Elastin is one of the most important and abundant extracellular matrix (ECM) proteins that provide elasticity and resilience to tissues and organs, including vascular walls, ligaments, skin, and lung. Besides hereditary diseases, such as Williams-Beuren syndrome (WBS), which results in reduced elastin synthesis, injuries, aging, or acquired diseases can lead to the degradation of existing elastin fibers. Thus, the de novo synthesis of elastin is required in several medical conditions to restore the elasticity of affected tissues. Here, we applied synthetic modified mRNA encoding tropoelastin (TE) for the de novo synthesis of elastin and determined the mRNA-mediated elastin synthesis in cells, as well as ex vivo in porcine skin. EA.hy926 cells, human fibroblasts, and mesenchymal stem cells (MSCs) isolated from a patient with WBS were transfected with 2.5 μg TE mRNA. After 24 hr, the production of elastin was analyzed by Fastin assay and dot blot analyses. Compared with untreated cells, significantly enhanced elastin amounts were detected in TE mRNA transfected cells. The delivered synthetic TE mRNA was even able to significantly increase the elastin production in elastin-deficient MSCs. In porcine skin, approximately 20% higher elastin amount was detected after the intradermal delivery of synthetic mRNA by microinjection. In this study, we demonstrated the successful applicability of synthetic TE encoding mRNA to produce elastin in elastin-deficient cells as well as in skin. Thus, this auspicious mRNA-based integration-free method has a huge potential in the field of regenerative medicine to induce de novo elastin synthesis, e.g., in skin, blood vessels, or alveoli.

Objective The aim of this study was to compare the performance of 2 approved computer-aided detection (CAD) systems for detection of pulmonary solid nodules (PSNs) in an oncologic cohort. The first CAD system is based on a conventional machine learning approach (VD10F), and the other is based on a deep 3D convolutional neural network (CNN) CAD software (VD20A). Methods and Materials Nine hundred sixty-seven patients with a total of 2451 PSNs were retrospectively evaluated using the 2 different CAD systems. All patients had thin-slice chest computed tomography (0.6 mm) using 100 kV and 100 mAs and a high-resolution kernel (I50f). The CAD images generated by VD10F were transferred to the PACS for evaluation. The images generated by VD20A were evaluated using a Web browser–based viewer. Finally, a senior radiologist who was blinded for the CAD results examined the thin-slice images of every patient (ground truth). Results A total of 2451 PSNs were detected by the senior radiologist. CAD-VD10F detected 1401 true-positive, 143 false-negative, 565 false-positive (FP), and 342 true-negative PSNs, resulting in sensitivity of 90.7%, specificity of 37.7%, positive predictive value of 0.71, and negative predictive value of 0.70. CAD-VD20A detected 1381 true-positive, 163 false-negative, 337 FP, and 570 true-negative PSNs, resulting in sensitivity of 89.4%, specificity of 62.8%, positive predictive value of 0.80, and negative predictive value 0.77, respectively. The rate of FP per scan was 0.6 for CAD-VD10F and 0.3 for CAD-VD20A. Conclusions The new deep learning–based CAD software (VD20A) shows similar sensitivity with the conventional CAD software (VD10F), but a significantly higher specificity.

The goal of this study was to investigate the value of CT-textural features and volume-based PET parameters in comparison to serologic markers for response prediction in patients with diffuse large B-cell lymphoma (DLBCL) undergoing cluster of differentiation (CD19)-chimeric antigen receptor (CAR)-T cell therapy. We retrospectively analyzed the whole-body (WB)-metabolic tumor volume (MTV), the WB-total lesion glycolysis (TLG) and first order textural features derived from 18F-FDG-PET/CT, as well as serologic parameters (C-reactive protein [CRP] and lactate dehydrogenase [LDH], leucocytes) prior and after CAR-T cell therapy in 21 patients with DLBCL (57.7 ± 14.7 year; 7 female). Interleukin 6 (IL-6) and IL-2 receptor peaks were monitored after treatment onset and compared with patient outcome judged by follow-up 18F-FDG-PET/CT. In 12/21 patients (57%), complete remission (CR) was observed, whereas 9/21 patients (43%) showed partial remission (PR). At baseline, WB-MTV and WB-TLG were lower in patients achieving CR (35 ± 38 mL and 319 ± 362) compared to patients achieving PR (88 ± 110 mL and 1487 ± 2254; p < 0.05). The “entropy” proved lower (1.81 ± 0.09) and “uniformity” higher (0.33 ± 0.02) in patients with CR compared to PR (2.08 ± 0.22 and 0.28 ± 0.47; p < 0.05). Patients achieving CR had lower levels of CRP, LDH and leucocytes at baseline compared to patients achieving PR (p < 0.05). In the entire cohort, WB-MTV and WB-TLG decreased after therapy onset (p < 0.01) becoming not measurable in the CR-group. Leucocytes and CRP significantly dropped after therapy (p < 0.01). The IL-6 and IL-2R peaks after therapy were lower in patients with CR compared to PR (p > 0.05). In conclusion, volume-based PET parameters derived from PET/CT and CT-textural features have the potential to predict therapy response in patients with DLBCL undergoing CAR-T cell therapy.

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