Age at first birth in women is genetically associated with increased risk of schizophrenia

Prof. Paunio on PGC:n jäsenPrevious studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.Peer reviewe

PET/CT-guided percutaneous liver mass biopsies and ablations: Targeting accuracy of a single 20 s breath-hold PET acquisition

Aim To determine whether a single 20 s breath-hold positron-emission tomography (PET) acquisition obtained during combined PET/computed tomography (CT)-guided percutaneous liver biopsy or ablation procedures has the potential to target 2-[18F]-fluoro-2-deoxy-d-glucose (FDG)-avid liver masses as accurately as up to 180 s breath-hold PET acquisitions. Materials and methods This retrospective study included 10 adult patients with 13 liver masses who underwent FDG PET/CT-guided percutaneous biopsies (n = 5) or ablations (n = 5). PET was acquired as nine sequential 20 s, monitored, same-level breath-hold frames and CT was acquired in one monitored breath-hold. Twenty, 40, 60, and 180 s PET datasets were reconstructed. Two blinded readers marked tumour centres on randomized PET and CT datasets. Three-dimensional spatial localization differences between PET datasets and either 180 s PET or CT were analysed using multiple regression analyses. Statistical tests were two-sided and p < 0.05 was considered significant. Results Targeting differences between 20 s PET and 180 s PET ranged from 0.7-20.3 mm (mean 5.3 \ub1 4.4 mm; median 4.3) and were not statistically different from 40 or 60 s PET (p = 0.74 and 0.91, respectively). Targeting differences between 20 s PET and CT ranged from 1.4-36 mm (mean 9.6 \ub1 7.1 mm; median 8.2 mm) and were not statistically different from 40, 60, or 180 s PET (p = 0.84, 0.77, and 0.35, respectively). Conclusion Single 20 s breath-hold PET acquisitions from PET/CT-guided percutaneous liver procedures have the potential to target FDG-avid liver masses with equivalent accuracy to 180 s summed, breath-hold PET acquisitions and may facilitate strategies that improve image registration and shorten procedure times

Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection

A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented

Management of oral epithelial dysplasia: a review

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