Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference.

3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods able to perform fast and accurate cell counting and localization. Recent advances in deep learning have enabled the development of various tools for cell segmentation.

Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation.

Background: The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been used to predict outcomes in the AF population, but not in a population entirely on anticoagulant therapy.

Methods And Aims: Different supervised ML models were applied to predict all-cause death, cardiovascular (CV) death, major bleeding and stroke in anticoagulated patients with AF, processing data from the multicenter START-2 Register.

Universal autofocus for quantitative volumetric microscopy of whole mouse brains.

Unbiased quantitative analysis of macroscopic biological samples demands fast imaging systems capable of maintaining high resolution across large volumes. Here we introduce RAPID (rapid autofocusing via pupil-split image phase detection), a real-time autofocus method applicable in every widefield-based microscope. RAPID-enabled light-sheet microscopy reliably reconstructs intact, cleared mouse brains with subcellular resolution, and allowed us to characterize the three-dimensional (3D) spatial clustering of somatostatin-positive neurons in the whole encephalon, including densely labeled areas.

Impact of COVID-19 pandemic on the incidence of otitis media with effusion in adults and children: a multicenter study.

Purpose: To compare and analyze the incidence of otitis media with effusion (OME), before and during the COVID-19-related pandemic period, to evaluate the effects of the social changes (lockdown, continuous use of facial masks, social distancing, reduction of social activities) in the OME incidence in children and adults.

Methods: The number of diagnosed OME in e five referral centers, between 1 March 2018 and 1 March 2021, has been reviewed and collected. To estimate the reduction of OME incidence in children and adults during the COVID-19 pandemic period the OME incidence in three period of time were evaluated and compared: group 1-patients with OME diagnosis achieved between 1/03/2018 and 01/03/2019 (not pandemic period).