Publications by authors named "J Heine"

Background: The new patient cataract and lens ophthalmic questionnaire (CLOQ) determines subjective patient satisfaction before and after cataract surgery according to item response theory (IRT).

Methods: A total of 317 patients with bilateral cataract surgery and implantation of different intraocular lens (IOL) types were included. The questionnaire (37 questions) is divided into questions on visual acuity-relevant quality of life (VRQoL), quality of vision (QoV), spectacle dependence, and general satisfaction.

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An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD).

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Background And Objectives: The characteristics of persistent long-term symptoms and their contribution to subjective quality of life remain unclear in patients with NMDAR encephalitis. In this study, we aimed to evaluate postacute neuropsychiatric symptoms, subjective cognitive complaints, and disease coping mechanisms and identify predictors of health-related quality of life (HRQoL) after N-methyl-D-aspartate receptor (NMDAR) encephalitis.

Methods: This cross-sectional observational study investigated patients with NMDAR encephalitis in the postacute phase.

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Background: Endometriosis is a chronic, estrogen-dependent, benign condition, affecting 10-15% of women of reproductive age. It is associated with a prevalence of sexual dysfunction that is nearly twice as high as that seen in women with other benign gynecological conditions. Our study aimed to assess the effect of surgical intervention on sexual function, as measured by the FSFI (Female Sexual Function Index) score, in women with endometriosis compared to those with other benign gynecological conditions, both before and after surgery.

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Article Synopsis
  • The integration of AI into microscopy greatly improves image acquisition and analysis, particularly in super-resolution microscopy.
  • The development of AI-assisted microscopy is hampered by the lack of extensive biological datasets and challenges in standardizing methods across varied samples.
  • The pySTED platform provides a realistic simulation environment that aids in training AI models and optimizing microscopy strategies, demonstrating successful application on actual systems without needing extensive adjustments.
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