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Purpose This study aims to (1) describe the health-related quality of life (HRQoL) outcomes experienced by children born very preterm (28–31 weeks’ gestation) and extremely preterm ( Methods This investigation was based on data for 3687 children born at ™ GCS scores. A mediation analysis that applied generalised structural equation modelling explored the association between potential mediators and PedsQL™ GCS scores. Results The multi-level OLS regression (fully adjusted model) revealed that birth at ™ GCS score of 0.35, 3.71 and 5.87, respectively. The mediation analysis revealed that the indirect effects of BPD and severe non-respiratory morbidity on the total PedsQL™ GCS score translated into decrements of 1.73 and 17.56, respectively, at Conclusion The findings suggest that HRQoL is particularly impaired by extremely preterm birth and the concomitant complications of preterm birth such as BPD and severe non-respiratory morbidity.

Joyce S Ramos,1,2 Lance C Dalleck,2,3 Rebecca C Stennett,1 Gregore I Mielke,1 Shelley E Keating,1 Lydia Murray,4 Sumaira Z Hasnain,4,5 Robert G Fassett,1 Michael McGuckin,4,6 Ilaria Croci,1,7 Jeff S Coombes1 1Centre for Research on Exercise, Physical Activity and Health, School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Queensland, Australia; 2Caring Futures Institute and SHAPE Research Centre, Exercise Science and Clinical Exercise Physiology, College of Nursing and Health Sciences, Flinders University, Adelaide, South Australia, Australia; 3Recreation, Exercise, and Sport Science Department, Western State Colorado University, Gunnison, Colorado, USA; 4Immunopathology Group, Mater Research Institute - The University of Queensland, Translational Research Institute, Brisbane, Queensland, Australia; 5Australian Infectious Disease Research Centre, University of Queensland, Brisbane, Queensland, Australia; 6Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia; 7K.G. Jebsen Center of Exercise in Medicine, Department of Circulation and Medical Imaging, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Sor Trondelag, NorwayCorrespondence: Joyce S Ramos Email joyce.ramos@flinders.edu.auIntroduction: IL-22 may have a role in the alleviation of the metabolic syndrome (MetS) via protection of pancreatic beta and endothelial cells from oxidative and lipid-induced damage. We aimed to investigate the effects of moderate-intensity continuous training (MICT) and different volumes of high-intensity interval training (HIIT) on changes in circulating IL-22.Methods: This was a sub-study of the “Exercise in the prevention of Metabolic Syndrome” (EX-MET) a multi-center, randomized trial. This study used data collected at the Brisbane site. Thirty-nine individuals with MetS were randomized to one of three 16-wk interventions: 1) MICT (n=10, 30min at 60– 70% HR peak, 5x/wk); 2) 4HIIT (n=13, 4x4min at 85– 95% HR peak, interspersed with 3min of active recovery at 50– 70% HR peak, 3x/wk); or 3) 1HIIT (n=16, 1x4min at 85– 95% HR peak, 3x/wk). Serum IL-22 concentration was measured following a 12-hr fast via an enzyme linked immunosorbent assay, before and after the intervention. MetS severity, insulin resistance (IR), visceral adipose tissue (VAT), and cardiorespiratory fitness (CRF) were also measured via MetS z-score, HOMA-IR, dual-energy X-ray absorptiometry, and indirect calorimetry (maximal exercise test), respectively.Results: The median (IQR) IL-22% changes from pre- to post-intervention in the MICT, 4HIIT, and 1HIIT groups were − 17% (− 43.0% to 31.3%), +16.5% (− 18.9% to 154.9%), and +15.9% (− 28.7% to 46.1%), respectively. Although there were no significant between-group differences in IL-22 concentration change, there was a medium-to-large group × time interaction effect [F(2,35)=2.08, p=0.14, η2=0.14].Conclusion: Although there was no statistically significant between-group difference in IL-22 change, the study suggests that different exercise intensities may have opposing effects on IL-22 concentration in individuals with MetS.Keywords: high-intensity interval training, IL-22, inflammation, cardiovascular disease, metabolic syndrome, cytokine

Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

AIM: It is well established that exercise is beneficial for prostate cancer survivors. The challenge for health professionals is to create effective strategies to encourage survivors to exercise in the community. Many community exercise programs are brief in duration (e.g.

Introduction: Quality of life in childhood cancer survivors is largely affected by survivorship care and transition from treatment to long-term follow-up (LTFU). Referring to evidence-based recommendations, we wanted to evaluate LTFU care for survivors through a survey among the Italian Association for Pediatric Hematology-Oncology (AIEOP) centers. The project aimed to evaluate the availability of services in Italy, investigate strengths and weaknesses, analyze improvements of awareness in the field, and identify the gaps that need to be addressed by different centers. Methods: Together with the family representatives, on behalf of AIEOP's Late Effects Working Group, we developed a questionnaire on assisting childhood cancer survivors. All AIEOP centers received one questionnaire including information on local health system organizations; LTFU for childhood cancer survivors; services for adult survivors of childhood cancer; information provided to survivors/caregivers and care plan delivery. Results: Forty-eight AIEOP centers were contacted and 42 replied, with a response rate of 87.5%. The majority of respondents confirmed their interest in assisting patients with a survivorship care plan (95.2%), regardless of a clinic or dedicated staff. Discussion: This is the first overview of LTFU in Italy, which provides detailed results at national levels, prompting consideration of improvements in the last decade. Although there is a high level of interest in survivorship care, many centers lack resources to implement such programs. The identification of these challenges is useful for planning future strategies.

Background: Advanced cancer patients often die in hospital after receiving needless, aggressive treatment. Although palliative care improves symptom management, barriers to accessing palliative care services affect its utilisation, and such disparities challenge the equitable provision of palliative care. This study aimed to identify which factors are associated with inequitable palliative care service utilisation among advanced cancer patients by applying the Andersen Behavioural Model of Health Services Use.Material and methods: This was a retrospective cohort study using administrative healthcare data. A total of 13,656 patients residing in the Lazio region of Italy, who died of an advanced cancer-related cause-either in hospital or in a specialised palliative care facility-during the period of 2012-2016 were included in the study. Potential predictors of specialised palliative service utilisation were explored by grouping the following factors: predisposing factors (i.e., individuals' characteristics), enabling factors (i.e., systemic/structural factors) and need factors (i.e., type/severity of illness).Results: The logistic hierarchical regression showed that older patients (odds ratio [OR] = 1.45

Background.Many instruments have been developed and validated to assess the stigma associated with mental disorders and its various domains across different populations. To our knowledge, the Reported and Intended Behaviour Scale (RIBS) is the only validated questionnaire to analyse the presence of reported and intended stigmatising/discriminatory behaviours towards people with mental health problems in the general population. The aims of the study presented herein are to translate and validate the RIBS in Italian language and to adapt it to the Italian socio-cultural background (RIBS-I).Method.The RIBS considers reported and intended behaviours across four different domains: (1) living with, (2) working with, (3) living nearby and (4) continuing a relationship with someone with a mental health problem. The validation process included four phases: (1) translation/back translation of the questionnaire from English to Italian and vice versa; (2,3) face validity and reliability of RIBS-I; (4) description of model fit through confirmatory factor analysis. The questionnaire was administered to a sample of the general public via distribution in public places such as shopping centres, markets, squares, cinemas and other gathering places. Questionnaires were administered by trained mental health professionals.Results.A total of 447 lay respondents were recruited. The mean age was 38.08 (s.d. = ±14.74) years. Fifty-seven per cent of the sample (n = 257) were female. The Cronbach alpha of RIBS-I was 0.83. All indices of model fit were above the reference values: Goodness of Fit Index (GFI) = 0.987 (GFI > 0.9); Adjusted Goodness of Fit Index (AGFI) = 0.975 (AGFI > 0.9); Comparative Fit Index (CFI) = 0.994 (CFI > 0.9); and Root-Mean-Square Error of Approximation (RMSEA) = 0.023 (RMSEA < 0.05). The χ2 = 23.60 (df = 19; p = 0.21) and χ2/df = 1.24 supported the model.Conclusions.The RIBS-I demonstrated good psychometric properties and it can be considered a useful tool to: (1) assess stigmatising (actual or potential) behaviours in the general population; (2) test the efficacy of anti-stigma campaigns and actions; (3) design further studies to better understand the relationship between the three different components of stigmatisation: knowledge, attitudes and behaviours.