Objectives To characterize swallowing deficits in amyotrophic lateral sclerosis (ALS); investigate the delay in dysphagia onset; estimate correlations between dysphagia severity and patients' functional status; identify the symptom(s) most likely to predict dysphagia. Materials and methods A group of 49 consecutive patients with ALS, 14 with bulbar onset and 35 with spinal onset, underwent swallowing evaluation including bedside and fiberoptic endoscopic examination to detect dysphagia. Results Patients with dysphagia were more likely than those without to have bulbar onset ALS (P = 0.02); more severely impaired chewing (P = 0.01); and tongue muscle deficits (P = 0.001). The only variable measured at first examination significantly associated with dysphagia was a more than mild tongue muscle deficit. The only variable useful in predicting dysphagia was a chewing deficit. In 10 of the 49 patients studied, swallowing evaluation disclosed an impaired cough reflex. Conclusions Dysphagia in patients with ALS correlates significantly with bulbar onset and with oral swallowing impairment. Fiberoptic swallowing evaluation is a useful tool for detecting swallowing deficits and laryngeal sensitivity in patients with ALS. An impaired cough reflex is an unexpected finding in many patients with ALS.

Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.

Multiple sclerosis (MS) is an autoimmune disease in which the immune system reacts by damaging the central nervous system, specifically myelin and oligodendrocytes. It is the most debilitating neurological disease among young adults, causing personal, familiar, social, and professional limitations. Multiple sclerosis can cause disturbances in the orofacial district, due to a demyelination process on the nerves of the head and neck district. The aim of this study was to evaluate the oral health status, dysphagia, and quality of life of patients affected by MS. For this study, 101 patients aged between 12 and 70 (47 males, 54 females) affected by MS were selected, and three questionnaires were handed out and anonymously filled in by them: An oral hygiene test, DYMUS (DYsphagia in MUltiple Sclerosis), and IOHIP-14 (Italian version Oral Health Impact Profile). Through the analysis of the questionnaires it was possible to observe pathological conditions, such as gingival inflammation, xerostomia, dysphagia, neuralgia, and dysarthria. Through the analysis it was possible to outline how the roles of a medical team, composed of a dentist, otolaryngologist, and dental hygienist, are fundamental in coping with other medical figures, during the whole development of the diseases, as well as to prevent possible complications.