Background: Transthoracic echocardiography (TTE) requires time-intensive integration of quantitative measure‑ ments and qualitative visual assessment. Fully automated artificial intelligence (AI)-based analysis may reduce total analysis time while preserving accuracy, but systematic real-world validation remains limited. Methods: This prospective, single-center pilot study enrolled 40 TTE examinations. Identical deidentified DICOM datasets were independently provided to a trained cardiac sonographer and a fully automated AI system comprising quantitative and qualitative visual interpretation modules. All outputs were compared with a cardiologist-adjudicated reference standard. Primary endpoints were total analysis time and noninferiority of AI-derived left ventricular ejection fraction (LVEF) versus the reference standard, with a prespecified margin of 3 percentage points (one-sided α = 0.025). Results: Median analysis time was 94 s (interquartile range [IQR], 82–106 s) for the AI workflow versus 490 s (IQR, 438–626 s) for the human workflow (P < 0.001). AI-derived LVEF met the noninferiority criterion (mean difference, 0.00 percentage points; upper one-sided 95% confidence bound, 1.41 percentage points; P < 0.001), with an intraclass correlation coefficient (ICC) of 0.902 (95% confidence interval, 0.822–0.947). ICCs for secondary quantitative indi‑ ces ranged from 0.625 to 0.989. For aortic regurgitation severity grading, AI’s overall accuracy was 75.0% (quadratic weighted κ = 0.762), compared with 82.5% for human interpretation (κ = 0.812, McNemar P = 0.579). Conclusions Fully automated AI-assisted TTE analysis substantially reduced total analysis time while maintaining noninferior LVEF accuracy and acceptable performance across secondary quantitative and qualitative indices. These findings support the use of AI as a practical workflow accelerator in routine echocardiography.

Background:Osteosarcoma is the most common bone malignancy in children and adolescents, and 20%–30% of the patients suffer from poor prognosis because of individual chemoresistance. The Hippo/yes?associated protein (YAP) signaling pathway has been shown to play a role in tumor chemoresistance, but no previous report has focused on its involvement in osteosarcoma chemoresistance. This study aimed to investigate the role of the Hippo/YAP sign?aling pathway in osteosarcoma chemoresistance and to determine potential treatment targets.
Methods:Using the Cell Titer?Glo Luminescent cell viability assay and lfow cytometry analysis, we determined the proliferation and chemosensitivity of YAP?overexpressing and YAP?knockdown osteosarcoma cells. In addition, using western blotting and the real?time polymerase chain reaction technique, we investigated the alteration of the Hippo/YAP signaling pathway in osteosarcoma cells treated with chemotherapeutic agents.
Results:Mammalian sterile 20?like kinase 1 (MST1) degradation was increased, and large tumor suppressor kinase 1/2 (LATS1/2) total protein levels were decreased by methotrexate and doxorubicin, which increased activation and nuclear translocation of YAP. Moreover, YAP increased the proliferation and chemoresistance of MG63 cells.
Conclusions:The Hippo/YAP signaling pathway plays a role in osteosarcoma chemoresistance, and YAP is a potential target for reducing chemoresistance.