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Background: The SITLESS programme comprises exercise referral schemes and self-management strategies and has been evaluated in a trial in Denmark, Spain, Germany and Northern Ireland. The aim of this qualitative study was to understand the implementation and contextual aspects of the intervention in relation to the mechanisms of impact and to explore the perceived effects. Methods: Qualitative methodologies were nested in the SITLESS trial including 71 individual interviews and 12 focus groups targeting intervention and control group participants from postintervention to 18-month follow-up in all intervention sites based on a semi-structured topic guide. Results: Overarching themes were identified under the framework categories of context, implementation, mechanisms of impact and perceived effects. The findings highlight the perceived barriers and facilitators to older adults’ engagement in exercise referral schemes. Social interaction and enjoyment through the group-based programmes are key components to promote adherence and encourage the maintenance of targeted behaviours through peer support and connectedness. Exit strategies and signposting to relevant classes and facilities enabled the maintenance of positive lifestyle behaviours. Conclusions: When designing and implementing interventions, key components enhancing social interaction, enjoyment and continuity should be in place in order to successfully promote sustained behaviour change.

Kinases regulate cellular processes and are essential for understanding cellular function and disease. To investigate the regulatory state of a kinase, numerous methods have been developed to infer kinase activities from phosphoproteomics data using kinase-substrate libraries. However, few phosphorylation sites can be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. Moreover, inferred activities vary across methods, necessitating evaluation for accurate interpretation. Here, we present benchmarKIN, an R package enabling comprehensive evaluation of kinase activity inference methods. Alongside classical perturbation experiments, benchmarKIN introduces a tumor-based benchmarking approach utilizing multi-omics data to identify highly active or inactive kinases. We used benchmarKIN to evaluate kinase-substrate libraries, inference algorithms and the potential of adding predicted kinase-substrate interactions to overcome the coverage limitations. Our evaluation shows most computational methods perform similarly, but the choice of library impacts the inferred activities with a combination of manually curated libraries demonstrating superior performance in recapitulating kinase activities. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance. We then demonstrate how kinase activity inference aids characterize kinase inhibitor responses in cell lines. Overall, benchmarKIN helps researchers to select reliable methods for identifying deregulated kinases. Kinases regulate cellular processes, making their study essential for understanding cellular function and disease. Here, the authors evaluate methods to infer kinase activity from phosphoproteomics data and provide a toolkit to evaluate future methods.

Norovirus infections are a leading cause of acute gastroenteritis outbreaks worldwide, with genotypes GII.2 and GII.4 being the most prevalent. The aim of this study was to compare the characteristics of GII.2 and GII.4 norovirus outbreaks reported in Catalonia in closed or semi-closed institutions in 2017 and 2018. The epidemiological and clinical characteristics of GII.2 and GII.4 outbreaks were compared using the chi-square test or Fisher's exact test for categorical variables and the Mann–Whitney U test for continuous variables. Odds ratios and their 95% confidence intervals were estimated. 61 outbreaks were reported: GII.4 was the causative agent in 12 outbreaks (30%) and GII.2 in 9 outbreaks (22.5%). GII.2 outbreaks were detected more frequently in schools or summer camps (66.7%) and GII.4 outbreaks in nursing homes (91.7%) ( p  = 0.01). Ninety-three people were affected in GII.2 outbreaks and 94 in GII.4 outbreaks. The median age was 15 years (range: 1–95 years) in GII.2 outbreaks and 86 years (range: 0–100 years) in GII.4 outbreaks ( p  < 0.001). Nausea, abdominal pain, and headache were observed more frequently in persons affected by GII.2 outbreaks ( p  < 0.05). Symptomatic cases presented a higher viral load suggestive of greater transmission capacity, although asymptomatic patients presented relevant loads indicative of transmission capacity.

Objective To assess neurofilaments as neurodegenerative biomarkers in serum of patients with Friedreich’s ataxia. Methods Single molecule array measurements of neurofilament light (NfL) and heavy chain (pNfH) in 99 patients with genetically confirmed Friedreich’s ataxia. Correlation of NfL/pNfH serum levels with disease severity, disease duration, age, age at onset, and GAA repeat length. Results Median serum levels of NfL were 21.2 pg/ml (range 3.6–49.3) in controls and 26.1 pg/ml (0–78.1) in Friedreich’s ataxia ( p  = 0.002). pNfH levels were 23.5 pg/ml (13.3–43.3) in controls and 92 pg/ml (3.1–303) in Friedreich’s ataxia ( p  = 0.0004). NfL levels were significantly increased in younger patients (age 16–31 years, p  < 0.001) and patients aged 32–47 years ( p  = 0.008), but not in patients of age 48 years and older ( p  = 0.41). In a longitudinal assessment, there was no difference in NfL levels in 14 patients with repeated sampling 2 years after baseline measurement. Levels of NfL correlated inversely with GAA1 repeat length ( r  = − 0.24, p  = 0.02) but not with disease severity ( r  = − 0.13, p  = 0.22), disease duration ( r  = − 0.06, p  = 0.53), or age at onset ( r  = 0.05, p  = 0.62). Conclusion Serum levels of NfL and pNfH are elevated in Friedreich’s ataxia, but differences to healthy controls decrease with increasing age. Long-term longitudinal data are required to explore whether this reflects a selection bias from early death of more severely affected individuals or a slowing down of the neurodegenerative process with age. In a pilot study over 2 years of follow-up—a period relevant for biomarkers indicating treatment effects—we found NfL levels to be stable.

Kinases play a central role in regulating cellular processes, making their study essential for understanding cellular function and disease mechanisms. To investigate the regulatory state of a kinase, numerous methods have been, and continue to be, developed to infer kinase activities from phosphoproteomics data. These methods usually rely on a set of kinase targets collected from various kinase-substrate libraries. However, only a small percentage of measured phosphorylation sites can usually be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. In addition, the inferred activities from different methods can vary making it crucial to evaluate them for accurate interpretation. Here, we present a comprehensive evaluation of kinase activity inference methods using multiple kinase-substrate libraries combined with different inference algorithms. Additionally, we try to overcome the coverage limitations for measured targets in kinase substrate libraries by adding predicted kinase-substrate interactions for activity inference. For the evaluation, in addition to classical cell-based perturbation experiments, we introduce a tumor-based benchmarking approach that utilizes multi-omics data to identify highly active or inactive kinases per tumor type. We show that while most computational algorithms perform comparably regardless of their complexity, the choice of kinase-substrate library can highly impact the inferred kinase activities. Hereby, manually curated libraries, particularly PhosphoSitePlus, demonstrate superior performance in recapitulating kinase activities from phosphoproteomics data. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance, while normalizing sites to host protein levels reduces kinase activity inference performance. We then showcase how kinase activity inference can help in characterizing the response to kinase inhibitors in different cell lines. Overall, the selection of reliable kinase activity inference methods is important in identifying deregulated kinases and novel drug targets. Finally, to facilitate the evaluation of novel methods in the future, we provide both benchmarking approaches in the R package benchmarKIN.