1,720,996 research outputs found
The Difficulties in Emotional Regulation among a Cohort of Females with Lipedema
No full textBackground: Lipedema is a chronic and progressive adipose tissue disorder that causes significant morbidity and negatively influences mental health and quality of life, and increases the risk of depression, anxiety, and eating disorders. One construct of relevance to better understanding psychological disorders is emotion regulation (ER). Therefore, the aim of this study is to investigate the difficulties in ER among lipedema patients compared to healthy people without lipedema. Methods: This cross-sectional study assessed differences in ER and anxiety between two groups: 26 female patients with lipedema and 26 sex- and age-matched healthy controls. The Difficulties in Emotion Regulation Scale (DERS) assessed emotional regulation across six dimensions: Impulse control, goal-directed behavior, awareness, clarity, non-acceptance, and strategies. Anxiety was assessed by the Hamilton Anxiety Scale (HAM-A). ANOVA assessed differences in measures between lipedema and healthy control groups. Results: Lipedema patients presented with significantly more difficulties in ER and a higher level of anxiety than those without lipedema. Specifically, the lipedema group showed higher and significant differences in total DERS and anxiety scores and all DERS subscales scores compared to those without lipedema. Conclusions: Lipedema patients showed significant difficulties with ER, and were associated with anxiety symptoms, indicating that ER difficulties may play a role in developing emotional disorders, such as anxiety, for patients with lipedema. The health care provider should pay more attention to ER difficulties and psychological status among lipedema patients
The association between serum vitamin D and mood disorders in a cohort of lipedema patients
No full textThe association between serum Vitamin D (Vit. D) and mood disorders in lipedema patients has not been investigated. Therefore, the main aim of this study is to investigate the correlation between serum Vit. D, depression and anxiety risk.Objectives: The association between serum Vitamin D (Vit. D) and mood disorders in lipedema patients has not been investigated. Therefore, the main aim of this study is to investigate the correlation between serum Vit. D, depression and anxiety risk. Methods: A cross-sectional cohort of lipedema patients were investigated by collecting the clinical and demographic data. The Hamilton Depression Scale (HAM-D) and the Hamilton of Anxiety Scale (HAM-A) were used to evaluating the risk of depression and anxiety. Serum concentrations of Vit. D were measured. The association between Vit. D levels and both HAM-A and HAM-D scores were statistically examined by bivariate and partial correlations. Results: Forty lipedema patients were enrolled in this study. Around two-thirds of them had a higher depression or anxiety risk, and 77.5% were under the normal serum Vit. D levels. A significant and inverse correlation was observed between serum Vit. D levels and both HAM-D (r=-0.661, p<0.001), and HAM-A (r=-0.496, p=0.001) scores. This strong association was sustained after the statistical model adjusted for the main potential confounding factors (age, body mass index (BMI), disease duration, and lipedema stages). Additionally, serum Vit. D correlated significantly and inversely with BMI (r=-0.647, p<0.001). Moreover, BMI significantly correlated with HAM-D: r=0.560, p<0.001, and HAM-A: r=0.511, p=0.00. Conclusions: This study suggests a strong correlation between Vit. D levels, depression scores, and anxiety scores in lipedema patients. Our results also demonstrate a strong and direct relationship between BMI, Vit. D levels, depression, and anxiety
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Voice in Parkinson's Disease: A Machine Learning Study
Full text linkIntroduction: Parkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy. Methods: We investigated 115 patients affected by PD (mean age: 68.2 ± 9.2 years) and 108 age-matched healthy subjects (mean age: 60.2 ± 11.0 years). The PD cohort included 57 early-stage patients (Hoehn &Yahr ≤ 2) who never took L-Dopa for their disease at the time of the study, and 58 mid-advanced-stage patients (Hoehn &Yahr >2) who were chronically-treated with L-Dopa. We clinically evaluated voices using specific subitems of the Unified Parkinson's Disease Rating Scale and the Voice Handicap Index. Voice samples recorded through a high-definition audio recorder underwent machine learning analysis based on the support vector machine classifier. We also calculated the receiver operating characteristic curves to examine the diagnostic accuracy of the analysis and assessed possible clinical-instrumental correlations. Results: Voice is abnormal in early-stage PD and as the disease progresses, voice increasingly degradres as demonstrated by high accuracy in the discrimination between healthy subjects and PD patients in the early-stage and mid-advanced-stage. Also, L-dopa therapy improves but not restore voice in PD as shown by high accuracy in the comparison between patients OFF and ON therapy. Finally, for the first time we achieved significant clinical-instrumental correlations by using a new score (LR value) calculated by machine learning. Conclusion: Voice is abnormal in early-stage PD, progressively degrades in mid-advanced-stage and can be improved but not restored by L-Dopa. Lastly, machine learning allows tracking disease severity and quantifying the symptomatic effect of L-Dopa on voice parameters with previously unreported high accuracy, thus representing a potential new biomarker of PD
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson's Disease
No full textBackground: Technology-based objective measures (TOMs) recently gained relevance to support clinicians in the assessment of motor function in Parkinson's disease (PD), although limited data are available in the early phases.Objective: To assess motor performances of a population of newly diagnosed, drug free PD patients using wearable inertial sensors and to compare them to healthy controls (HC) and differentiate different PD subtypes [tremor dominant (TD), postural instability gait disability (PIGD), and mixed phenotype (MP)].Methods: We enrolled 65 subjects, 36 newly diagnosed, drug-free PD patients and 29 HCs. PD patients were clinically defined as tremor dominant, postural instability-gait difficulties or mixed phenotype. All 65 subjects performed seven MDSUPDRS III motor tasks wearing inertial sensors: rest tremor, postural tremor, rapid alternating hand movement, foot tapping, heel-to-toe tapping, Timed-Up-and-Go test (TUG) and pull test. The most relevant motor tasks were found combining ReliefF ranking and Kruskal-Wallis feature-selection methods. We used these features, linked to the relevant motor tasks, to highlight differences between PD from HC, by means of Support Vector Machine (SVM) classifier. Furthermore, we adopted SVM to support the relevance of each motor task on the classification accuracy, excluding one task at time.Results: Motion analysis distinguished PD from HC with an accuracy as high as 97%, based on SVM performed with measured features from tremor and bradykinesia items, pull test and TUG. Heel-to-toe test was the most relevant, followed by TUG and pull test.Conclusions: In this pilot study, we demonstrate that the SVM algorithm successfully distinguishes de novo drug-free PD patients from HC. Surprisingly, pull test and TUG tests provided relevant features for obtaining high SVM classification accuracy, differing from the report of the experienced examiner. The use of TOMs may improve diagnostic accuracy for these patients
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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