Deep-Learning Generated Synthetic Material Decomposition Images Based on Single-Energy CT to Differentiate Intracranial Hemorrhage and Contrast Staining Within 24 Hours After Endovascular Thrombectomy.

Aims: To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-energy CT (SECT) for real-time detection of intracranial hemorrhage (ICH) after endovascular thrombectomy.

Materials: We retrospectively collected data from two hospitals, consisting of 237 dual-energy CT (DECT) scans, including matched iodine overlay maps, virtual noncontrast, and simulated SECT images. These scans were randomly divided into a training set (n = 190) and an internal validation set (n = 47) in a 4:1 ratio based on the proportion of ICH.

Predicting TNFRSF4 expression and prognosis in head and neck squamous cell carcinoma tissue: a pathological image analysis approach.

Head and neck squamous cell carcinoma (HNSCC) exhibits a poor 5-year survival rate. TNFRSF4 is gaining attention in tumor therapy. The objective of this study was to forecast the expression of TNFRSF4 in HNSCC tissue using analysis of pathological images and investigate its possible molecular mechanisms.

Unphysiological lung strain promotes ventilation-induced lung injury via activation of the PECAM-1/Src/STAT3 signaling pathway.

Introduction: In patients with acute respiratory distress syndrome, mechanical ventilation often leads to ventilation-induced lung injury (VILI), which is attributed to unphysiological lung strain (UPLS) in respiratory dynamics. Platelet endothelial cell adhesion molecule-1 (PECAM-1), a transmembrane receptor, senses mechanical signals. The Src/STAT3 pathway plays a crucial role in the mechanotransduction network, concurrently triggering pyroptosis related inflammatory responses.