Background Cytological analysis is part of the initial etiological evaluation of serous effusions. The newly proposed International System for Reporting Serous Fluid Cytopathology (ISRSFC) aims to standardize reporting. Methods All pleural and peritoneal effusion samples admitted for cytological analysis at our institution between 2012 and 2016, and pericardial effusion samples admitted between 2008 and 2018, were reviewed and reclassified according to the ISRSFC. Risk of malignancy (ROM) and performance parameters were calculated. Results 1496 pleural effusion samples were reclassified: 12(0.8%) non‐diagnostic (ND), 944(63.1%) negative for malignancy (NFM), 9(0.6%) atypia of undetermined significance (AUS), 54(3.6%) suspicious of malignancy (SFM) and 477(31.9%) malignant (M). 64 pericardial effusion samples were reclassified: 23(35.9%) NFM, 1(1.6%) AUS, 4(6.3%) SFM and 36(56.2%) M. 763 peritoneal effusion samples were reclassified: 5(0.7%) ND, 457(59.9%) NFM, 12(1.6%) AUS, 37(4.8%) SFM and 252(33%) M. The ROM was, respectively, for each of the aforementioned categories, 57.1%, 23.9%, 50%, 76.2%, 100% in pleural effusions, 100%, 26.3%, 62.5%, 91.7%, 100% in peritoneal effusions and 0% for NFM, 0% for AUS and 100% for M in pericardial effusions. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy were, respectively, 61.6%, 100%, 100%, 73.3%, 81.3% for pleural, 100%, 100%, 100%, 100%, 100% for pericardial and 61.2%, 100%, 100%, 70%, 79.7% for peritoneal effusion samples. Conclusion Serous effusion cytology has a high specificity and positive predictive value and a modest sensitivity and negative predictive value, supporting its role in confirming the diagnosis of malignancy. The ISRSFC will increase standardization and reproducibility in reporting, leading to improved clinical decision‐making.

Fibromyalgia (FM) is a chronic non-degenerative disease, whose nutritional therapy seems controversial. This systematic review aimed to synthesize the knowledge about the effect of dietary interventions on patient-reported outcomes (PRO) and inflammation in patients with FM. Six electronic databases -PubMed, BioMed Central, Cochrane library, EMBASE, LILACS and ISIwere searched for clinical trials, in which a dietary intervention in patients with FM diagnosed was conducted. Quality of evidence assessment was measured in accordance with GRADE methodology. Seven clinical trials -3 randomized controlled trials, 1 unrandomized clinical trial and 3 uncontrolled clinical trials were identified. Dietary approaches included gluten-free diet (n ¼ 1), raw vegetarian diet (n ¼ 2), low Fermentable oligo-, di-and monossacharides, alcohols and polyols (FODMAPs) diet (n ¼ 1), hypocaloric diet (n ¼ 2) and monosodium glutamate-and aspartamefree diet interventions (n ¼ 1). The major PRO were pain and functional repercussion, with 5 out of 7 studies reporting an improvement. The progress in secondary outcomes was reported for fatigue (2/5 studies), sleep quality (2/3 studies), depression and anxiety (3/6 studies), quality of life (4/5 studies), gastrointestinal symptoms (1/2 studies) and inflammatory biomarkers (1/1 study). However, according to Cochrane Risk of Bias, these studies had poor statistical quality. Well-designed studies should be performed to investigate the dietary interventions effect on FM. KEY MESSAGESFibromyalgia (FM) is a chronic non-degenerative disease, whose nutritional therapy seems controversial but promising. Pain and functional repercussion in FM patients seem to improve with a hypocaloric diet, a raw vegetarian diet or a low FODMAPs diet, as much as quality of life, quality of sleep, anxiety and depression and inflammatory biomarkers. Existing studies in this subject are scarce and low quality, which does not allow conclusions to be drawn.

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Pedestrian modelling and simulation play a fundamental role in reducing traffic risks and new policies implementation costs. However, representing human behaviour in this dynamic environment is not a trivial task and such models require an accurate representation of pedestrian behavior. Virtual environments have been gaining notoriety as a behaviour elicitation tool, but it is still necessary to understand the validity of this technique in the context of pedestrian studies, as well as to create guidelines for its use. This work proposes a proper methodology for pedestrian behavior elicitation using virtual reality environments in conjunction with surveys or questionnaires. The methodology focuses on gathering data about the subject, the context and the action taken; analyzing the collected data, to finally output a behavioral model. The resulting model can be used as a feedback signal to improve environment conditions for experiment iterations. A concrete implementation was built based on this methodology, serving as an example for future studies. A virtual reality traffic environment and two surveys were used as data sources for pedestrian crossing experiments. The subjects controlled a virtual avatar using an HTC Vive and were tasked with traversing between two points of a city. The data collected during the experiment was analyzed and used as input to a machine learning model capable of predicting pedestrians speed, taking into account their actions and perceptions. The proposed methodology made possible the successful data gathering and its use to predict pedestrian behaviour with high accuracy.

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