Long-COVID improves in 50% of patients after a year in a Midwestern cohort.

Background: Many of those infected with COVID-19 experience long-term disability due to persistent symptoms known as Long-COVID, which include ongoing respiratory issues, loss of taste and smell, and impaired daily functioning. Research Question: This study aims to better understand the chronology of long-COVID symptoms. Study Design and Methods: We prospectively enrolled 403 adults from the University of Iowa long-COVID clinic (June 2020 to February 2022). Participants provided symptom data during acute illness, symptom progression, and other clinical characteristics. Patients in this registry received a survey containing questions including current symptoms and status since long-COVID diagnosis (sliding status scale, PHQ2, GAD2, MMRC). Those >12 months since acute-COVID diagnosis had chart review done to track their symptomology. Results: Of 403 participants contacted, 129 (32%) responded. The mean age (in years) was 50.17 +/-14.28, with 31.8% male and 68.2% female. Severity of acute covid treatment was stratified by treatment in the outpatient (70.5%), inpatient (16.3%), or ICU (13.2%) settings. 51.2% reported subjective improvement (sliding scale scores of 67-100) since long-COVID onset. Ages 18-29 reported significantly higher subjective status scores. Subjective status scores were unaffected by severity. 102 respondents were >12 months from their initial COVID-19 diagnosis and were tracked for longitudinal symptom persistence. All symptoms tracked had variance (mean fraction 0.58, range 0.34-0.75) in the reported symptoms at the time of long-COVID presentation when compared with patient survey report. 48 reported persistent dyspnea, 23 (48%) had resolved it at time of survey. For fatigue, 44 had persistence, 12 (27%) resolved. Interpretation: Overall, 51.2% respondents improved since their long-COVID began. Pulmonary symptoms were more persistent than neuromuscular symptoms (anosmia, dysgeusia, myalgias). Gender, time since acute COVID infection, and its severity didn't affect subjective status or symptoms. This study highlights recall bias that may be prevalent in other long-COVID research reliant on participant memory.

Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm's ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.

Ca2+-Binding Protein 1 Regulates Hippocampal-dependent Memory and Synaptic Plasticity.

Ca2+-binding protein 1 (CaBP1) is a Ca2+-sensing protein similar to calmodulin that potently regulates voltage-gated Ca2+ channels. Unlike calmodulin, however, CaBP1 is mainly expressed in neuronal cell-types and enriched in the hippocampus, where its function is unknown. Here, we investigated the role of CaBP1 in hippocampal-dependent behaviors using mice lacking expression of CaBP1 (C-KO). By western blot, the largest CaBP1 splice variant, caldendrin, was detected in hippocampal lysates from wild-type (WT) but not C-KO mice. Compared to WT mice, C-KO mice exhibited mild deficits in spatial learning and memory in both the Barnes maze and in Morris water maze reversal learning. In contextual but not cued fear-conditioning assays, C-KO mice showed greater freezing responses than WT mice. In addition, the number of adult-born neurons in the hippocampus of C-KO mice was ∼40% of that in WT mice, as measured by bromodeoxyuridine labeling. Moreover, hippocampal long-term potentiation was significantly reduced in C-KO mice. We conclude that CaBP1 is required for cellular mechanisms underlying optimal encoding of hippocampal-dependent spatial and fear-related memories.