BACKGROUND: Using proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes. METHODS: Two hundred nineteen carotid endarterectomy samples were procured from 120 patients. A sequential protein extraction protocol was employed in conjunction with multiplexed, discovery proteomics. To focus on extracellular proteins, parallel reaction monitoring was employed for targeted proteomics. Proteomic signatures were integrated with bulk, single-cell, and spatial RNA-sequencing data, and validated in 200 patients from the Athero-Express Biobank study. RESULTS: This extensive proteomics analysis identified plaque inflammation and calcification signatures, which were inversely correlated and validated using targeted proteomics. The inflammation signature was characterized by the presence of neutrophil-derived proteins, such as S100A8/9 (calprotectin) and myeloperoxidase, whereas the calcification signature included fetuin-A, osteopontin, and gamma-carboxylated proteins. The proteomics data also revealed sex differences in atherosclerosis, with large-aggregating proteoglycans versican and aggrecan being more abundant in females and exhibiting an inverse correlation with estradiol levels. The integration of RNA-sequencing data attributed the inflammation signature predominantly to neutrophils and macrophages, and the calcification and sex signatures to smooth muscle cells, except for certain plasma proteins that were not expressed but retained in plaques, such as fetuin-A. Dimensionality reduction and machine learning techniques were applied to identify 4 distinct plaque phenotypes based on proteomics data. A protein signature of 4 key proteins (calponin, protein C, serpin H1, and versican) predicted future cardiovascular mortality with an area under the curve of 75% and 67.5% in the discovery and validation cohort, respectively, surpassing the prognostic performance of imaging and histology. CONCLUSIONS: Plaque proteomics redefined clinically relevant patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events.

Photon-counting detector CT detected subtle post-COVID-19 lung abnormalities indicative of irreversible pulmonary fibrosis and provided superior image quality compared with conventional energy-inte... Background Photon-counting detector (PCD) CT allows ultra-high-resolution lung imaging and may shed light on morphologic correlates of persistent symptoms after COVID-19. Purpose To compare PCD CT ...

Ultralow-dose photon-counting CT yielded appropriate image quality to show anatomic structures and detect interstitial lung abnormalities, thus indicating its usefulness as a tool for long-term follow-up after lung transplant.

Objectives: In cardiac amyloidosis (CA) protein misfolding and consecutive storage into the extracellular myocardial compartment causes left ventricular hypertrophy and, in later stages of the disease, heart failure. The aim of this study was to compare extracellular volume (ECV) measurements obtained from photon-counting CT (PCCT) to the imaging reference cardiac magnetic resonance imaging (CMR) and to evaluate coronary artery disease (CAD) in a CA cohort. Materials and Methods: Thirty CA patients (mean age 77.5 +/− 7.9 years) underwent clinically indicated coronary CT angiography (CCTA) for the evaluation of CAD on a first-generation PCCT including a late-phase scan for assessment of ECV. ECV in PCCT was derived using 2 different techniques: (I) a single-energy (SE) technique, based on attenuation changes between the precontrast calcium scoring scan and delayed CCTA in the equilibrium phase (II) a dual-energy (DE) technique, based on iodine density maps from the delayed scan. Both methods were compared with CMR-derived ECV. Statistical analysis included repeated-measures analysis of variance (RM-ANOVA) with Bonferroni-adjusted pairwise comparisons. Correlations between methods were assessed using Pearson's correlation coefficient, and agreement was evaluated using Bland-Altman analysis. Results: CMR exhibited the highest mean ECV value (42.93 ± 10.14), followed by the SE method (42.5 ± 9.1), while the DE method yielded the lowest ECV values (40.7 ± 9.2). When compared with CMR, ECV obtained via the DE method was significantly lower (M _Diff = −2.24, P = 0.04). In contrast, no significant difference was observed between CMR and the SE method (M _Diff = 0.43, P = 1.00). Differences between the DE and SE methods were significant (M _Diff = −1.82, P < 0.001). Despite these differences, all 3 methods demonstrated excellent positive correlations. The strongest correlation was observed between the DE and SE methods (r = 0.98, P < 0.001), indicating high consistency in their measurements. Comparatively, the correlation between CMR and DE (r = 0.892, P < 0.001) was slightly stronger than that between CMR and SE methods (r = 0.882, P < 0.001). CAD was present in 29 (97.0%) CA patients with a mean Agatston score of 1086 ± 1398 (range 0–6848.5). Despite this high mean plaque burden and 14 (47.6%) patients presenting with atrial fibrillation, image quality was preserved in 29 (97.0%) patients with 17 (57.6%) of the patients having nonobstructive CAD. Conclusions: Compared to the imaging reference standard CMR, ECV derived from the DE and SE methods via PCCT demonstrated excellent positive correlations with CMR. The DE method exhibited minor differences compared to CMR, which were clinically not relevant. CAD with an extensive burden of calcified plaque was highly prevalent in CA; however, 57.6% of patients presented with nonobstructive CAD. Therefore, PCCT is a valuable tool for imaging both the coronary arteries and myocardial structure in CA.

The introduction of three-dimensional (3D) printed anatomical models has garnered interest in pre-operative planning, especially in orthopedic and trauma surgery. Identifying potential error sources and quantifying their effect on the model dimensional accuracy are crucial for the applicability and reliability of such models. In this study, twenty radii were extracted from anatomic forearm specimens and subjected to osteotomy to simulate a defined fracture of the distal radius (Colles’ fracture). Various factors, including two different computed tomography (CT) technologies (energy-integrating detector (EID) and photon-counting detector (PCD)), four different CT scanners, two scan protocols (i.e., routine and high dosage), two different scan orientations, as well as two segmentation algorithms were considered to determine their effect on 3D model accuracy. Ground truth was established using 3D reconstructions of surface scans of the physical specimens. Results indicated that all investigated variables significantly impacted the 3D model accuracy (p < 0.001). However, the mean absolute deviation fell within the range of 0.03 ± 0.20 to 0.32 ± 0.23 mm, well below the 0.5 mm threshold necessary for pre-operative planning. Intra- and inter-operator variability demonstrated fair to excellent agreement for 3D model accuracy, with an intra-class correlation (ICC) of 0.43 to 0.92. This systematic investigation displayed dimensional deviations in the magnitude of sub-voxel imaging resolution for all variables. Major pitfalls included missed or overestimated bone regions during the segmentation process, necessitating additional manual editing of 3D models. In conclusion, this study demonstrates that 3D bone fracture models can be obtained with clinical routine scanners and scan protocols, utilizing a simple global segmentation threshold, thereby providing an accurate and reliable tool for pre-operative planning.