RESUMEN
Background: Insufficient clinicians' auscultation ability delays the diagnosis and treatment of valvular heart disease (VHD); artificial intelligence provides a solution to compensate for the insufficiency in auscultation ability by distinguishing between heart murmurs and normal heart sounds. However, whether artificial intelligence can automatically diagnose VHD remains unknown. Our objective was to use deep learning to process and compare raw heart sound data to identify patients with VHD requiring intervention. Methods: Heart sounds from patients with VHD and healthy controls were collected using an electronic stethoscope. Echocardiographic findings were used as the gold standard for this study. According to the chronological order of enrollment, the early-enrolled samples were used to train the deep learning model, and the late-enrollment samples were used to validate the results. Results: The final study population comprised 499 patients (354 in the algorithm training group and 145 in the result validation group). The sensitivity, specificity, and accuracy of the deep-learning model for identifying various VHDs ranged from 71.4 to 100.0%, 83.5-100.0%, and 84.1-100.0%, respectively; the best diagnostic performance was observed for mitral stenosis, with a sensitivity of 100.0% (31.0-100.0%), a specificity of 100% (96.7-100.0%), and an accuracy of 100% (97.5-100.0%). Conclusions: Based on raw heart sound data, the deep learning model effectively identifies patients with various types of VHD who require intervention and assists in the screening, diagnosis, and follow-up of VHD.
RESUMEN
With the increased construction of dam reservoirs and the demand for water security, terrestrial dissolved organic matter (DOM) has received attention because of its role in regulating water quality, ecological functions, and the fate and transport of pollutants in dam reservoirs. This study investigated the transformations of soil DOM and vegetation DOM of dam reservoirs following photodegradation and biodegradation before conservative mixing, as well as the resultant effects on phenanthrene binding. Based on the results, terrestrial DOM could undergo transformation via photodegradation and biodegradation before conservative mixing in dam reservoirs. Although both processes resulted in substantial decreases in DOM concentrations, the changes in chromophoric DOM and fluorescent DOM depended on the original DOM sources. Furthermore, the photodegradation of terrestrial DOM resulted in more pronounced photobleaching than photomineralization. In addition, photodegradation of terrestrial DOM resulted in the generation of DOM-derived by-products with low molecular weight and low aromaticity, whereas the biodegradation of terrestrial DOM resulted in DOM-derived by-products with low molecular weight and high aromaticity. Subsequently, the photodegradation and biodegradation of terrestrial DOM substantially enhanced the binding affinity of phenanthrene. Soil DOM is prior to vegetation DOM when predicting the ecological risk of HOCs. These results indicate that the terrestrial DOM in dam reservoirs should be reconsidered before conservative mixing. Further studies on the coupling effects of both biogeochemical processes, as well as on the relative contributions of soil DOM and vegetation DOM after transformation to the aquatic DOM in dam reservoirs, are required. This study provides information on the environmental effects of dam construction from the perspective of biogeochemical processes.
