[An autopsy of a case of pulmonary hypertension secondary to pulmonary embolization of intrahepatic cholangiocarcinoma].

A 70-year-old woman was admitted to our hospital for one month because of progressive dyspnea. Her medical history included an operation for hepatolithiasis at age 47. She was a current smoker. Chest CT revealed emphysematous change and honeycombing in the lung and bilateral subpleural opacifications. Cardiac ultrasound examination showed pulmonary hypertension. Treatments with antibiotics, corticosteroids and heparin were unsuccessful. Despite mechanical ventilation, she died of respiratory failure. Autopsy revealed that intrahepatic cholangiocarcinoma had spread via the hematogeneous route, formed multiple emboli into the pulmonary small arteries, and led to severe pulmonary hypertension and lung infarction.

A Comprehensive and Versatile Multimodal Deep-Learning Approach for Predicting Diverse Properties of Advanced Materials.

A multimodal deep-learning (MDL) framework is presented for predicting physical properties of a ten-dimensional acrylic polymer composite material by merging physical attributes and chemical data. The MDL model comprises four modules, including three generative deep-learning models for material structure characterization and a fourth model for property prediction. The approach handles an 18-dimensional complexity, with ten compositional inputs and eight property outputs, successfully predicting 913 680 property data points across 114 210 composition conditions. This level of complexity is unprecedented in computational materials science, particularly for materials with undefined structures. A framework is proposed to analyze the high-dimensional information space for inverse material design, demonstrating flexibility and adaptability to various materials and scales, provided sufficient data are available. This study advances future research on different materials and the development of more sophisticated models, drawing the authors closer to the ultimate goal of predicting all properties of all materials.

Dendronized macromonomers for three-dimensional data storage.

A series of dendritic macromonomers have been synthesized and utilized as the photoactive component in holographic storage systems leading to high performance, low shrinkage materials.