Search

Background: The state of food comminution caused by chewing is an important factor triggering the swallowing reflex. However, the impact of retronasal aroma released from comminuted food to the nose upon swallowing during food intake is poorly understood. Objective: The present study investigated the relationship between aroma concentration and swallowing threshold while chewing a standardised test food. Methods: Twenty healthy participants took part in this study. Concentration of retronasal aroma was measured over time through the nostrils using an odour sensor. The aroma concentration was measured while chewing an orange‐flavoured gummy jelly until swallowing, and the number of chewing strokes was measured to determine the swallowing threshold. Next, the aroma concentration was measured while chewing the gummy jelly for 30 strokes, and 100% and 200% of swallowing threshold without swallowing. The surface area increase in the expectorated gummy jelly pieces was calculated using image analysis and defined as masticatory performance at 30 strokes. Results: The average number of chewing strokes until the swallowing threshold was 45.1 ± 14.2. Higher masticatory performance was associated with a smaller number of chewing strokes at swallowing threshold, and a greater increase in surface area at the swallowing threshold. The number of chewing strokes and the aroma concentration were similar between the swallowing threshold and at the maximum aroma concentration while chewing at 200% of the swallowing threshold. Conclusion: The findings suggested that the swallowing threshold might be influenced by retronasal aroma concentration as well as the state of food comminution. [ABSTRACT FROM AUTHOR]

A male patient in his 80s was brought to our emergency room with a one-day history of general malaise and cough. He had received a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccination seven days earlier. He initially presented with respiratory failure, and an alveolar hemorrhage was suspected. The patient was intubated and admitted to the intensive care unit. We suspected that the alveolar hemorrhage was caused by acute respiratory distress syndrome (ARDS). Treatment including high-dose corticosteroid therapy and venovenous extracorporeal membrane oxygenation (ECMO) was initiated, however, the patient did not respond to treatment and died 35 days after admission. Pathological autopsy revealed diffuse alveolar damage (DAD) and idiopathic diffuse alveolar hemorrhage (DAH) consistent with ARDS. ARDS should be considered as a differential diagnosis when a patient develops respiratory symptoms after receiving the SARS-CoV-2 vaccine., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Kawano et al.)

Introduction: The tongue plays an important role in mastication, swallowing, and articulation, but it cannot be directly observed because of its location inside the oral cavity. This study aimed to clarify detailed 3D tongue movements during chewing using electromagnetic articulography (EMA)., Materials and Methods: The participants were 10 healthy, young volunteers (average age 26.8 ± 2.1 years; 5 males, 5 females). Tongue and jaw movement during gum chewing was measured and recorded using EMA. Four EMA sensors were attached to the anterior, posterior, left, and right surfaces of the tongue, and one sensor was also attached to the mandibular left incisor. The tongue motion during the chewing cycle was spatially and sequentially analyzed based on the motion trajectories of the tongue and mandible., Results and Discussion: The tongue moved downward and to the masticatory side in a manner similar to the movement of the jaw. The anterior tongue marker moved downward to a greater extent than the other tongue markers. However, the tongue moved forward as the jaw moved backward. The anterior marker reached the most anterior position during the jaw-opening phase and the posterior markers reached the most anterior position during the jaw-closing phase. Just before maximum jaw-opening, all markers on the tongue reached the bottom lowest position. During the jaw-closing phase, the tongue reached the dominant farthest position in the masticatory side. All the markers reached the most posterior position during the occlusal phase., Conclusion: These findings demonstrate the sequence of tongue motion patterns during gum chewing., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sato, Kodama, Okawa, Murakami, Ono and Hori.)

Purpose: To build an image recognition network to evaluate tongue coating status., Methods: Two image recognition networks were built: one for tongue detection and another for tongue coating classification. Digital tongue photographs were used to develop both networks; images from 251 (178 women, 74.7±6.6 years) and 144 older adults (83 women, 73.8±7.3 years) who volunteered to participate were used for the tongue detection network and coating classification network, respectively. The learning objective of the tongue detection network is to extract a rectangular region that includes the tongue. You-Only-Look-Once (YOLO) v2 was used as the detection network, and transfer learning was performed using ResNet-50. The accuracy was evaluated by calculating the intersection over the union. For tongue coating classification, the rectangular area including the tongue was divided into a grid of 7×7. Five experienced panelists scored the tongue coating in each area using one of five grades, and the tongue coating index (TCI) was calculated. Transfer learning for tongue coating grades was performed using ResNet-18, and the TCI was calculated. Agreement between the panelists and network for the tongue coating grades in each area and TCI was evaluated using the kappa coefficient and intraclass correlation, respectively., Results: The tongue detection network recognized the tongue with a high intersection over union (0.885±0.081). The tongue coating classification network showed high agreement with tongue coating grades and TCI, with a kappa coefficient of 0.826 and an intraclass correlation coefficient of 0.807, respectively., Conclusions: Image recognition enables simple and detailed assessment of tongue coating status.