = +0.07; 95% CI: −0.06, 0.21 cm2; P = 0.276) protein intake and muscle CSA at year 6, adjusted for baseline mid-thigh muscle CSA and potential confounders.Conclusions: This study suggests that a higher total, animal, or vegetable protein intake is not associated with 5-y change in midthigh muscle CSA in older adults. This conclusion contradicts some, but not all, previous research. This trial was registered at www.trialregister.nl as NTR6930.
In light of increasing calls for transparent reporting of research and prevention of detrimental research practices, we conducted a cross-sectional machine-assisted analysis of a representative sample of scientific journals' instructions to authors (ItAs) across all disciplines. We investigated addressing of 19 topics related to transparency in reporting and research integrity. Only three topics were addressed in more than one third of ItAs: conflicts of interest, plagiarism, and the type of peer review the journal employs. Health and Life Sciences journals, journals published by medium or large publishers, and journals registered in the Directory of Open Access Journals (DOAJ) were more likely to address many of the analysed topics, while Arts & Humanities journals were least likely to do so. Despite the recent calls for transparency and integrity in research, our analysis shows that most scientific journals need to update their ItAs to align them with practices which prevent detrimental research practices and ensure transparent reporting of research.
MULTIFILE
Background: Advanced medical technologies (AMTs), such as respiratory support or suction devices, are increasingly used in home settings and incidents may well result in patient harm. Information about risks and incidents can contribute to improved patient safety, provided that those are reported and analysed systematically. Objectives: To identify the frequency of incidents when using AMTs in home settings, the effects on patient outcomes and the actions taken by nurses following identification of incidents. Methods: A cross-sectional study of 209 home care nurses in the Netherlands working with infusion therapy, parenteral nutrition or morphine pumps, combining data from a questionnaire and registration forms covering more than 13 000 patient contacts. Descriptive statistics were used. Results: We identified 140 incidents (57 adverse events; 83 near misses). The frequencies in relation to the number of patient contacts were 2.7% for infusion therapy, 1.3% for parenteral nutrition and 2.6% for morphine pumps. The main causes were identified as related to the product (43.6%), the organisation of care (27.9%), the nurse as a user (15.7%) and the environment (12.9%). 40% of all adverse events resulted in mild to severe harm to the patient. Incidents had been discussed in the team (70.7%), with the patient/informal caregiver(s) (50%), or other actions had been taken (40.5%). 15.5% of incidents had been formally reported according to the organisation's protocol. Conclusions: Most incidents are attributed to product failures. Although such events predominantly cause no harm, a significant proportion of patients do suffer some degree of harm. There is considerable underreporting of incidents with AMTs in home care. This study has identified a discrepancy in quality circles: learning takes place at the team level rather than at the organisational level.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
