Can human maxillary premolars discriminate between sexes in South Asian populations? / ¿Pueden los premolares maxilares humanos discriminar entre sexos en las poblaciones del sur de Asia?

The studies have illustrated odontometric analysis can be used to determine the sexual dimorphism effect on size of the teeth in various populations. The main aim of the study was to identify the inter-cuspal-, bucco-lingual -dimensions and weight of human upper-arch pre-molars in males and females of different South Asian populations. These metrics can distinguish sex which can have application in mass disasters, archaeology of mingled human remains and the in unidentified or several ancestry. The sample size consisted of 60 orthodontically extracted maxillary pre-molars from Pakistani and Saudi Arabian populations respectively. For male and female groups of each population fifteen first and second maxillary premolars were collected respectively, stored in PBS solution. The weight of the individual teeth was recorded. Later, digitally pictures were captured parallel to the occlusal surface to measure maximal bucco-lingual and inter-cuspal dimensions using Image-J software. The dimensions and weights were compared using Students' t-test between males and females respective Pakistani and Saudi Arabian first (P1) and second (P2) maxillary pre-molars. The dimensions for male P1 and P2 were statistically significantly larger than that for females in both populations. Furthermore, wet-weight of pre-molars in males is significantly greater than females in both populations. The findings demonstrate maxillary pre-molars can discriminate between the sexes in various populations.

Las investigaciones han ilustrado que el análisis odontométrico se puede utilizar para determinar el efecto del dimorfismo sexual en el tamaño de los dientes en varias poblaciones. El objetivo principal del estudio fue identificar las dimensiones y el peso entre cúspides, buco-linguales y el peso de los premolares de la arcada superior humana en hombres y mujeres de diferentes poblaciones del sur de Asia. Estas medidas pueden distinguir el sexo y ser importante en desastres masivos, arqueología de restos humanos entremezclados y en ancestros no identificados. El tamaño de la muestra consistió en 60 premolares maxilares extraídos ortodóncicamente de las poblaciones de Pakistán y Arabia Saudita, respectivamente. Para los grupos de hombres y mujeres de cada población, se recogieron quince primeros y segundos premolares superiores respectivamente, almacenados en solución de PBS. Se registró el peso de los dientes individuales. Posteriormente se capturaron imágenes digitales paralelas a la superficie oclusal para medir las dimensiones máximas buco-linguales e intercúspides utilizando software Image-J. Las dimensiones y los pesos se compararon mediante la prueba t de Student entre lo premolares maxilares (P1) y segundos (P2) de hombres y mujeres paquistaníes y saudíes. Las dimensiones para P1 y P2 de los hombres fueron estadísticamente significativos mayores que para las mujeres en ambas poblaciones. Además, el peso húmedo de los premolares en los varones era significativamente mayor que el de las mujeres en ambas poblaciones. Los hallazgos demuestran que los premolares maxilares pueden discriminar entre los sexos en varias poblaciones.

Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data / 대한의료정보학회지

OBJECTIVES: Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. METHODS: We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. RESULTS: To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. CONCLUSIONS: In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.