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BACKGROUND The interaction between early life viral respiratory illness and atopy in the genesis of asthma has been widely discussed in the literature as the 'two hit hypothesis'. OBJECTIVE The aim of this study was to synthesise evidence regarding the association of childhood viral respiratory illness and atopy in the development of persistent wheezing and asthma. METHODS A systematic review was performed, according to PRISMA guidelines. Human studies investigating early life associations between atopy and viral respiratory illness with outcomes of asthma and wheezing were included. Meta-analysis was performed to investigate the association of viral illness across atopic and non-atopic groups. Subgroup analysis was undertaken to investigate potential effect modification of age at outcome. RESULTS Nine cohort studies were included with data available for meta-analysis in four birth cohort studies. There was a stronger association of viral respiratory illness with persistent asthma/wheeze in atopic (OR 4.02, 95%CI 1.46-11.06) compared to non-atopic (OR 2.32, 95%CI 1.22-4.40) individuals, however the evidence for this was limited. In three studies amenable to subanalysis based on outcome age, a stronger effect was observed up to 7 years for those with atopy (OR 7.27, 95%CI 4.65-11.36) compared with those without atopy (OR 3.19, 95%CI 2.09-4.87). DNA Damage inhibitor CONCLUSIONS There was a stronger association between viral respiratory illness and asthma/wheeze outcomes in individuals with atopy as compared to those without atopy . When outcomes were considered at younger ages, a greater differential effect was observed. Within the limitations of the few available studies however, definite conclusions cannot be made . There was also insufficient evidence for differential effects of early versus late atopy. Further research, in particular regarding virus type, timing of atopy and atopic phenotype would contribute to untangling this complex association. Chronic urticaria (CU) and angioedema can occur at any age. While most chronic urticaria with or without angioedema occurs in adults, it can also present in children or the elderly and can complicate pregnancy and breast-feeding. The presentations of chronic urticaria and angioedema are different in children, middle-aged adults and older patients as are the differential diagnoses. Therefore, management of chronic urticaria and angioedema in these different age groups and special populations needs to take into account the age-specific features of urticaria and angioedema. Here, we describe the evaluation, diagnosis and treatment of chronic urticaria and angioedema in children, middle-aged adults and older patients. This review focuses on CU with or without angioedema and does not discuss acute urticaria or bradykinin-mediated angioedema. The National Heart, Lung, and Blood Institute (NHLBI) has played an important role in funding the clinical science that supports many contemporary cardiology practice guidelines and in shaping the conduct of cardiovascular clinical trials. This Perspective outlines contemporary funding options as well as select important NHLBI policies, philosophy, and priorities. Published by Elsevier Inc.The objective of this work is to assess, on metrological basis, the average grey-levels (AVGL) calculated from B-Mode images for estimating temperature variations non-invasively in different kinds of tissues. Thermal medicine includes several thermal therapies, being hyperthermia the most noted and well known. Recently, efforts have been made to understand the benefits of ultrasound hyperthermia at mild temperature levels, i.e., between 39 °C and 41 °C. Moreover, the best practices on ultrasound bio-effects research have been encouraged by recommending that temperature rise in the region of interest should be measured even when a thermal mechanism is not being tested. In this work, the average grey-levels (AVGL) calculated from B-Mode images were assessed for non-invasive temperature estimation in a porcine tissue sample containing two different tissue types, fat and muscle, with temperature varying from 35 °C to 41 °C. The sample was continuously imaged with an ultrasound scanner, and simultaneously the temperature was measured. The achieved results were assessed under the light of the measurement uncertainty in order to allow comparability among different ultrasound thermometry methods. The highest expanded uncertainty of estimating temperature variation using AVGL was determined as 0.68 °C. This work deals with negation detection in the context of clinical texts. Negation detection is a key for decision support systems since negated events (detection of absence of some events) help ascertain current medical conditions. For artificial intelligence, negation detection is a valuable point as it can revert the meaning of a part of a text and, accordingly, influence other tasks such as medical dosage adjustment, the detection of adverse drug reactions or hospital acquired diseases. We focus on negated medical events such as disorders, findings and allergies. From Natural Language Processing (NLP) background, we refer to them as negated medical entities. A novelty of this work is that we approached this task as Named Entity Recognition (NER) with the restriction that just negated medical entities must be recognized (in an attempt to help distinguish them from non-negated ones). Our study is driven with Electronic Health Records (EHRs) written in Spanish. A challenge to cope with is the lexical variability (alternative medical forms, abbreviations, etc.). To this end, we employed an approach based on deep learning. Specifically, the system combines character embeddings to cope with out-of-vocabulary (OOV) words, Long Short-Term Memory (LSTM) networks to model contextual representations and it makes use of Conditional Random Fields (CRF) to classify each medical entity as either negated or not given the contextual dense representation. Moreover, we explored both embeddings created from words and embeddings created from lemmas. The best results were obtained with the lemmatized embeddings. Apparently, this approach reinforced the capability of the LSTMs to cope with the high lexical variability. The f-measure for exact-match was 65.1 and 82.4 for the partial-match.
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