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.

OBJECTIVES: This study investigated the improvement of food habits and eating behavior of cancer patients during treatment through the continuous nutrition counseling and monitoring. METHODS: Thirty cancer patients during treatment were participated in this study, and the first nutrition counseling and the first and second monitoring were conducted after 2-3 week intervals. RESULTS: As a result of the nutrition counseling and monitoring, all patients improved to a great extent to consume 3 meals a day. The frequency of having breakfast was significantly increased with the nutrition counseling and continuous monitoring as well. The meal fixed quantity was stabilized and the eating speed slowed down as more nutrition counseling were done. The snack intakes of patients did not show any significant difference after the nutrition counseling but showed a slight decline after the monitoring. The frequencies of eating out and a late-night meal significantly decreased after the monitoring. The intakes of fish, meat, vegetables, milk and fruits needed for a well-balanced diet significantly increased as more nutrition counseling were done. The intakes of processed food were significantly decreased after the nutrition counseling and the intakes of fast food were significantly decreased as more nutrition counseling were done. The water intake of patients also significantly increased. In the eating behavior related to health, the frequency of drinking alcohol significantly decreased after nutrition counseling consultation and no patients had dietary supplements after the first monitoring. CONCLUSIONS: These results suggested that continuous nutrition counseling is effective in improving eating habits of cancer patients.
Breakfast ; Counseling* ; Diet ; Dietary Supplements ; Drinking ; Eating ; Fast Foods ; Feeding Behavior ; Food Habits ; Fruit ; Humans ; Meals ; Meat ; Milk ; Snacks ; Vegetables

Experimental autoimmune encephalomyelitis (EAE), an animal model of human multiple sclerosis (MS), reflects pathophysiologic steps in MS such as the influence of T cells and antibodies reactive to the myelin sheath, and the cytotoxic effect of cytokines. Galectin-9 (Gal-9) is a member of animal lectins that plays an essential role in various biological functions. The expression of Gal-9 is significantly enhanced in MS lesions; however, its role in autoimmune disease has not been fully elucidated. To identify the role of Gal-9 in EAE, we measured changes in mRNA and protein expression of Gal-9 as EAE progressed. Expression increased with disease progression, with a sharp rise occurring at its peak. Gal-9 immunoreactivity was mainly expressed in astrocytes and microglia of the central nervous system (CNS) and macrophages of spleen. Flow cytometric analysis revealed that Gal-9+CD11b+ cells were dramatically increased in the spleen at the peak of disease. Increased expression of tumor necrosis factor (TNF)-R1 and p-Jun N-terminal kinase (JNK) was observed in the CNS of EAE mice, suggesting that TNF-R1 and p-JNK might be key regulators contributing to the expression of Gal-9 during EAE. These results suggest that identification of the relationship between Gal-9 and EAE progression is critical for better understanding Gal-9 biology in autoimmune disease.
Animals ; Antibodies ; Astrocytes ; Autoimmune Diseases ; Biology ; Central Nervous System ; Cytokines ; Disease Progression ; Encephalomyelitis, Autoimmune, Experimental* ; Humans ; Lectins ; Macrophages ; Mice ; Microglia ; Models, Animal ; Multiple Sclerosis ; Myelin Sheath ; Phosphotransferases ; RNA, Messenger ; Spleen ; T-Lymphocytes ; Tumor Necrosis Factor-alpha