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Holgersen Skaarup posted an update 6 months, 1 week ago
The original version of this article, published on 18 September 2020, unfortunately contained a mistake.A Correction to this paper has been published https//doi.org/10.1007/s00330-020-07506-0.A Correction to this paper has been published https//doi.org/10.1007/s00330-020-07505-1.
This study evaluated the diagnostic performance of the proton density fat fraction (PDFF) in predicting the progression of osteoporotic vertebral compression fractures (OVCFs).
The cohort in this retrospective study consisted of 48 patients with OVCFs who underwent spine MRI that included PDFF between December 2016 and June 2018. The patients were divided into two groups (with versus without OVCF progression, based on the radiographic results obtained at the 6-month follow-up examination). Two musculoskeletal radiologists independently calculated the PDFF of the fracture and the PDFF ratio (fracture PDFF/normal vertebrae PDFF) using regions of interest. The mean values of these parameters were compared between the two groups, and the receiver operating characteristic curves were analysed.
The mean age was significantly higher in the group with OVCF progression (71.6 ± 8.4 years) than in the group without (64.8 ± 10.5 years) (p = 0.018). According to reader 1, the PDFF ratio was significantly lower in tho is significantly lower in patients with OVCF progression. • The PDFF ratio is superior to the PDFF for predicting OVCF progression.
The LI-RADS M (LR-M) category describes hepatic lesions probably or definitely malignant, but not specific for hepatocellular carcinoma in at-risk patients. Differentiation among LR-M entities, particularly detecting cholangiocarcinoma-containing tumors (M-CCs), is essential for treatment and prognosis. Thus, we aimed to develop diagnostic models on gadoxetate disodium-enhanced MRI comprising serum tumor markers and LI-RADS imaging features for M-CC.
Consecutive at-risk patients with LR-M lesions exclusively (no co-existing LR-4 and/or LR-5 lesions) were retrieved retrospectively from a prospectively collected database spanning 3 years. Intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (c-HCC-CCA) were classified together as M-CC. LI-RADS features determined by three independent radiologists and clinically relevant serum tumor markers were used to generate M-CC diagnostic models through logistic regression analysis against histology. Per-patient performance was evaluatedα-fetoprotein > 4.8 ng/mL, and absence of the LI-RADS feature “blood products in mass” achieved high accuracy for diagnosing cholangiocarcinoma-containing tumors. • In patients of whom all three criteria were fulfilled, the specificity for M-CC was 100%, which might reduce or eliminate the need for biopsy confirmation.
4.8 ng/mL, and absence of the LI-RADS feature “blood products in mass” achieved high accuracy for diagnosing cholangiocarcinoma-containing tumors. • In patients of whom all three criteria were fulfilled, the specificity for M-CC was 100%, which might reduce or eliminate the need for biopsy confirmation.
Clinical evidence suggests that the response to immune checkpoint blockade depends on the immune status in the tumor microenvironment. This study aims to predict the immunophenotyping (IP) and overall survival (OS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative magnetic resonance imaging (MRI) texture analysis.
A total of 78 ICC patients were included and divided into inflamed (n = 26) or non-inflamed (n = 52) immunophenotyping based on the density of CD8
T cells. The enhanced T1-weighted MRI in the arterial phase was employed with texture analysis. Prexasertib The logistic regression analysis was applied to select the significant features related to IP. The OS-related feature was determined by Cox proportional-hazards model and Kaplan-Meier analysis. IP and OS predictive models were developed using the selected features, respectively.
Three wavelets and one 3D feature have favorable ability to discriminate IP, a combination of which performed best with an AUC of 0.919. The inflamed immunophbuild the OS predictive model, which could well stratify ICC patients into high- and low-risk groups.
• The MRI texture signature, including three wavelets and one 3D feature, showed significant associations with immunophenotyping of ICC, and all have favorable ability to discriminate immunophenotyping; a combination of the above features performed best with an AUC of 0.919. • The only wavelet-HLH_firstorder_Median feature was associated with the OS of ICC and used to build the OS predictive model, which could well stratify ICC patients into high- and low-risk groups.Autophagy is responsible for degradation of non-essential or damaged cellular constituents and damaged organelles. The autophagy pathway maintains efficient cellular metabolism and reduces cellular stress by removing additional and pathogenic components. Dysfunctional autophagy underlies several diseases. Thus, several research groups have worked toward elucidating key steps in this pathway. Autophagy can be studied by animal modeling, chemical modulators, and in vitro disease modeling with induced pluripotent stem cells (iPSC) as a loss-of-function platform. The introduction of iPSC technology, which has the capability to maintain the genetic background, has facilitated in vitro modeling of some diseases. Furthermore, iPSC technology can be used as a platform to study defective cellular and molecular pathways during development and unravel novel steps in signaling pathways of health and disease. Different studies have used iPSC technology to explore the role of autophagy in disease pathogenesis which could not have been addressed by animal modeling or chemical inducers/inhibitors. In this review, we discuss iPSC models of autophagy-associated disorders where the disease is caused due to mutations in autophagy-related genes. We classified this group as “primary autophagy induced defects (PAID)”. There are iPSC models of diseases in which the primary cause is not dysfunctional autophagy, but autophagy is impaired secondary to disease phenotypes. We call this group “secondary autophagy induced defects (SAID)” and discuss them. Graphical abstract.
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