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| 1 | An update on iron physiology显示文摘Iron is an essential micronutrient, as it is required for adequate erythropoietic function, oxidative metabolism and cellular immune responses. Although the absorption of dietary iron (1-2 mg/d) is regulated tightly, it is just balanced with losses. Therefore, internal turnover of iron is essential to meet the requirements for erythropoiesis (20-30 mg/d). Increased iron requirements, limited external supply, and increased blood loss may lead to iron deficiency (ID) and iron-deficiency anemia. Hepcidin, which is made primarily in hepatocytes in response to liver iron levels, inflammation, hypoxia and anemia, is the main iron regulatory hormone. Once secreted into the circulation, hepcidin binds ferroportin on enterocytes and macrophages, which triggers its internalization and lysosomal degradation. Thus, in chronic inflammation, the excess of hepcidin decreases iron absorption and prevents iron recycling, which results in hypoferremia and iron-restricted erythropoiesis, despite normal iron stores (functional ID), and anemia of chronic disease (ACD), which can evolve to ACD plus true ID (ACD + ID). In contrast, low hepcidin expression may lead to iron overload, and vice versa. Laboratory tests provide evidence of iron depletion in the body, or reflect iron-deficient red cell production. The appropriate combination of these laboratory tests help to establish a correct diagnosis of ID status and anemia. | Manuel Muoz Isabel Villar José Antonio García-Erce | 2009 | World Journal of Gastroenterology2009，15，37： | 12 |
| 2 | TP53 R249S mutation, genetic variations in HBX and risk of hepatocellular carcinoma in The Gambia显示文摘 | Doriane A. Gouas Stéphanie Villar Sandra Ortiz-Cuaran Pénélope Legros Gilles Ferro Gregory D. Kirk Olufunmilayo A. Lesi Maimuna Mendy Ebrima Bah Marlin D. Friesen John Groopman Isabelle Chemin Pierre Hainaut | 2012 | Carcinogenesis2012，，6： | 1 |
| 3 | TP53 R249S mutation, genetic variations in HBX and risk of hepatocellular carcinoma in The Gambia显示文摘 | Doriane A. Gouas Stéphanie Villar Sandra Ortiz-Cuaran Pénélope Legros Gilles Ferro Gregory D. Kirk Olufunmilayo A. Lesi Maimuna Mendy Ebrima Bah Marlin D. Friesen John Groopman Isabelle Chemin Pierre Hainaut | 2012 | Carcinogenesis2012，，6： | 1 |
| 4 | Production of melatonin by Saccharomyces strains under growth and fermentation conditions显示文摘 | María Isabel Rodriguez‐Naranjo María Jesús Torija Albert Mas Emma Cantos‐Villar María del Carmen Garcia‐Parrilla | 2012 | Journal of Pineal Research2012，，3： | 1 |
| 5 | Cochlear abnormalities in insulin-like growth factor-1 mouse mutants显示文摘 | Guadalupe Camarero M.Angeles Villar Julio Contreras Carmen Fernández-Moreno José G. Pichel Carlos Avenda?o Isabel Varela-Nieto | 2002 | Hearing Research2002，，1： | 1 |
| 6 | Comparison be tween electrochemical capacitors based on NaOH and KOH activated carbon显示文摘 | Silvia Roldan Isabel Villar Vanesa Ruiz | 2010 | Energy Fuel2010，24，6： | 1 |
| 7 | Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble显示文摘Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances. | Muhammad Rizwan Muhammad Faheem Mushtaq Maryam Rafiq Arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay Imran Ashraf | 2024 | Computers, Materials & Continua2024，78，2： | 0 |
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