Publications by authors named "L Kirchner"

Repetitive negative thinking (RNT) is a transdiagnostic process associated with the onset, maintenance, and risk of relapse of various mental disorders. However, previous research syntheses addressing the effect of cognitive behavioral therapy (CBT) on RNT are limited to specific diagnoses, treatments, or RNT constructs (transdiagnostic RNT, worry, rumination). In the present meta-analysis, we integrate findings from randomized controlled trials (RCTs) of CBT on RNT across diagnoses, intervention types, and RNT constructs.

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We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7194 contrast-enhanced CT images (3597 with COPD; 3597 healthy controls) from 78 subjects were selected retrospectively (01.2018-12.

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Recently, a high-level daptomycin (DAP)-resistant strain (TS92) was identified, which mediates a 33% decline of DAP when incubated in Mueller-Hinton (MH) medium. The genetic background of the DAP resistance in TS92 is a newly discovered two-gene operon, named whose expression was reported to impair the structural integrity of DAP, eventually leading to its inactivation. Here, we set out to elucidate the chemical nature of -mediated DAP modification by applying a general unknown comparative screening (GUCS) approach in high-resolution mass spectrometry.

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c-di-GMP is an important second messenger in bacteria regulating, for example motility, biofilm formation, cell wall biosynthesis, infectivity, and natural transformability. It binds to a multitude of intracellular receptors. This includes proteins containing general secretory pathway II (GSPII) domains such as the N-terminal domain of the Vibrio cholerae ATPase MshE (MshEN) which binds c-di-GMP with two copies of a 24-amino acids sequence motif.

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A prediction model to assess the risk of hospital readmission can be valuable to identify patients who may benefit from extra care. Developing hospital-specific readmission risk prediction models using local data is not feasible for many institutions. Models developed on data from one hospital may not generalize well to another hospital.

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