Epiphytic metazoans on emergent macrophytes in oxbow lakes of the Krapina River, Croatia: differences related to plant species and limnological conditions

This study investigated the structure of the epiphytic metazoans on emerged macrophytes in the littoral zone of two oxbow lakes with different trophic levels. Differences in the diversity and density of the epiphytic metazoans were analyzed in relation to plant architecture (simple or complex stems), food resources (algae and detritus) and water characteristics (transparency and derived trophic state index). Asignificant negative correlation was found between detritus on plants as food resource, and diversity and density of epiphytic metazoans, indicating grazing of microphagous species. Rotifers dominated in diversity and density in the epiphyton on all habitats. Total density of metazoans, rotifers and copepods in epiphyton were significantly higher on Mentha in mesotrophic lake than on Iris in a eutrophic lake.We presume that macrophyte belt width and trophic state governed biotic interactions and consequently epiphytic assemblages more strongly than macrophyte architecture. However, a Mentha habitat showed a slightly higher density and diversity of epiphytic metazoans in relation to Iris at the same site, but these differences were not significant

Factor analysis of the Zung self-rating depression scale in a large sample of patients with major depressive disorder in primary care

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to examine the symptomatic dimensions of depression in a large sample of patients with major depressive disorder (MDD) in the primary care (PC) setting by means of a factor analysis of the Zung self-rating depression scale (ZSDS).</p> <p>Methods</p> <p>A factor analysis was performed, based on the polychoric correlations matrix, between ZSDS items using promax oblique rotation in 1049 PC patients with a diagnosis of MDD (DSM-IV).</p> <p>Results</p> <p>A clinical interpretable four-factor solution consisting of a <it>core depressive </it>factor (I); a <it>cognitive </it>factor (II); an <it>anxiety </it>factor (III) and a <it>somatic </it>factor (IV) was extracted. These factors accounted for 36.9% of the variance on the ZSDS. The 4-factor structure was validated and high coefficients of congruence were obtained (0.98, 0.95, 0.92 and 0.87 for factors I, II, III and IV, respectively). The model seemed to fit the data well with fit indexes within recommended ranges (GFI = 0.9330, AGFI = 0.9112 and RMR = 0.0843).</p> <p>Conclusion</p> <p>Our findings suggest that depressive symptoms in patients with MDD in the PC setting cluster into four dimensions: <it>core depressive, cognitive, anxiety </it>and <it>somatic</it>, by means of a factor analysis of the ZSDS. Further research is needed to identify possible diagnostic, therapeutic or prognostic implications of the different depressive symptomatic profiles.</p

Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy

Background: Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain. Methods: Over 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28–75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either “persisting pain” or “non-persisting pain” groups. Unsupervised machine learning was applied to map the parameters to these diagnoses. Results: A symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with “yes/no” items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%. Conclusions: The present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer