Alterations of sympathetic dynamics after atrial fibrillation ablation by analysis sympathetic nerve activity provide prognostic value for recurrence and mechanistic insights into ablation.

Background: Pulmonary vein isolation (PVI) is the cornerstone of atrial fibrillation (AF) ablation. Success is associated with autonomic function modulation; however, the relationship between the changes after ablation is not fully understood. We aimed to investigate the effect of ablation on autonomic modulation by skin sympathetic nerve activity (SKNA) using conventional electrocardiogram (ECG) electrodes and to predict the treatment success. Methods: We enrolled 79 patients. We recorded neuECG for 10 min at 10 kHz before and after ablation. The NeuECG was bandpass-filtered (500-1,000 Hz) and integrated at intervals of 100 ms (iSKNA). iSKNA was averaged over different time windows (1-, 5-,10-s; aSKNAs), and burst analyses were derived from aSKNAs to quantify the dynamics of sympathetic activities. AF recurrence after 3 months was defined as the study endpoint. Results: Sixteen patients experienced AF recurrence after the ablation. For burst analysis of 1-s aSKNA, the recurrence group had a higher bursting frequency than the non-recurrence group (0.074 ± 0.055 vs. 0.109 ± 0.067; p < 0.05) before ablation. The differences between pre- and post-ablation of firing duration longer than 2 s were more in the non-recurrence group (2.75 ± 6.41 vs. -1.41 ± 5.14; p < 0.05), while no significant changes were observed in the percentage of duration longer than 10 s using 5-s aSKNA. In addition, decreases in differences in firing frequency and percentage of both overall firing duration and longer firing duration (> 2 s) between pre- and post-ablation were independently associated with AF recurrence and more area under receiver operating characteristics (ROC) curve in combination with CHADS2 score (0.833). Conclusion: We demonstrated the applicability of neuECG for determining sympathetic modulation during AF ablation. Decreasing sympathetic activity is the key to successful ablation.

Serum vaspin levels are positively associated with diabetic retinopathy in patients with type 2 diabetes mellitus.

AIMS/INTRODUCTION: Vaspin is linked to obesity and its metabolic abnormalities. However, the role of vaspin serum levels in diabetic retinopathy (DR) is unknown. In the present study, we investigated the association between serum levels of vaspin and both DR and vision-threatening DR. MATERIALS AND METHODS: This was a cross-sectional single-center observational study from December 2018 to September 2019. We evaluated circulating serum levels of vaspin in 372 participants with type 2 diabetes. DR was screened through detailed ocular examination. DR patients were also divided two groups: vision-threatening DR and non-vision-threatening DR. The relationship between vaspin and DR was investigated by univariate and multivariate logistic regression analyses, and the results are shown as odds ratios with 95% confidence intervals. RESULTS: The vaspin serum levels of 372 patients were obtained, with a median value of 1.50 ng/mL (interquartile range 0.94-2.18 ng/mL). The median age of those patients was 53 years (interquartile range 44-62 years), and 44.4% were women. Patients with DR and VDTR had significantly increased vaspin serum levels (P < 0.001 andP < 0.001). A multivariable regression model found that patients with high levels of vaspin were approximately 1.85-fold (odds ratio for per unit increase 1.85, 95% confidence interval 1.43-2.55; P < 0.001) more likely to experience DR, and 3.76-fold (odds ratio for per unit increase 3.76, 95% confidence interval 2.05-6.55; P < 0.001) more likely to experience VTDR. The predictive value of vaspin was stronger in women than in men. CONCLUSION: Higher vaspin serum levels were associated with an increased risk of DR and VDTR in patients with type 2 diabetes, which showed that vaspin is an important indicator factor for DR.

An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network.

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user's head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user's head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.

Depth-Sensor-Based Monitoring of Therapeutic Exercises.

In this paper, we propose a self-organizing feature map-based (SOM) monitoring system which is able to evaluate whether the physiotherapeutic exercise performed by a patient matches the corresponding assigned exercise. It allows patients to be able to perform their physiotherapeutic exercises on their own, but their progress during exercises can be monitored. The performance of the proposed the SOM-based monitoring system is tested on a database consisting of 12 different types of physiotherapeutic exercises. An average 98.8% correct rate was achieved.