Abstract Background: The coronavirus disease-19 (COVID-19) and its variants have increased rapidly worldwide since December 2019, with respiratory disease being a prominent complication. As such, optimizing evaluation methods and identifying factors predictive of disease progress remain critical. The purpose of the study was to assess late phase (≥3 weeks) pulmonary changes using intensity-based computed tomography (CT) scoring in COVID-19 patients and determine the clinical characteristics predicting lung abnormalities and recovery. Methods: We conducted a retrospective study on 42 patients (14 males, 28 females; age 65±10 years) with COVID-19. Only patients with at least 3 CT scans taken at least 3 weeks after initial symptom onset were included in the study. Two scoring methods were assessed: (1) area-based scoring (ABS) and (2) intensity-weighted scoring (IWS). Temporal changes in the average lung lesion were evaluated by the calculating the averaged area under the curve (AUC) of the CT score-time curve. Correlations between averaged AUCs and clinical characteristics were determined. Results : Using the ABS system, temporal changes in lung abnormalities during recovery were highly variable (P=0.934). By contrast, the IWS system detected more subtle changes in lung abnormalities during in COVID-19 patients, with consistent week-to-week relative reductions in IWS (P=0.025). Strong relationships were observed with D-dimer and C-reactive protein (CRP) levels on admission, with hazard ratios (HR)(95%CI) of 5.32 (1.25-22.6)(P=0.026) and 1.05 (1.10-1.09)(P=0.017), respectively. Conclusion : Our results suggest COVID-19-mediated pulmonary abnormalities persist well-beyond 3-weeks of symptom onset, with intensity-weighted rather than area-based scoring being more sensitive. Moreover, D-dimer and CRP levels were predictive of the recovery from the disease.
Provisional stenting using drug-eluting stent is effective for simple coronary bifurcation lesions. Kissing balloon inflation using conventional non-compliant balloon is the primary treatment of side branch (SB) after main vessel (MV) stenting. Drug-coating balloon (DCB) is reported to be associated with less frequent clinical events in in-stent restenosis and small vessel disease. The importance of DCB in bifurcation treatment is understudied. Accordingly, this trial is designed to investigate the superiority of DCB to non-compliant balloon angioplasty for SB after provisional stenting in patients with true coronary bifurcation lesions.
Abstract: Cardiac fibrosis often has adverse cardiovascular effects, including heart failure, sudden death, and malignant arrhythmias. However, there is no targeted therapy for cardiac fibrosis. Inflammation is known to play a crucial role in the disorder, and the NLR pyrin domain-containing-3 (NLRP3) inflammasome is closely associated with innate immunity. Therefore, further understanding the pathophysiological role of the inflammasome in cardiac fibrosis may provide novel strategies for the prevention and treatment of the disorder. The aim of this review was to summarize the present knowledge of NLRP3 inflammasome-related mechanisms underlying cardiac fibrosis and to suggest potential targeted therapy that could be used to treat the condition. Keywords: NLRP3 inflammasome, cardiac fibrosis, AIM2, ASC, caspase-1
Severe coronary artery calcification increases the difficulty of percutaneous coronary intervention procedures and impairs stent expansion. Herein, we report a case of a patient who was successfully treated with rotational atherectomy using a stepped burr strategy combined with intravascular lithotripsy for plaque modification under intracoronary imaging.A 65 year-old woman presented to our hospital with recurrent chest pain evolving for 1 year. Coronary angiography showed approximately 80% stenosis of the proximal mid-left anterior descending artery. Intravascular ultrasound (IVUS) and optical coherence tomography (OCT) revealed a 360° annular calcification. The calcification was rotablated with 1.5 and 1.75 burrs, and the lesion was undilatable with a 3.0 mm non-compliant balloon at 14 atm. Subsequently, the intravascular lithotripsy was reset for the modification of the calcified lesion. A shockwave balloon measuring 3.0 mm × 12 mm was delivered, and 40 pulses were performed at 6 atm. Intravascular imaging modalities (IVUS and OCT) revealed a circumferential calcified plaque with deep fractures. After post-balloon expansion followed by drug-eluting stent placement with a final stent expansion of 84%, there were no intraoperative complications and no major adverse cardiovascular events within 90 days postoperatively.A combination of rotational atherectomy and intravascular lithotripsy may be an effective and complementary strategy for the treatment of severely calcified lesions that cannot be resolved using a single procedure. However, more clinical studies are required to clarify this finding.
Abstract Cardiac magnetic resonance imaging (CMR) is widely adopted in clinic for the assessment of cardiac anatomical structures and functions, and accurately segmenting left ventricular, myocardium and right ventricle from CMR plays an important role in clinical practice. Convolutional neural networks (CNNs), for example, U‐Net, have been widely used in CMR segmentation. However, current CNN‐based models focus on extracting local features via convolution modules, which cannot well understand the long‐range dependencies within images, leading to the sub‐optimal solution for CMR segmentation task. Inspired by Mamba that efficiently captures global context information using state space model, we propose a novel CMR segmentation model, called CSS‐UNet, that can capture both local features and global contexts simultaneously by fusing features from convolution block and visual state space block. Our new model follows the design of U‐Net architecture that contains encoder and decoder with skip connections, where a proposed feature fusion module, called spatial‐state space, is seamlessly integrated into our model. By using the spatial‐state space module, the low‐level and high‐level features can be extracted and fused for capturing both global and local information, enhancing the capability of feature extraction of CSS‐UNet. We evaluate our proposed model on two public CMR datasets, and the experimental results reveal that our proposed model outperforms the most widely‐used UNet, demonstrating the effectiveness of our model.
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