Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations.

Evaluation of the specificity of the 2023 European Society of Cardiology classification for infective endocarditis.

Background: The 2023 Duke-ISCVID and 2023 ESC classifications have recently issued independent diagnostic criteria for infective endocarditis (IE), updating the 2015 ESC criteria.

Objectives: The specificity of the 2023 ESC criteria should be evaluated and compared to the two other classifications in IE suspected patients.

Methods: We retrospectively collected the characteristics of patients hospitalised in Bichat University Hospital, in 2021, who had been evaluated for suspicion of IE, and in whom IE diagnosis was finally rejected.

Patient comfort with sexual orientation and gender identity questions in adult congenital cardiology clinics.

Background: Lack of sexual orientation and gender identity (SOGI) data creates barriers for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people in health care. Barriers to SOGI data collection include physician misperception that patients do not want to answer these questions and discomfort asking SOGI questions. This study aimed to assess patient comfort towards SOGI questions across five quaternary care adult congenital heart disease (ACHD) centres.

A high-throughput microfabricated platform for rapid quantification of metastatic potential.

Assays that measure morphology, proliferation, motility, deformability, and migration are used to study the invasiveness of cancer cells. However, native invasive potential of cells may be hidden from these contextual metrics because they depend on culture conditions. We created a micropatterned chip that mimics the native environmental conditions, quantifies the invasive potential of tumor cells, and improves our understanding of the malignancy signatures.