Objectives To estimate the national prevalence of rheumatic and musculoskeletal diseases (RMDs) in the adult Portuguese population and to determine their impact on health-related quality of life (HRQoL), physical function, anxiety and depression. Methods EpiReumaPt is a national health survey with a three-stage approach. First, 10 661 adult participants were randomly selected. Trained interviewers undertook structured face-to-face questionnaires that included screening for RMDs and assessments of health-related quality of life, physical function, anxiety and depression. Second, positive screenings for ≥1 RMD plus 20% negative screenings were invited to be evaluated by a rheumatologist. Finally, three rheumatologists revised all the information and confirmed the diagnoses according to validated criteria. Estimates were computed as weighted proportions, taking the sampling design into account. Results The disease-specific prevalence rates (and 95% CIs) of RMDs in the adult Portuguese population were: low back pain, 26.4% (23.3% to 29.5%); periarticular disease, 15.8% (13.5% to 18.0%); knee osteoarthritis (OA), 12.4% (11.0% to 13.8%); osteoporosis, 10.2% (9.0% to 11.3%); hand OA, 8.7% (7.5% to 9.9%); hip OA, 2.9% (2.3% to 3.6%); fibromyalgia, 1.7% (1.1% to 2.1%); spondyloarthritis, 1.6% (1.2% to 2.1%); gout, 1.3% (1.0% to 1.6%); rheumatoid arthritis, 0.7% (0.5% to 0.9%); systemic lupus erythaematosus, 0.1% (0.1% to 0.2%) and polymyalgia rheumatica, 0.1% (0.0% to 0.2%). After multivariable adjustment, participants with RMDs had significantly lower EQ5D scores (β=−0.09; p<0.001) and higher HAQ scores (β=0.13; p<0.001) than participants without RMDs. RMDs were also significantly associated with the presence of anxiety symptoms (OR=3.5; p=0.006). Conclusions RMDs are highly prevalent in Portugal and are associated not only with significant physical function and mental health impairment but also with poor HRQoL, leading to more health resource consumption. The EpiReumaPt study emphasises the burden of RMDs in Portugal and the need to increase RMD awareness, being a strong argument to encourage policymakers to increase the amount of resources allocated to the treatment of rheumatic patients.

Rare disease research faces significant challenges due to limited patient data, strict privacy regulations, and the need for diverse datasets to develop accurate AI-driven diagnostics and treatments. Synthetic data—artificially generated datasets that mimic patient data while preserving privacy—offer a promising solution to these issues. This article explores how synthetic data can bridge data gaps, enabling the training of AI models, simulating clinical trials, and facilitating cross-border collaborations in rare disease research. We examine case studies where synthetic data successfully replicated patient characteristics, and supported predictive modelling and ensured compliance with regulations like GDPR and HIPAA. While acknowledging current limitations, we discuss synthetic data’s potential to revolutionise rare disease research by enhancing data availability and privacy file enabling more efficient and effective research efforts in diagnosing, treating, and managing rare diseases globally.

Spatial and temporal patterns of large fire (>100 ha) incidence in Portugal over the period 1984–2004 were modeled using extreme value statistics, namely the Peaks Over Threshold approach, which uses the Generalized Pareto Distribution (GPD) as a model. The original dataset includes all fires larger than 5 ha (30 616 fires) that were observed in Portugal during the study period, mapped from Landsat satellite imagery. The country was divided into eight regions, considered internally homogeneous from the perspective of their fire regimes and respective environmental correlates. The temporal analysis showed that there does not appear to be any trend in the incidence of very large fires, but revealed a cyclical behavior in the values of the GPD shape parameter, with a period in the range of 3 to 5 years. Spatial analysis highlighted strong regional differences in the incidence of large fires, and allowed the calculation of return levels for a range of fire sizes. This analysis was affected by the presence of a few outlying observations, which may correspond to clusters of contiguous fire scars, resulting in artificially large burned areas. We discuss some of the implications of our findings in terms of consequences for fire management aimed at preventing the occurrence of extremely large fires, and present ideas for extending the present study.

Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging compared to other industries. A promising approach is to combine deep neural networks (DNN) with prior knowledge in hybrid neural network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It reveals that HNNs have been applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs have been applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies have combined shallow feedforward neural networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, convolution neural networks (CNN), long short-term memory (LSTM) networks and physics-informed neural networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.

A 14-Item Mediterranean Diet Adherence Screener (MEDAS) questionnaire was developed and validated in face-to-face interviews, but not via telephone. The aims of this study were to evaluate the validity and reliability of a telephone-administered version of the MEDAS as well as to validate the Portuguese version of the MEDAS questionnaire. A convenience community-based sample of adults (n = 224) participated in a three-stage survey. First, trained researchers administered MEDAS via a telephone. Second, the Portuguese version of Food Frequency Questionnaire (FFQ), and MEDAS were administered in a semi-structured face-to-face interview. Finally, MEDAS was again administered via telephone. The telephone-administered MEDAS questionnaire was compared with the face-to-face-version using several metrics. The telephone-administered MEDAS was significantly correlated with the face-to-face-administered MEDAS [r = 0.805, p < 0.001; interclass correlation coefficient (ICC) = 0.803, p < 0.001] and showed strong agreement (k = 0.60). The MEDAS scores that were obtained in the first and second telephone interviews were significantly correlated (r = 0.661, p < 0.001; ICC = 0.639, p < 0.001). The overall agreement between the Portuguese version of MEDAS and the FFQ-derived Mediterranean diet adherence score had a Cohen’s k = 0.39. The telephone-administered version of MEDAS is a valid tool for assessing the adherence to the Mediterranean diet and acquiring data for large population-based studies.

The World Mental Health Survey Initiative was designed to evaluate the prevalence, the correlates, the impact and the treatment patterns of mental disorders. This paper describes the rationale and the methodological details regarding the implementation of the survey in Portugal, a country that still lacks representative epidemiological data about psychiatric disorders.