Search

Background Worldwide, very few studies have been published on the prevalence of acne among university students. Objective The main objective of this study was to estimate the prevalence of acne among Portuguese medical students. We also assessed associated factors and opinions. Methods Participants were recruited among 145 fifth year medical students. They were asked to complete a questionnaire, and were observed to grade their acne. Acne grading was done using Pillsbury criteria. Results Ninety-eight students were assessed and returned the self-completed questionnaire, 67 (68%) were female students and 31 (32%) male students. The observed prevalence of acne was 61 students (62.2%); 42 (69%) and 18 (30%) had acne grades I and II, respectively; only one was classified as grade III. The prevalence of acne was not significantly associated with gender ( P = 0.72), family history of acne ( P = 0.65), greasy hair ( P = 0.18), smoking habits ( P = 0.25) or self-perceived presence of acne ( P = 0.48). Regularity of menstrual cycles was not associated ( P = 0.73) with the presence of acne among the 67 women. The majority thought that acne strongly affects self-image and, to a much lesser extent, personal relationships, academic performance or recruitment to a job. Conclusion The prevalence of acne in this study was higher than that observed in previous studies among medical students and other non-university young adults. Opinions about the causes of acne (with hormones in top of the list) were similar to previous studies. Our study sample perceived more serious consequences for self-image than university students elsewhere and younger Portuguese school children. [ABSTRACT FROM AUTHOR]

Unmanned vehicles have already proved invaluable in environmental field studies by providing levels of spatial-temporal sampling resolution which could have not been attained before. Recent trends show that the levels of spatial-temporal sampling resolutions attained with individual vehicles are feasible for wide areas through the operation of persistent vehicle networks. The possibility of persistent sampling over wide areas has the potential to revolutionize environmental field studies. The roles of unmanned vehicle systems in future environmental field studies are discussed in the light of the recent technological developments and trends, along with the major challenges associated to this vision. The discussion is illustrated with examples of developments from the Underwater Systems and Technologies Laboratory from Porto University, Portugal. [ABSTRACT FROM AUTHOR]

Healthcare professionals' high risk of infection and burnout in the first months of the COVID-19 pandemic probably hindered their much-needed preparedness to respond. We aimed to inform how individual and institutional factors contributed for the preparedness to respond during the first months of a public health emergency. Cross-sectional study. We surveyed healthcare workers from a Local Health Unit in Portugal, which comprises primary health care centers and hospital services, including public health units and intensive care units, in the second and third months of the COVID-19 epidemic in Portugal. The 460 answers, completed by 252 participants (about 10% of the healthcare workers), were analyzed using descriptive statistics and multiple logistic regressions. We estimated adjusted odds ratios for the readiness and willingness to respond. Readiness to respond was associated with the perception of adequate infrastructures (aOR = 4.04, P < 0.005), lack of access to personal protective equipment (aOR = 0.26, P < 0.05) and organization (aOR = 0.31, P < 0.05). The willingness to act was associated with the perception of not being able to make a difference (aOR = 0.05, P < 0.005), risk of work-related burnout (aOR = 21.21, P < 0.01) and experiencing colleagues or patients' deaths due to COVID-19 (aOR = 0.24, P < 0.05). Adequate organization, infrastructures, and access to personal protective equipment may be crucial for workers' preparedness in a new public health emergency, as well workers' understanding of their roles and expected impact. These factors, together with the risk of work-related burnout, shall be taken into account in the planning of the response of healthcare institutions in future public health emergencies. [ABSTRACT FROM AUTHOR]

[Display omitted] • From the multispectral bands, the NIR spectrum alone is generally sufficient to obtain satisfactory performance on most datasets. • High-resolution RGB images have equivalent or higher performance than multispectral images. • DL segmentation networks outperform classical methods in vine plant segmentation. • SegNet, U-Net and ModSegNet have equivalent overall performance. Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available on https://github.com/Cybonic/DL_vineyard_segmentation_study.git. [ABSTRACT FROM AUTHOR]