Early posterior vitreous detachment is associated with LAMA5 dominant mutation

Background: Extracellular matrix molecular components, previously linked to multisystem syndromes include collagens, fibrillins and laminins. Recently, we described a novel multisystem syndrome caused by the c.9418G>A p.(V3140M) mutation in the laminin alpha-5 (LAMA5) gene, which affects connective tissues of all organs and apparatus in a three generation family. In the same family, we have also reported a myopic trait, which, however, was linked to the Prolyl 4-hydroxylase subunit alpha-2 (P4HA2) gene. Results of investigation on vitreous changes and their pathogenesis are reported in the present study.Materials and Methods: Nineteen family individuals underwent complete ophthalmic examination including best-corrected visual acuity (BCVA), fundus examination, fundus photography, intraocular pressure measurement, axial length measurement using ocular biometry, Goldmann visual field examination, standard electroretinogram, SD-OCT. Segregation analysis of LAMA5 and P4HA2 mutations was performed in enrolled members.Results: The vitreous alterations fully segregated with LAMA5 mutation in both young and adult family members. Slight reduction of retinal thickness and peripheral retinal degeneration in only two patients were reported.Conclusions: In this work we showed that PVD is a common trait of LAMA5 multisystem syndrome, therefore occurring as an age-unrelated trait. We hypothesize that the p.(V3140M) mutation results in a reduction of retinal inner limiting membrane (ILM) stability, leading to a derangement in the macromolecular structure of the vitreous gel, and PVD. Further investigations will be necessary to elucidate the role of wild type and mutated LAMA5 in the pathogenesis of PVD.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.     

Free Water Loss–induced Heterogeneous Residual Strain and Reduced Fatigue Resistance in Root Dentin: A 3-dimensional Digital Image Correlation Analysis

IntroductionThis study evaluated free water loss–induced residual strain with and without axial compressive loading and assessed the mechanical effect of cyclic loading in fully hydrated and partially dehydrated root dentin.MethodsRoot dentin sections prepared from freshly extracted human premolars were used. Customized 3-dimensional digital image correlation was used to qualitatively and quantitatively analyze the residual strain induced by 2 hours of free water loss in different regions of root dentin. Residual strain in partially dehydrated root dentin during axial compressive loading was also analyzed using 3-dimensional digital image correlation. The effect of cyclic loading on load to fracture in fully hydrated and partially dehydrated dentin and their fractography were analyzed using micro–computed tomographic imaging.ResultsFree water loss resulted in a heterogeneous distribution of residual strain and an overall formation of residual compressive strain with areas of tensile strain localized to the root canal and outer dentin. More residual compressive strain was observed in the apical dentin compared with the cervical dentin (P < .05), and more residual shear strain was observed in outer dentin compared with inner dentin (P < .05). Axial loading resulted in an increase in the load-induced compressive strain in the direction perpendicular to dentinal tubules (P < .05). Fully hydrated roots displayed a higher mean (P < .05) and median (P < .05) number of cycles to fracture with microcracks characteristic of toughness.ConclusionsAfter free water loss, root dentin displayed an increased formation of heterogenous residual strain, which resulted in increased axial compressive load-induced strain and a decreased resistance to fatigue failure. The effect of free water loss in the loss of mechanical integrity of root-filled teeth needs further investigation.     

高分辨率CT对颞部疾病的检查价值

目的：探讨高分辨率CT（HRCT）对颞部疾病的检查价值。方法：对43例颞部疾病患者行常规CT和高分辨率CT（HRCT）检查所获图像对比分析，并讨论HRCT的检查技术和图像后处理。结果：HRCT对病变的显示率及病变引起骨质破坏的程度，病变边缘，轮廓的显示均明显优于常规CT，尤其能清楚显示常规CT难以显示的中耳及内耳的细微结构，结论：高分辨率CT是颞部疾病的首选检查方法，使用高分辨率CT对颞部疾病的检查给临床提供更多，更准确的诊断信息。     

灰关联分析应用于因素分析时关联度指标的改进

本文指出在应用灰关联分析进行因素分析时传统的关联度计算方法的不足，并提出采用新的关联度指标来表示因素之间的关联关系，同时给出了实例介绍计算方法。