Retrospective analysis of the occurrence, potential risk factors and medical significance of pulmonary complications after total shoulder arthroplasty from the National Inpatient Sample database (2010-2019).

Background: In USA, total shoulder arthroplasty (TSA) ranks amongst the top five surgeries that require hospitalization. As a result, the healthcare system in USA could face a considerable financial strain due to the emergence of subsequent pulmonary problems. This study aimed to conduct a thorough examination of the prevalence, influential factors and medical importance of pulmonary complications, with emphasis on pneumonia, respiratory failure and pulmonary embolism (PE) following total shoulder arthroplasty (TSA) procedures in USA.

Association of quantitative measurements of postmortem tau tangle and thread pathology with antemortem cortical thickness measurements in neuropathologically confirmed Alzheimer’s Disease continuum patients.

Background: Tau pathology and neurodegeneration in the medial temporal lobe (MTL) are highly associated in Alzheimer’s Disease (AD). However, the spatial pattern of neurodegeneration, contribution of individual tau inclusion types, and influence of MTL co‐pathologies (i.e.

Spatial Pattern of Medial Temporal Lobe Cross‐Sectional and Longitudinal Structural Change in Cognitively Normal Individuals.

Background: The medial temporal lobe's (MTL) early involvement in tau pathology makes it a key focus in the development of preclinical Alzheimer’s disease (AD) biomarkers. ROI analyses in prior studies reported significant MTL structural differences in cognitively normal individuals with and without β‐amyloid (A+/‐CN). Pointwise analysis, offering spatial information of early neurodegeneration, has potential to pinpoint “signature regions” of pathological change, but has been underexplored in the MTL.

Robust multi‐modality segmentation of medial temporal lobe subregions using both 3‐tesla and 7‐tesla magnetic resonance imaging.

Background: Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer’s disease. However, dropout artifacts are present in some modalities, leading to poor image quality and unreliable segmentation of MTL subregions. Considering that MRI modalities with different field strength offer distinct advantages in imaging different parts of the MTL, we developed a muti‐modality segmentation model using both 7‐tesla (7T) and 3‐tesla (3T) structural MRI to obtain robust segmentation in poor‐quality images.

Regional Deep Atrophy: Using Temporal Information to Automatically Identify Regions Associated with Alzheimer’s Disease Progression from Longitudinal MRI.

Background: Assessment of longitudinal hippocampal atrophy is a well‐studied biomarker for Alzheimer’s disease (AD). However, most state‐of‐the‐art measurements calculate changes directly from MRI images using image registration/segmentation, which may misreport head motion or MRI artifacts as neurodegeneration. We present a deep learning method Regional Deep Atrophy (RDA) that (1) estimates atrophy sensitive to progression by quantifying time‐associated changes in images, especially in preclinical AD stage (as in DeepAtrophy (Dong et al.