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- 저자 : Alexander Roth (검색결과 2 건)
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조규성,Anja König,Stephanie Seifert,Alexander Hanak,Alexander Roth,Melanie Huch,Achim Bub,Bernhard Watzl,Charles M.A.P. Franz 한국식품과학회 2015 Food Science and Biotechnology Vol.24 No.6
The effect of cloudy apple juice on fecal microbiota of type 2 diabetics was studied. Five volunteers consumed apple juice while 5 control volunteers received an isocaloric control beverage daily for 4 weeks. DGGE profile analysis showed high diversity between volunteers that did not change over the intervention period using primers for Firmicutes, Bacteroidetes, bifidobacteria, enterococci, and enterobacteria. An exception was observed using lactobacilli primers, perhaps as the result of the dietary influence. Consumption of apple juice was not correlated with changes in DGGE profiles. Quantitative PCR was used to investigate the effect of apple juice on bacterial counts in different subgroups. Apple juice did not lead to significantly (p>0.05) different numbers of total bacteria, enterobacteria, bifidobacteria, lactobacilli, or Bacteroidetes, but caused a significant (p<0.05) decrease in numbers of enterococci, and a smaller but also significant decrease in numbers of Firmicutes, when comparing before and after intervention with apple juice.
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Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning
Ludovic Amruthalingam,Oliver Buerzle,Philippe Gottfrois,Alvaro Gonzalez Jimenez,Anastasia Roth,Thomas Koller,Marc Pouly,Alexander A. Navarini 대한의료정보학회 2022 Healthcare Informatics Research Vol.28 No.3
Objectives: Pustular psoriasis (PP) is one of the most severe and chronic skin conditions. Its treatment is difficult, and measurementsof its severity are highly dependent on clinicians’ experience. Pustules and brown spots are the main efflorescencesof the disease and directly correlate with its activity. We propose an automated deep learning model (DLM) to quantifylesions in terms of count and surface percentage from patient photographs. Methods: In this retrospective study, two dermatologistsand a student labeled 151 photographs of PP patients for pustules and brown spots. The DLM was trained andvalidated with 121 photographs, keeping 30 photographs as a test set to assess the DLM performance on unseen data. We alsoevaluated our DLM on 213 unstandardized, out-of-distribution photographs of various pustular disorders (referred to as thepustular set), which were ranked from 0 (no disease) to 4 (very severe) by one dermatologist for disease severity. The agreementbetween the DLM predictions and experts’ labels was evaluated with the intraclass correlation coefficient (ICC) for thetest set and Spearman correlation (SC) coefficient for the pustular set. Results: On the test set, the DLM achieved an ICC of0.97 (95% confidence interval [CI], 0.97–0.98) for count and 0.93 (95% CI, 0.92–0.94) for surface percentage. On the pustularset, the DLM reached a SC coefficient of 0.66 (95% CI, 0.60–0.74) for count and 0.80 (95% CI, 0.75–0.83) for surface percentage. Conclusions: The proposed method quantifies efflorescences from PP photographs reliably and automatically, enabling aprecise and objective evaluation of disease activity.
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