The hedgehog tongue is a tactile and taste organ which carries out various functions. Detailed functional and morphological studies are required to clearly define the relationship of the hedgehog tongue with taste, food palatability, mastication and swallowing of food, as well as the production of sounds. The aim of this study was to determine the relationship between the morphological characteristics of the European hedgehog tongue and the lifestyle of this animal, as well as to compare findings with the results of studies on other vertebrates. Gross and micro-anatomical light and scanning electron microscopy studies revealed that the hedgehog tongue could be divided in three areas, namely the apex, body and root. A keratinized stratified squamous epithelium, which was smooth on the ventral surface but bore four types of papillae on the dorsal surface, lined the tongue. Three types of these papillae were found to have gustatory functions and to express their activity in close relation with the salivary glands. These simple conical filiform papillae were situated caudally and distributed one after the other without a break. The dome-shaped fungiform papillae on the apex, with the highest distribution rate on the apex edge, were small, but those on the body and root were large. The three circular vallate papillae were arranged in a triangular shape. The foliate papillae with a few tiny projections, found in a shallow furrow, were situated between the root and the body. Most of the nerve fibers observed in different sections of the tongue tissue were of the unmyelinated type, confirming that the main task of the hedgehog tongue was its gustatory function.
Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.
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