Noncanonical atherosclerosis as the driving force in tricuspid aortic valve associated aneurysms - A trace collection

Pathogenic mechanisms in degenerative thoracic aortic aneurysms (TAA) are still unclear. There is an ongoing debate about whether TAAs are caused by uniform or distinct processes, which would obviously have a major impact on future treatment strategies. Clearly, the ultimate outcome of TAA subgroups associated with a tricuspid aortic valve (TAV) or a bicuspid aortic valve (BAV) is the same, namely a TAA. Based on results from our own and others' studies, we decided to compare the different TAAs (TAV and BAV) and controls using a broad array of analyses, i.e., metabolomic analyses, gene expression profiling, protein expression analyses, histological characterization, and matrix-assisted laser desorption ionization imaging. Central findings of the present study are the presence of noncanonical atherosclerosis, pathological accumulation of macrophages, and disturbances of lipid metabolism in the aortic media. Moreover, we have also found that lipid metabolism is impaired systemically. Importantly, all of the above-described phenotypes are characteristic for TAV-TAA only, and not for BAV-TAA. In summary, our results suggest different modes of pathogenesis in TAV- and BAV-associated aneurysms. Intimal atherosclerotic changes play a more central role in TAV-TAA formation than previously thought, particularly as the observed alterations do not follow classical patterns. Atherosclerotic alterations are not limited to the intima but also affect and alter the TAV-TAA media. Further studies are needed to i) clarify patho-relevant intima-media interconnections, ii) define the origin of the systemic alteration of lipid metabolism, and iii) to define valid biomarkers for early diagnosis, disease progression, and successful treatments in TAV-TAAs

An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization

Abstract Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potential, clinical embryologists routinely work with static images (morphological assessment) or short video sequences (time-lapse annotation). Recently, Artificial Intelligence models were utilized to support the embryo selection procedure. Even though they have proven their great potential in different in vitro fertilization settings, there is still considerable room for improvement. To support the advancement of algorithms in this research field, we built a dataset consisting of static blastocyst images and additional annotations. As such, Gardner criteria annotations, depicting a morphological blastocyst rating scheme, and collected clinical parameters are provided. The presented dataset is intended to be used to train deep learning models on static morphological images to predict Gardner’s criteria and clinical outcomes such as live birth. A benchmark of human expert’s performance in annotating Gardner criteria is provided