Left atrial appendage thrombus (LAAT) is a surrogate of thromboembolic events in patients with nonvalvular atrial fibrillation (NVAF). We aimed to investigate the risk factors for LAAT formation before catheter ablation and cardioversion beside the CHA2DS2-VASc score. In this case-control study, patients with NVAF who underwent transesophageal echocardiography (TEE) were included. Demographic data, laboratory results, and echocardiographic measurements were retrospectively collected. Logistic regression analysis was performed to determine risk factors predicting LAAT. Of the 543 included patients, LAAT was identified in 50 patients (9.2%). Multivariable logistic regression analysis for the entire cohort showed that NT-proBNP (per 500 ng/L increase, OR (95% CI): 1.09 (1.00–1.19), p = 0.038) and LDL-C (per 1 mmol/L increase, OR (95% CI): 1.70 (1.05–2.77), p = 0.032) were independently correlated with the presence of LAAT after the adjustment for CHA2DS2-VASc score and anticoagulant therapy. The subgroup analysis of patients without anticoagulant therapy also yielded similar results. Regarding patients with CHA2DS2-VASc scores ≤ 1, a higher level of LDL-C (per 1 mmol/L increase, OR (95% CI): 6.31 (2.38–16.74), p < 0.001) independently correlated with the presence of LAAT. The present study suggests that beyond CHA2DS2-VASc score, raised NT-proBNP and LDL-C are additional predictors for LAAT in NVAF patients.
Synthetic aperture radar (SAR) ship detection has been the focus of many previous studies. Traditional SAR ship detectors face challenges in complex environments due to the limitations of manual feature extraction. With the rise of deep learning (DL) techniques, SAR ship detection based on convolutional neural networks (CNNs) has achieved significant achievements. However, research on CNN-based SAR ship detection has mainly focused on improving detection accuracy, and relatively little research has been conducted on reducing computational complexity. Therefore, this paper proposes a lightweight detector, LssDet, for SAR ship detection. LssDet uses Shufflenet v2, YOLOX PAFPN and YOLOX Decopuled Head as the baseline networks, improving based on the cross sidelobe attention (CSAT) module, the lightweight path aggregation feature pyramid network (L-PAFPN) module and the Focus module. Specifically, the CSAT module is an attention mechanism that enhances the model’s attention to the cross sidelobe region and models the long-range dependence between the channel and spatial information. The L-PAFPN module is a lightweight feature fusion network that achieves excellent performance with little computational effort and a low parametric count. The Focus module is a low-loss feature extraction structure. Experiments showed that on the Sar ship detection dataset(SSDD), LssDet’s computational cost was 2.60 GFlops, the model’s volume was 2.25 M and AP@[0.5:0.95] was 68.1%. On the Large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0), LssDet’s computational cost was 4.49 GFlops, the model’s volume was 2.25 M and AP@[0.5:0.95] was 27.8%. Compared to the baseline network, LssDet had a 3.6% improvement in AP@[0.5:0.95] on the SSDD, and LssDet had a 1.5% improvement in AP@[0.5:0.95] on the LS-SSDD-v1.0. At the same time, LssDet reduced Floating-point operations per second (Flops) by 7.1% and Paraments (Params) by 23.2%. Extensive experiments showed that LssDet achieves excellent detection results with minimal computational complexity. Furthermore, we investigated the effectiveness of the proposed module through ablation experiments.
Introduction Intramural or epicardial locations of the arrhythmogenic substrate are regarded as one of the main reasons for radiofrequency (RF) catheter ablation failure. This study aims to conduct a comprehensive analysis of various factors including baseline impedance, irrigant and electrode configuration at similar ablation index (AI) value. Methods In 12 ex vivo swine hearts, RF ablation was performed at a target AI value of 500 and a multistep impedance load (100–180 Ω) in 4 settings: (1) conventional unipolar configuration with an irrigant of normal saline (NS); (2) conventional unipolar configuration with an irrigant of half normal saline (HNS); (3) bipolar configuration with an irrigant of NS; (4) sequential unipolar configuration with an irrigant of NS. The relationships between lesion dimensions and above factors were examined. Results Baseline impedance had a strong negative linear correlation with lesion dimensions at a certain AI. The correlation coefficient between baseline impedance and depth, width, and volume were R = −0.890, R = −0.755 and R = −0.813, respectively (p < .01). There were 10 (total: 10/100, 10%; bipolar: 10/25, 40%) transmural lesions during the whole procedure. Bipolar ablation resulted in significantly deeper lesion than other electrode configurations. Other comparisons in our experiment did not achieve statistical significance. Conclusion There is a strong negative linear correlation between baseline impedance and lesion dimensions at a certain AI value. Baseline impedance has an influence on the overall lesion dimensions among irrigated fluid and ablation configurations. Over a threshold impedance of 150 Ω, the predictive accuracy of AI can be compromised.
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