Angiotensin inhibition remains a cornerstone for pharmacologic management of heart failure (HF), despite being associated with decreased hemoglobin (Hb) levels. To investigate the effect of anemia and its treatment on patients with HF treated with sacubitril–valsartan (S/V), we conducted a retrospective study involving patients with recorded left ventricular ejection fractions (LVEFs) of < 40% between January 2017 and December 2019. We identified 677 patients, 37.7% of whom received S/V. The median follow-up period was 868 days. Anemia was associated with significantly decreased survival, increased mortality rates, and higher all-cause hospitalizations in S/V-using patients. We further analyzed 236 patients with HF who had recorded renal function, LVEF, and Hb at the initiation of S/V therapy to identify Hb patterns after S/V therapy. Of these patients, 35.6% exhibited decreasing Hb 12 months after S/V initiation, which was associated with a lower survival rate. Among the patients who were not prescribed anemia medications, Hb of ≥ 12 (vs. < 12 g/dL) was associated with a higher survival rate; this association was absent among the patients undergoing anemia treatment. These results emphasize that consistent screening and treatment for anemia should be implemented to reduce the morbidity and mortality of patients with HF receiving S/V.
Often a face has a voice. Appearance sometimes has a strong relationship with one's voice. In this work, we study how a face can be converted to a voice, which is a face-based voice conversion. Since there is no clean dataset that contains face and speech, voice conversion faces difficult learning and low-quality problems caused by background noise or echo. Too much redundant information for face-to-voice also causes synthesis of a general style of speech. Furthermore, previous work tried to disentangle speech with bottleneck adjustment. However, it is hard to decide on the size of the bottleneck. Therefore, we propose a bottleneck-free strategy for speech disentanglement. To avoid synthesizing the general style of speech, we utilize framewise facial embedding. It applied adversarial learning with a multi-scale discriminator for the model to achieve better quality. In addition, the self-attention module is added to focus on content-related features for in-the-wild data. Quantitative experiments show that our method outperforms previous work.
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