3 resultados para LARYNGEAL NERVES


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BACKGROUND Tapia's syndrome is an uncommon disease described in 1904 by Antonio Garcia Tapia, a Spanish otolaryngologist. It is characterized by concomitant paralysis of the hypoglossal (XIIth) and pneumogastric (Xth) nerves. Only 69 cases have been described in the literature. Typically, the reported patients presented with a history of orotracheal intubation. Common symptoms are dysphonia, tongue deviation toward the affected side, lingual motility disturbance, and swallowing difficulty. CASE PRESENTATION In the report, we describe three cases of Tapia's syndrome in three Caucasian patients who underwent surgery with general anesthesia. Two of these patients underwent neck abscess drainage, and the third had an open reduction of a shoulder fracture. The clinical symptoms of Tapia's syndrome appeared after extubation. All three of our patients recovered their lost function at 3 months after diagnosis. CONCLUSIONS We underline the importance of performing airway endoscopy and a specific program of swallowing rehabilitation for the proper management of Tapia's syndrome.

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This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

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Vestibular migraine (VM) is a common disorder in which genetic, epigenetic, and environmental factors probably contribute to its development. The pathophysiology of VM is unknown; nevertheless in the last few years, several studies are contributing to understand the neurophysiological pathways involved in VM. The current hypotheses are mostly based on the knowledge of migraine itself. The evidence of trigeminal innervation of the labyrinth vessels and the localization of vasoactive neuropeptides in the perivascular afferent terminals of these trigeminal fibers support the involvement of the trigemino-vascular system. The neurogenic inflammation triggered by activation of the trigeminal-vestibulocochlear reflex, with the subsequent inner ear plasma protein extravasation and the release of inflammatory mediators, can contribute to a sustained activation and sensitization of the trigeminal primary afferent neurons explaining VM symptoms. The reciprocal connections between brainstem vestibular nuclei and the structures that modulate trigeminal nociceptive inputs (rostral ventromedial medulla, ventrolateral periaqueductal gray, locus coeruleus, and nucleus raphe magnus) are critical to understand the pathophysiology of VM. Although cortical spreading depression can affect cortical areas involved in processing vestibular information, functional neuroimaging techniques suggest a dysmodulation in the multimodal sensory integration and processing of vestibular and nociceptive information, resulting from a vestibulo-thalamo-cortical dysfunction, as the pathogenic mechanism underlying VM. The elevated prevalence of VM suggests that multiple functional variants may confer a genetic susceptibility leading to a dysregulation of excitatory-inhibitory balance in brain structures involved in the processing of sensory information, vestibular inputs, and pain. The interactions among several functional and structural neural networks could explain the pathogenic mechanisms of VM.