Causal effects of osteoporosis on structural changes in specific brain regions: a Mendelian randomization study.

Observational studies have reported that osteoporosis is associated with cortical changes in the brain. However, the inherent limitations of observational studies pose challenges in eliminating confounding factors and establishing causal relationships. And previous observational studies have not reported changes in specific brain regions. By employing Mendelian randomization, we have been able to infer a causal relationship between osteoporosis and a reduction in the surficial area (SA) of the brain cortical. This effect is partially mediated by vascular calcification. We found that osteoporosis significantly decreased the SA of global brain cortical (ß = -1587.62 mm2, 95%CI: -2645.94 mm2 to -529.32 mm2, P = 0.003) as well as the paracentral gyrus without global weighted (ß = - 19.42 mm2, 95%CI: -28.90 mm2 to -9.95 mm2, P = 5.85 × 10-5). Furthermore, we estimated that 42.25% and 47.21% of the aforementioned effects are mediated through vascular calcification, respectively. Osteoporosis leads to a reduction in the SA of the brain cortical, suggesting the presence of the bone-brain axis. Vascular calcification plays a role in mediating this process to a certain extent. These findings establish a theoretical foundation for further investigations into the intricate interplay between bone, blood vessels, and the brain.

Inflammatory arthritis and eye diseases: a Mendelian randomization study.

Objectives: The aim of this study was to determine causal associations between inflammatory arthritis and eye diseases (disorders of sclera, cornea, iris, and ciliary body [DSCIC] and disorders of choroid and retina [DCR]). Methods: Genome-wide association studies' summary data of rheumatoid arthritis (RA) from a large-scale meta-analysis were used to identify genetically predicted RA. UK Biobank source data predicted ankylosing spondylitis (AS), psoriatic arthritis (PsA), and juvenile idiopathic arthritis (JIA). Furthermore, data from the FinnGen Biobank were used to identify genetically predicted eye diseases. Two-sample Mendelian randomization analysis was used to assess the causal relationship between inflammatory arthritis and eye diseases in the European population. Inverse-variance weighting (IVW) was used as the primary method, while MR-Egger, weighted median, and MR-PRESSO outlier test were used to detect heterogeneity and pleiotropy. Results: Genetically determined RA was indeed observed to have a causal effect on DSCIC (odds ratio [OR] = 1.084, p = 2.353 × 10-10) and DCR (OR = 1.151, p = 1.584 × 10-19). AS was causally associated with DSCIC (OR = 1.068, p < 2.024 × 10-8). In addition, PsA was also found to have a causal association with an increased risk of 17.9% for the development of DSCIC (OR = 1.179, p = 0.003). On the flip side, DSCIC increased the risk of JIA (OR = 2.276, p = 0.003). Conclusion: Our study provided genetic evidence for the causal associations of RA, AS, and PsA with an increased risk of DSCIC, and a causal association between RA and DCR was also identified. In addition, DSCIC greatly increased the risk of JIA.

A fast threshold segmentation method for froth image base on the pixel distribution characteristic.

With the increase of the camera resolution, the number of pixels contained in froth image is increased, which brings many challenges to image segmentation. Froth size and distribution are the important index in froth flotation. The segmentation of froth images is always a problem in building flotation model. In segmenting froth images, Otsu method is usually used to get a binary image for classification of froth images, this method can get a satisfactory segmentation result. However, each gray level is required to calculate each of the between-class variance, it takes a longer time in froth images with a large number of pixels. To solve this problem, an improved method is proposed in this paper. Most froth images have the pixel distribution characteristic that the gray histogram curve is a sawtooth shape. The proposed method uses polynomial to fit the curve of gray histogram and takes the characteristic of gray histogram's valley into consideration in Otsu method. Two performance comparison methods are introduced and used. Experimental comparison between Otsu method and the proposed method shows that the proposed method has a satisfactory image segmentation with a low computing time.