Technische Universität Dresden: Qucosa
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Effectiveness of Digital Health Interventions Containing Game Components for the Self-management of Type 2 Diabetes: Systematic Review
No full textBackground: Games and game components have become a major trend in the realm of digital health research and practice as they are assumed to foster behavior change and thereby improve patient-reported and clinical outcomes for patients with type 2 diabetes. - Objective: The aim of this systematic review was to summarize and evaluate the current evidence on the effectiveness of digital health interventions containing game components on behavioral, patient-reported, and clinical outcomes for patients with type 2 diabetes. - Methods: An electronic search was conducted in MEDLINE and PsycINFO in April 2020; updated in April 2022; and supplemented by additional searches via Google Scholar, Web of Science (which was used for forward citation tracking), and within the references of the included records. Articles were identified using predefined inclusion and exclusion criteria. In total, 2 reviewers independently conducted title, abstract, and full-text screening and then individually performed a critical appraisal of all the included studies using the Cochrane risk-of-bias tool version 2. A consensus was reached through discussion. - Results: Of 2325 potentially relevant titles (duplicates excluded), 10 (0.43%) randomized controlled trials were included in this review. Quality assessment revealed a high risk of bias for all randomized controlled trials except for 10% (1/10), with performance bias due to the lack of blinding being the major source of bias. There is evidence suggesting that digital health interventions containing game components can substantially improve motivation for physical activity (1/1, 100% of the studies dealing with PA motivation), exercise intensity (3/5, 60%), dietary behavior (4/4, 100%), health literacy (1/3, 33%), mental quality of life (2/2, 100%), glycated hemoglobin level (2/6, 33%), BMI (1/3, 33%), fasting plasma glucose level (1/2, 50%), waist circumference (1/1, 100%), and aerobic capacity (1/1, 100%). - Conclusions: Published studies indicated that digital health interventions containing game components might improve health behavior patterns, quality of life, and clinical outcomes in patients with type 2 diabetes. However, the intervention types and outcomes studied were heterogeneous, and study quality was mostly low, which translates to ambiguous results. Future research should focus on sound methodology and reporting as well as on identifying game components that contribute to significant positive effects
AWMF-S2k-Leitlinie „Intravenöse Infusionstherapie bei akut kranken Kindern jenseits der Neugeborenenperiode“
No full textDie i.v.-Infusionstherapie zählt zu den häufigsten Therapien bei hospitalisierten Kindern. Die primären Ziele der Infusionstherapie sind der Erhalt bzw. die Wiederherstellung der Flüssigkeits- und Osmo-Homöostase, Normoglykämie und Vermeidung einer katabolen Stoffwechsellage, wenn die orale oder die Sondenernährung nicht oder nicht ausreichend möglich ist. ...[aus d. Artikel
Analysis of the Differential-Growth Method's potential for Designing Complex Heat-Transferring walls for compact heat exchanger
No full textWater scarcity and resource depletion can be expected during the climate crisis. Therefore, thermally loaded processes in particular, must be made more efficient in the future. Heat exchangers will play a key role in this optimization process. More efficient designs allow a greater heat flow to be removed from processes while mass flows remain constant. In this context, the heat-transferring wall of heat exchangers is a focus of current research on the design of heat exchangers. The aim is to increase the heat-transferring surface of the wall as much as possible and to keep the design space as compact as possible. Therefore, this study investigates the suitability of the differential-growth method for generating complex heat-transferring walls for heat exchangers using CFD-analysis. Firstly, a framework for generating the wall structures and a computational model for predicting the design influence of such structures for the thermal and fluid-dynamic behavior of the heat exchanger are presented. Thereby, the potential of such wall structures is analyzed in this study. Furthermore, the study identified weaknesses of such walls designed with the differential-growth method, which should be the focus of future investigations
(Re)shaping Literary Canon in the Soviet Indigenous North
No full textIn this article I demonstrate how in the post-Thaw period - the period of “soft” socialist realism - the northern indigenous minorities began to (re)invent literary writing and manifest their own version of the canon. Due to the lack of a pre-Soviet written literary tradition, “young” literatures were born as a symbiosis of folklore, beliefs, indigenous-Christian customs and the surrogate literary tradition of the Russian-European center: the Soviet “master plot.” Having graduated from universities in Moscow or Leningrad, the first generations of writers “(re)invented” a view of themselves as simultaneously native and Other. A consequence of the fact that the authors internalized the role of the youngest “brother” was, among others, the amalgamation of children's and adults' narrative and pedagogical zeal, which combined folklore ethics with socialist realist moralism. The study is of a transitional time: before the local authors had experienced a cardinal reevaluation of their values during perestroika and afterwards
Tensorial time derivatives on moving surfaces: General concepts and a specific application for surface Landau-de Gennes models
No full textObserver-invariance is regarded as a minimum requirement for an appropriate definition of time derivatives. We systematically discuss such time derivatives for surface tensor field and provide explicit formulations for material, upper-convected, lower-convected and Jaumann/corotational time derivatives which all lead to different physical implications. We compare these results with the corresponding time derivatives for tangential tensor fields. As specific surface 2-tensor fields we consider surface Q-tensor fields and conforming surface Q-tensor fields and apply the results in surface Landau-de Gennes models for surface liquid crystals
Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma
No full textPrimary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA
Enantioselective synthesis of the bioactive flavanones lonchocarpol A and 6-(1,1-dimethylallyl)naringenin by catalytic asymmetric transfer hydrogenation
No full textPractical enantioselective syntheses of the natural products lonchocarpol A and 6-(1,1-dimethylallyl)naringenin as well as their optical antipodes are reported. A rhodium-catalyzed asymmetric transfer hydrogenation of readily available racemic substrates with virtually complete kinetic resolution served as the key step. Solvent-controlled, clay-catalyzed [1,3] or [3,3] shifts of prenyl aryl ethers were used to install the C-6 prenyl or 1,1-dimethylallyl substituent of the target flavanones
Investigation of the Damage Phenomenology with Dependence on the Macroporosity and Microporosity of Porous Freeze Foams
No full textFreeze Foams are cellular, ceramic structures with hierarchical pore structures that are manufactured using the direct foaming process. By tailoring their morphology and strength, these foam structures are able to cover a wide range of application. Earlier works identified that pore-forming influencing factors (water and air content, suspension temperature, as well as pressure reduction rate) dictate the constitution on a macroscopic and microscopic scale. Therefore, the ability to manufacture foams whose properties align with the component requirements would be an important step in advancing towards a widespread application of these promising materials. With this goal in mind, the correlation between the pore-forming influencing factors and the resulting mechanical properties was quantified. Foams with independently adjustable porosities were produced at the micro and macro scales and evaluated according to their material failure behavior under compressive loads. As a result, foams with determined macroporosities between 38 and 62%, microporosities between 25 and 42%, and compression strengths between 1 and 7 MPa with different material failure characteristics were manufactured and systematically investigated
Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning
No full textAims:
One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system.
Methods and results:
We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns.
Conclusion:
We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts
Second‑Order Confdence in Supervaluationism
No full textRecently, Wilcox (JGPS 51: 65–87, 2020) argued against the so-called wide interval view and in favor of the principle of indifference as the correct response to unspecific evidence. Embedded in a formal model of the beliefs of an agent, the former presupposes imprecise probabilities and the latter numerically precise degrees of belief. His argument is illustrated by a thought experiment that comes with a fundamental intuition. According to Wilcox, the wide interval view is incompatible with this intuition and, thus, undermined. In contrast, I show that the intuition behind the thought experiment is, in fact, compatible with the wide interval view if it is embedded into a specific conception of imprecise probabilities as model of belief. This conception is an extension of a framework which I call modified supervaluationism (MSV) and which I recently presented elsewhere (Karge 2021, 175–191). To accommodate the thought experiment’s fundamental intuition, it introduces a notion of second-order beliefs