High-Similarity-Pass Attention for Single Image Super-Resolution.

Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually use the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to that of the NLA with randomly selected regions prompted us to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code and a pre-trained model were uploaded to GitHub (https://github.com/laoyangui/HSPAN) for validation.

Uric acid to high-density lipoprotein cholesterol ratio predicts adverse cardiovascular events in patients with coronary chronic total occlusion.

BACKGROUND AND AIMS: Uric acid to high-density lipoprotein cholesterol ratio (UHR) is a novel index of metabolism and inflammation proposed by recent studies. The prognostic value of UHR is undetermined in patients with coronary chronic total occlusion (CTO). The aim of this study was to investigate the association of UHR with adverse cardiovascular events in patients with CTO. METHODS AND RESULTS: In this retrospective cohort study, we enrolled 566 patients with CTO lesion in our hospital from January 2016 to December 2019. Patients were divided into three groups based on UHR level. The primary endpoint was major adverse cardiovascular event (MACE), defined as a combination of death, non-fatal MI, target vessel revascularization (TVR), and non-fatal stroke. The median follow-up time of this study was 43 months. During the follow-up, 107 (18.9%) MACEs were recorded. Kaplan-Meier survival plots show the cumulative incidence of MACE-free decreased across tertile of UHR (log-rank test, p < 0.001). In the fully adjusted model, the Hazard ratio (95% CI) of MACE was 2.16 (1.17-3.99) in tertile 3 and 2.01 (1.62-2.49) for per SD increase in UHR. CONCLUSION: Elevated UHR predicts an increasing risk of MACE in patients with CTO. UHR is a simple and reliable indicator for risk stratification and early intervention in CTO patients.

The interaction between uric acid and high-density lipoprotein cholesterol on the prognosis of patients with acute myocardial infarction.

Background: The significance of uric acid (UA) and high-density lipoprotein cholesterol (HDL-C) in the prognosis of acute myocardial infarction (AMI) remains controversial. This study investigated the effect of the interaction between UA and HDL-C on the prognosis of patients with AMI. Methods: In total, 480 patients with AMI were included in this study. Baseline and follow-up data were collected, and the primary endpoint was major adverse cardiovascular events (MACE). The secondary endpoint was all-cause death. Both additive and multiplicative interactions were calculated to evaluate their interaction with prognosis. Then, the impact of UA and HDL-C ratio (UHR) on prognosis was assessed. Results: Over a median follow-up period of 41 (30,46) months, 136 (28.3%) MACEs, and 44 (9.2%) deaths were recorded. There was a positive additive interaction between UA and HDL-C for MACEs. The attributable proportion (AP) showed that 46% of the estimated effect (MACE in patients) was attributable to this interaction. The synergy index (SI) was 2.04 (1.07,3.88) for MACE, indicating that the risk for patients presenting with both risk factors was greater than the sum of the risk factors alone. Multivariate Cox regression analysis revealed that UHR independently predicted MACEs and mortality. Kaplan-Meier survival curves according to tertiles of UHR showed statistically significant differences in MACE (log-rank test, P < 0.001). Receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of UHR for predicting MACE was 0.716. Conclusion: The coexistence of high UA and low HDL-C has a synergistic effect and provides further information for risk stratification of patients with AMI. UHR is a simple and easily available prognostic indicator independent of traditional risk factors.

Acetylation, ferroptosis, and their potential relationships: Implications in myocardial ischemia-reperfusion injury.

Myocardial ischemia-reperfusion injury (MIRI) is a serious complication affecting the prognosis of patients with myocardial infarction and can cause cardiac arrest, reperfusion arrhythmias, no-reflow, and irreversible myocardial cell death. Ferroptosis, an iron-dependent, peroxide-driven, non-apoptotic form of regulated cell death, plays a vital role in reperfusion injury. Acetylation, an important post-translational modification, participates in many cellular signaling pathways and diseases, and plays a pivotal role in ferroptosis. Elucidating the role of acetylation in ferroptosis may therefore provide new insights for the treatment of MIRI. Here, we summarized the recently discovered knowledge about acetylation and ferroptosis in MIRI. Finally, we focused on the acetylation modification during ferroptosis and its potential relationship with MIRI.

Anticoagulation therapy and clinical outcomes following transcatheter mitral valve repair for patients with mitral regurgitation: A meta-analysis.

Transcatheter mitral valve repair (TMVR) using MitraClip (MC) is now an established technique in the interventional treatment of mitral regurgitation. Common complications after MC procedure are bleeding and ischemic events. However, 2017 ESC/EACTS and 2020 ACC/AHA did not give a clear antithrombotic protocol, the policy has been based on clinical experience. Here, we performed a meta-analysis comparing outcomes with and without the addition of anticoagulants after TMVR. We searched the Cochrane Library, EMBASE, PubMed, and Web of Science from inception to October 6, 2022 to identify studies with or without the use of anticoagulants after TMVR. From each study, we extracted the number of people with bleeding, stroke, combined endpoints, and all-cause death. Five observational cohort studies were included, enrolling a total of 1892 patients undergoing TMVR who were assigned to either the anticoagulation group (n = 1209) or the no-anticoagulation group (n = 683). Pooled analysis showed a significantly lower stroke rate in the anticoagulated group (at least 4 weeks duration) compared with the non-anticoagulated group (RR [95% CI] = 0.14 [0.0-0.77], p = 0.02), and similar rates of bleeding, combined endpoints, and all-cause death in both groups (RR [95% CI] = 0.76 [0.48-1.22], p = 0.26), (RR [95% CI] = 0.52 [0.10-2.63], p = 0.43), and (RR [95% CI] = 0.89 [0.58-1.35], p = 0.58). We observed a reduced risk of stroke without elevated risk of bleeding, combined endpoints, or all-cause death in patients using anticoagulants (at least 4 weeks duration) after TMVR compared to no anticoagulants.

Global Learnable Attention for Single Image Super-Resolution.

Self-similarity is valuable to the exploration of non-local textures in single image super-resolution (SISR). Researchers usually assume that the importance of non-local textures is positively related to their similarity scores. In this paper, we surprisingly found that when repairing severely damaged query textures, some non-local textures with low-similarity which are closer to the target can provide more accurate and richer details than the high-similarity ones. In these cases, low-similarity does not mean inferior but is usually caused by different scales or orientations. Utilizing this finding, we proposed a Global Learnable Attention (GLA) to adaptively modify similarity scores of non-local textures during training instead of only using a fixed similarity scoring function such as the dot product. The proposed GLA can explore non-local textures with low-similarity but more accurate details to repair severely damaged textures. Furthermore, we propose to adopt Super-Bit Locality-Sensitive Hashing (SB-LSH) as a preprocessing method for our GLA. With the SB-LSH, the computational complexity of our GLA is reduced from quadratic to asymptotic linear with respect to the image size. In addition, the proposed GLA can be integrated into existing deep SISR models as an efficient general building block. Based on the GLA, we constructed a Deep Learnable Similarity Network (DLSN), which achieves state-of-the-art performance for SISR tasks of different degradation types (e.g., blur and noise). Our code and a pre-trained DLSN have been uploaded to GitHub† for validation.