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Objective: This study aimed to evaluate the efficacy of adjunct low-frequency pulse electrical stimulation alongside dexamethasone in the treatment of facial nerve paralysis and its subsequent effects on facial nerve function and electromyographic parameters. With the aim of addressing a knowledge gap in the field, this research provides valuable insights into the potential benefits of combining these treatments and their impact on clinical outcomes, facial nerve functionality, and electromyographic dynamics. Methods: A cohort of 66 patients with facial nerve paralysis treated at our institution between April 2018 and November 2021 were randomly assigned to either an observation (n=33) or an experimental group (n=33). The observation group received standard pharmacotherapy, including Western medications and Daqinjiao decoction, along with dexamethasone. The experimental group was administered low-frequency pulse electrical stimulation in addition to the observation group's regimen. Outcomes assessed were clinical efficacy, facial nerve paralysis scoring, facial nerve functional scoring and indices, electromyographic latency, amplitude ratios between affected and unaffected sides, as well as any adverse events. Results: The experimental group demonstrated a significant improvement over the observation group in clinical treatment outcomes, facial nerve paralysis scores, and facial nerve function scores (P < .05 for all). Furthermore, electromyographic analysis revealed shorter latency periods and greater amplitude ratios in the experimental group's facial muscles post-treatment (P < .05). No significant difference was observed in the incidence of adverse reactions between the two groups (P > .05). Conclusion: The integration of low-frequency pulse electrical stimulation with dexamethasone therapy significantly ameliorates the severity of facial nerve paralysis, enhances facial nerve function, and improves electromyographic signals in facial muscles without increasing adverse effects. These findings support the clinical value and safety of this combined treatment approach for facial nerve paralysis, suggesting its suitability for broader clinical application. These results suggest that this combined treatment approach holds promise for broader clinical application, potentially providing a more effective and safer therapeutic option for patients with facial nerve paralysis. Implementing this approach in clinical practice may lead to improved treatment outcomes, better functional recovery, and enhanced quality of life for affected individuals.
RESUMO
With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.