Testosterone does not mediate the correlation between dietary inflammation and serum klotho levels among males: insights from NHANES database.

Introduction: Serum Klotho (S-Klotho) is a transmembrane protein holds pivotal roles in anti-aging. The Dietary Inflammation Index (DII), a meticulously dietary tool, quantifies the inflammatory potential of an individual's diet. The existing research strongly suggests that a low DII diet plays a significant role in delaying aging and reducing aging-related symptoms in males. Testosterone could potentially act as a mediating intermediary between DII and S-Klotho. However, this aspect remains unexplored. This study aims to investigate the potential causal link of testosterone between DII and S-Klotho in males. Methods: We utilized data from National Health and Nutrition Examination Survey (NHANES) which focused on male participants from 2013-2016. Mediation analyses were used to investigate the effects of testosterone (TT), free testosterone (FT), and free androgen index (FAI) on the DII-S-Klotho relationship, using three modes adjusting for covariates. Results: Mediation analysis unveiled a significant inverse correlation between DII and S-Klotho levels (model 1: c = -14.78, p = 0.046). The interaction between DII and S-Klotho was modulated by TT in model 1 (ab = -1.36; 95% CI: -5.59, -0.55; p = 0.008), but lost significance after adjustments (model 2: ab = -0.39; 95% CI: -4.15, 1.66; p = 0.378; model 3: ab = -0.59; 95% CI: -4.08, 2.15; p = 0.442). For FT, the mediating impact was not statistically significant (model 1: ab = 0.43; 95% CI: -0.51, 5.44; p = 0.188; model 2: ab = 0.72; 95% CI: -0.26, 5.91; p = 0.136; model 3: ab = 0.84; 95% CI: -0.02, 8.06; p = 0.056). Conversely, FAI consistently influenced the DII-S-Klotho relationship (model 1: ab = 2.39; 95% CI: 0.69, 9.42; p = 0.002), maintaining significance after adjustments (model 2: ab = 3.2; 95% CI: 0.98, 11.72; p = 0.004; model 3: ab = 3.15; 95% CI: 0.89, 14.51; p = 0.026). Discussion: This study observed no mediating influence of TT or FT on the correlation between DII and S-Klotho after covariate control. Remarkably, FAI continued to significantly mediate the DII-S-Klotho connection even following covariate adjustment, although its significance in males warrants careful consideration.

Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network.

Background: Stereotactic radiosurgery (SRS) treatment planning requires accurate delineation of brain metastases, a task that can be tedious and time-consuming. Although studies have explored the use of convolutional neural networks (CNNs) in magnetic resonance imaging (MRI) for automatic brain metastases delineation, none of these studies have performed clinical evaluation, raising concerns about clinical applicability. This study aimed to develop an artificial intelligence (AI) tool for the automatic delineation of single brain metastasis that could be integrated into clinical practice. Methods: Data from 426 patients with postcontrast T1-weighted MRIs who underwent SRS between March 2007 and August 2019 were retrospectively collected and divided into training, validation, and testing cohorts of 299, 42, and 85 patients, respectively. Two Gamma Knife (GK) surgeons contoured the brain metastases as the ground truth. A novel 2.5D CNN network was developed for single brain metastasis delineation. The mean Dice similarity coefficient (DSC) and average surface distance (ASD) were used to assess the performance of this method. Results: The mean DSC and ASD values were 88.34%±5.00% and 0.35±0.21 mm, respectively, for the contours generated with the AI tool based on the testing set. The DSC measure of the AI tool's performance was dependent on metastatic shape, reinforcement shape, and the existence of peritumoral edema (all P values <0.05). The clinical experts' subjective assessments showed that 415 out of 572 slices (72.6%) in the testing cohort were acceptable for clinical usage without revision. The average time spent editing an AI-generated contour compared with time spent with manual contouring was 74 vs. 196 seconds, respectively (P<0.01). Conclusions: The contours delineated with the AI tool for single brain metastasis were in close agreement with the ground truth. The developed AI tool can effectively reduce contouring time and aid in GK treatment planning of single brain metastasis in clinical practice.