Heel bone mineral density and various oral diseases: a bidirectional Mendelian randomization.

INTRODUCTION: Observational studies demonstrated that the relationship between bone mineral density and oral diseases is mixed. To access the association between heel bone mineral density and various oral diseases, we conducted the Mendelian randomization analysis to explore the association. MATERIALS AND METHODS: Two-sample bidirectional Mendelian analysis was used to explore the relationship between heel bone mineral density and various oral diseases. The inverse-variance weighted (IVW) was used as the primary effect estimate, and various methods were applied to test the reliability and stability of the results, namely MR-Egger, weighted median, simple mode, and weighted mode. RESULTS: This study showed that there was a negative relationship between heel BMD and periodontitis when heel BMD was used as an exposure factor and periodontitis as an outcome factor (IVW OR = 0.85; 95% CI, 0.75-0.95; p = 0.005). Bidirectional Mendelian randomization showed that there was no statistically significant association between periodontitis and heel bone mineral density when chronic periodontitis was the exposure factor (p > 0.05). And there was no significant relationship between heel bone mineral density and other oral diseases (dental caries, diseases of pulp and periapical tissues, impacted teeth, cleft lip, and cleft palate, oral and oropharyngeal cancer) (p > 0.05). CONCLUSION: This study showed that there was a negative relationship between heel bone density and periodontitis, and the decrease in heel bone density could promote the occurrence of periodontitis. In addition, there was no statistically significant relationship between heel bone density and other oral diseases.

Deep learning-based automatic sella turcica segmentation and morphology measurement in X-ray images.

BACKGROUND: Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs. METHODS: 1129 images were used to develop a deep learning-based segmentation network for automatic sella turcica segmentation. Besides, 50 images were used to test the generalization ability of the model. The performance of the segmented network was evaluated by the dice coefficient. Images in the test datasets were segmented by the trained segmentation network, and the segmentation results were saved in binary images. Then the extremum points and corner points were detected by calling the function in the OpenCV library to obtain the coordinates of the four landmarks of the sella turcica. Finally, the length, diameter, and depth of the sella turcica can be obtained by calculating the distance between the two points and the distance from the point to the straight line. Meanwhile, images were measured manually using Digimizer. Intraclass correlation coefficients (ICCs) and Bland-Altman plots were used to analyze the consistency between automatic and manual measurements to evaluate the reliability of the proposed methodology. RESULTS: The dice coefficient of the segmentation network is 92.84%. For the measurement of sella turcica, there is excellent agreement between the automatic measurement and the manual measurement. In Test1, the ICCs of length, diameter and depth are 0.954, 0.953, and 0.912, respectively. In Test2, ICCs of length, diameter and depth are 0.906, 0.921, and 0.915, respectively. In addition, Bland-Altman plots showed the excellent reliability of the automated measurement method, with the majority measurements differences falling within ± 1.96 SDs intervals around the mean difference and no bias was apparent. CONCLUSIONS: Our experimental results indicated that the proposed methodology could complete the automatic segmentation of the sella turcica efficiently, and reliably predict the length, diameter, and depth of the sella turcica. Moreover, the proposed method has generalization ability according to its excellent performance on Test2.

Effects of methamphetamine abuse on spatial cognitive function.

Methamphetamine (MA) abuse has been rising rapidly over the past decade, however, its impact in spatial cognitive function remains unknown. To understand its effect on visuospatial ability and spatial orientation ability, 40 MA users and 40 non-MA users conducted the Simple Reaction Task (Task 1), the Spatial Orientation Task (Task 2), and the Mental Rotation Task (Task 3), respectively. There was no significant difference in either accuracy or reaction time (RT) between 2 groups in Task 1. During Task 2, in comparison with non-MA users, MA users performed poorer on RT, but not in accuracy for foot and hand stimuli. In addition, both non-MA and MA users responded much more quickly to upward stimuli than downward stimuli on vertical surface, however, only non-MA users exhibited leftward visual field advantage in horizontal orientation processing. As for Task 3, MA users exhibited poorer performance and more errors than their healthy counterparts. For each group, linear relationship was revealed between RT and orientation angle, whereas MA abuse led to longer intercept for all stimuli involved. Our findings suggested that MA abuse may lead to a general deficit in the visuospatial ability and the spatial orientation ability with more serious impact in the former.

A flexible and highly pressure-sensitive graphene-polyurethane sponge based on fractured microstructure design.

A fractured microstructure design: A new type of piezoresistive sensor with ultra-high-pressure sensitivity (0.26 kPa(-1) ) in low pressure range (<2 kPa) and minimum detectable pressure of 9 Pa has been fabricated using a fractured microstructure design in a graphene-nanosheet-wrapped polyurethane (PU) sponge. This low-cost and easily scalable graphene-wrapped PU sponge pressure sensor has potential application in high-spatial-resolution, artificial skin without complex nanostructure design.