Linking cardiorespiratory fitness classification criteria to early subclinical atherosclerosis in children

It is unclear if cardiorespiratory fitness (CRF) can be used as a screening tool for premature changes in carotid intima-media thickness (cIMT) in paediatric populations. The purpose of this cross-sectional study was 3-fold: (i) to determine if CRF can be used to screen increased cIMT; (ii) to determine an optimal CRF cut-off to predict increased cIMT; and (iii) to evaluate its ability to predict increased cIMT among children in comparison with existent CRF cut-offs. cIMT was assessed with high-resolution ultrasonography and CRF was determined using a maximal cycle test. Receiver operating characteristic analyses were conducted in boys (n = 211) and girls (n = 202) aged 11-12 years to define the optimal sex-specific CRF cut-off to classify increased cIMT (≥75th percentile). Logistic regression was used to examine the association between the CRF cut-offs with the risk of having an increased cIMT. The optimal CRF cut-offs to predict increased cIMT were 45.81 and 34.46 mL·kg(-1)·min(-1) for boys and girls, respectively. The odds-ratios for having increased cIMT among children who were unfit was up to 2.8 times the odds among those who were fit (95% confidence interval: 1.40-5.53). Considering current CRF cut-offs, only those suggested by Adegboye et al. 2011. (Br. J. Sports Med. 45(9): 722-728) and Boddy et al. 2012 (PLoS One, 7(9): e45755) were significant in predicting increased cIMT. In conclusion, CRF cut-offs (boys: ≤ 45.8; girls: ≤ 34.5 mL·kg(-1)·min(-1)) are associated with thickening of the arterial wall in 11- to 12-year-old children. Low CRF is an important cardiovascular risk factor in children and our data highlight the importance of obtaining an adequate CRF.info:eu-repo/semantics/publishedVersio

Potencialidades das rochas graníticas no concelho de Nisa

O presente texto visa dar a conhecer os resultados obtidos a respeito das potencialidades das rochas graníticas existentes na área do município. Estes resultados foram obtidos no âmbito de um trabalho mais vasto de inventariação, caracterização e avaliação das potencialidades em recursos minerais do concelho de Nisa, o qual foi objecto de um protocolo de colaboração com carácter pioneiro entre a autarquia e o LNEG

Application of hyaluronic acid in non-surgical treatment of periodontitis

Poster apresentado no 2º Congresso Internacional do CiiEM: Translational Research and Innovation in Human in health Sciences. 11-13 Junho 2017, Campus Egas Moniz, Caparica, PortugalN/

Short-term water demand forecasting using machine learning techniques

Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Shortterm water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.publishe

Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24–48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.by UE/FEDER through the program COMPETE 2020 and UID/EMS/00481/2013-FCT under CENTRO-01-0145-FEDER- 022083publishe

What Has Changed During the COVID-19 Pandemic? - The Effect on an Academic Breast Department in Portugal

Introduction: One year ago, Portugal entered its first lockdown because of the coronavirus disease-2019 (COVID-19) pandemic. The impact of this on delays in cancer diagnosis and treatment is a major concern, which may negatively affect the outcomes of these patients. Materials and methods: This retrospective, single-center analysis compared the clinical and pathological characteristics of breast cancer (BC) patients referred to a medical oncology first appointment between March 2020 and 2021, with the same period in the previous year. Results: Strikingly, there was a 40% reduction in the number of BC patients during lockdown. However, there was a statistically significant increase in the proportion of metastatic BC patients admitted for the first time for systemic therapy (13.6% vs. 28.9%, p = 0.003). Additionally, a statistically significant increase in the number of patients with bilateral early BC at diagnosis after March 2020 was found (7.2% vs. 1.9%, p = 0.043). Conclusion: These findings support international recommendations for an accelerated restoration of BC screening, to reduce incidence of advanced breast cancer at diagnosis and mitigate the expected impact of the COVID-19 pandemic on patients with cancer. Further work is needed to examine in detail the impact of measures to manage the COVID-19 pandemic on breast cancer outcomes.info:eu-repo/semantics/publishedVersio