Efficacy and Safety of Tirofiban Before Stenting for Symptomatic Intracranial Atherosclerotic Stenosis: A Randomized Clinical Trial

BACKGROUND AND OBJECTIVES

Acute stent thrombosis (AST) is not uncommon and even catastrophic during intracranial stenting angioplasty in patients with symptomatic high-grade intracranial atherosclerotic stenosis (ICAS). The purpose of this study was to investigate whether adjuvant intravenous tirofiban before stenting could reduce the risk of AST and periprocedural ischemic stroke in patients receiving stent angioplasty for symptomatic ICAS.

METHODS

A prospective, multicenter, open-label, randomized clinical trial was conducted from September 9, 2020, to February 18, 2022, at 10 medical centers in China. Patients intended to receive stent angioplasty for symptomatic high-grade ICAS were enrolled and randomly assigned to receive intravenous tirofiban or not before stenting in a 1:1 ratio. The primary outcomes included the incidence of AST within 30 minutes after stenting, periprocedural new-onset ischemic stroke, and symptomatic intracranial hemorrhage. The outcomes were analyzed using logistic regression analysis to obtain an odds ratio and 95% confidence interval.

RESULTS

A total of 200 participants (122 men [61.0%]; median [interquartile ranges] age, 57 [52-66] years) were included in the analysis, with 100 participants randomly assigned to the tirofiban group and 100 participants to the control (no tirofiban) group. The AST incidence was lower in the tirofiban group than that in the control group (4.0% vs 14.0%; adjusted odds ratio, 0.25; 95% CI 0.08-0.82; p = 0.02). No significant difference was observed in the incidence of periprocedural ischemic stroke (7.0% vs 8.0%; p = 0.98) or symptomatic intracranial hemorrhage between the 2 groups.

DISCUSSION

This study suggests that adjuvant intravenous tirofiban before stenting could lower the risk of AST during stent angioplasty in patients with symptomatic high-grade ICAS.

TRIAL REGISTRATION INFORMATION

URL: chictr.org.cn; Unique identifier: ChiCTR2000031935.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that for patients with symptomatic high-grade ICAS, pretreatment with tirofiban decreases the incidence of acute stent thrombosis. This study is Class II due to the unequal distribution of involved arteries between the 2 groups.

GUIDE: Training Deep Graph Neural Networks via Guided Dropout Over Edges

Graph neural networks (GNNs) have made great progress in graph-based semi-supervised learning (GSSL). However, most existing GNNs are confronted with the oversmoothing issue that limits their expressive ability. A key factor that leads to this problem is the excessive aggregation of information from other classes when updating the node representation. To alleviate this limitation, we propose an effective method called GUIded Dropout over Edges (GUIDE) for training deep GNNs. The core of the method is to reduce the influence of nodes from other classes by removing a certain number of inter-class edges. In GUIDE, we drop edges according to the edge strength, which is defined as the time an edge acts as a bridge along the shortest path between node pairs. We find that the stronger the edge strength, the more likely it is to be an inter-class edge. In this way, GUIDE can drop more inter-class edges and keep more intra-class edges. Therefore, nodes in the same community or class are more similar, whereas different classes are more separated in the embedded space. In addition, we perform some theoretical analysis of the proposed method, which explains why it is effective in alleviating the oversmoothing problem. To validate its rationality and effectiveness, we conduct experiments on six public benchmarks with different GNNs backbones. Experimental results demonstrate that GUIDE consistently outperforms state-of-the-art methods in both shallow and deep GNNs.

Differential expression analysis identifies candidate synaptogenic molecules for wiring direction-selective circuits in the retina

An organizational feature of neural circuits is the specificity of synaptic connections. A striking example is the direction-selective (DS) circuit of the retina. There are multiple subtypes of DS retinal ganglion cells (DSGCs) that prefer motion along one of 4 preferred directions. This computation is mediated by selective wiring of a single inhibitory interneuron, the starburst amacrine cell (SAC), with each DSGC subtype preferentially receiving input from a subset of SAC processes. We hypothesize that the molecular basis of this wiring is mediated in part by unique expression profiles of DSGC subtypes. To test this, we first performed paired recordings from isolated mouse retina of both sexes to determine that postnatal day 10 (P10) represents the age at which asymmetric synapses form. Second, we performed RNA-sequencing and differential expression analysis on isolated P10 ON-OFF DSGCs tuned for either nasal or ventral motion and identified candidates which may promote direction-specific wiring. We then used a conditional knockout strategy to test the role of one candidate, the secreted synaptic organizer cerebellin-4 (Cbln4), in the development of DS tuning. Using two-photon calcium imaging, we observed a small deficit in directional tuning among ventral-preferring DSGCs lacking Cbln4, though whole-cell voltage clamp recordings did not identify a significant change in inhibitory inputs. This suggests that Cbln4 does not function primarily via a cell-autonomous mechanism to instruct wiring of DS circuits. Nevertheless, our transcriptomic analysis identified unique candidate factors for gaining insights into the molecular mechanisms that instruct wiring specificity in the DS circuit.Significance Statement By performing mRNA transcriptome analysis on three populations of direction-selective ganglion cells - two preferring horizontal motion and one preferring vertical motion - we identified differentially expressed candidate molecules potentially involved in cell subtype-specific synaptogenesis within this circuit. We tested the role of one differentially expressed candidate, Cbln4, enriched in ventral-preferring DSGCs. Using a targeted knockout approach, the deletion of Cbln4 led to a small reduction in direction-selective tuning while maintaining dendritic morphology and normal strength and asymmetry of inhibitory synaptic transmission. Overall, we have shown that this approach can be used to identify interesting candidate molecules, and future functional studies are required to reveal the mechanisms by which these candidates influence synaptic wiring within specific circuits.

Decline in Cancer Diagnoses during the 'Zero COVID' Policy in Hong Kong: Indirect Spillover Impact of the COVID-19 Pandemic

AIMS

Despite a largely successful 'zero COVID' policy in 2020, the COVID-19 pandemic disrupted routine cancer services in the city of Hong Kong. The aims of this study were to examine the trends in cancer incidence before and during the COVID-19 pandemic and estimate missed cancer diagnoses.

MATERIALS AND METHODS

We used population-based data from the Hong Kong Cancer Registry 1983-2020 to examine the trends of age- and sex-standardised cancer incidence before and during the COVID-19 pandemic. We applied: (i) the annual average percentage change (AAPC) calculated using the Joinpoint regression model and (ii) the autoregressive integrated moving average (ARIMA) model to forecast cancer incidence rates in 2020. Missed cancer diagnoses in 2020 were estimated by comparing forecasted incidence rates to reported rates. A subgroup analysis was conducted by sex, age and cancer site.

RESULTS

The cancer incidence in Hong Kong declined by 4.4% from 2019 to 2020 (male 8.1%; female 1.1%) compared with the long-term AAPC of 0.5% from 2005 to 2019 (95% confidence interval 0.3, 0.7). The gap between the reported and forecasted incidence for 2020 ranged from 5.1 to 5.7% (male 8.5%, 9.8%; female 2.3%, 3.5%). We estimated 1525-1596 missed cancer diagnoses (ARIMA estimate -98, 3148; AAPC 514, 1729) in 2020. Most missed diagnoses were in males (ARIMA 1361 [327, 2394]; AAPC 1401 [1353, 1460]), with an estimated 479-557 missed cases of colorectal cancer (ARIMA 112, 837; AAPC 518, 597) and 256-352 missed cases of prostate cancer (AAPC 231, 280; ARIMA 110, 594).

CONCLUSION

The incidence of new cancer diagnoses declined in 2020 contrary to the long-term increase over the previous decades. Significantly lower diagnoses than expected were observed in males, particularly for colorectal and prostate cancers. Fewer reported cancer cases indicate missed diagnoses and could lead to delayed treatment that could impact future health outcomes.

[Research advances of collagen-based biomaterials in wound repair]

Acute or chronic wounds are common clinical problems. Collagen, with advantages including rich sources, impeccable biocompatibility, and inherent biodegradability, has been widely used in fundamental research and clinical treatment of wound repair with broad prospects of clinical applications. This article provided a brief overview of the role of collagen in various biological processes related to wound healing and also outlined the sources of collagen. Furthermore, the article summarized the application and recent research advancements of collagen-based wound dressings in the field of wound repair.

[Strategies and advances in laboratory tests for primary liver cancer]

Primary liver cancer is one of the leading causes of cancer-related deaths worldwide, its early diagnosis and early treatment are of great clinical importance. The main detection tools for liver cancer include serological indicators, imaging tests and risk assessment models. With the advancement of technology and research, the sensitivity and specificity of laboratory tests for liver cancer have been substantially improved, but there are still false negatives and low rates of early diagnosis. For different causes and prevalence regions, each country has developed its clinical practice guidelines to guide risk groups for effective prevention, early diagnosis and standardized treatment. It is important to establish a liver cancer diagnosis strategy that is suitable for China's national conditions, concerning the guidelines for the vigilance and prevention of liver cancer. In this article, the advantages and disadvantages of liver cancer-related tests and the impact of future development trends on laboratory strategies are explained from the perspective of laboratory testing strategies, to provide theoretical support for the practical application of liver cancer diagnostic strategies.

Long and Short-Range Dependency Graph Structure Learning Framework on Point Cloud

Graph convolutional neural networks can effectively process geometric data and thus have been successfully used in point cloud data representation. However, existing graph-based methods usually adopt the K-nearest neighbor (KNN) algorithm to construct graphs, which may not be optimal for point cloud analysis tasks, owning to the solution of KNN is independent of network training. In this paper, we propose a novel graph structure learning convolutional neural network (GSLCN) for multiple point cloud analysis tasks. The fundamental concept is to propose a general graph structure learning architecture (GSL) that builds long-range and short-range dependency graphs. To learn optimal graphs that best serve to extract local features and investigate global contextual information, respectively, we integrated the GSL with the designed graph convolution operator under a unified framework. Furthermore, we design the graph structure losses with some prior knowledge to guide graph learning during network training. The main benefit is that given labels and prior knowledge are taken into account in GSLCN, providing useful supervised information to build graphs and thus facilitating the graph convolution operation for the point cloud. Experimental results on challenging benchmarks demonstrate that the proposed framework achieves excellent performance for point cloud classification, part segmentation, and semantic segmentation.

Improving Image Contrastive Clustering Through Self-Learning Pairwise Constraints

In this article, a new unsupervised contrastive clustering (CC) model is introduced, namely, image CC with self-learning pairwise constraints (ICC-SPC). This model is designed to integrate pairwise constraints into the CC process, enhancing the latent representation learning and improving clustering results for image data. The incorporation of pairwise constraints helps reduce the impact of false negatives and false positives in contrastive learning, while maintaining robust cluster discrimination. However, obtaining prior pairwise constraints from unlabeled data directly is quite challenging in unsupervised scenarios. To address this issue, ICC-SPC designs a pairwise constraints learning module. This module autonomously learns pairwise constraints among data samples by leveraging consensus information between latent representation and pseudo-labels, which are generated by the clustering algorithm. Consequently, there is no requirement for labeled images, offering a practical resolution to the challenge posed by the lack of sufficient supervised information in unsupervised clustering tasks. ICC-SPC's effectiveness is validated through evaluations on multiple benchmark datasets. This contribution is significant, as we present a novel framework for unsupervised clustering by integrating contrastive learning with self-learning pairwise constraints.

Exploring the role of edge distribution in graph convolutional networks

Graph Convolutional Networks (GCNs) have shown remarkable performance in processing graph-structured data by leveraging neighborhood information for node representation learning. While most GCN models assume strong homophily within the networks they handle, some models can also handle heterophilous graphs. However, the selection of neighbors participating in the node representation learning process can significantly impact these models' performance. To address this, we investigate the influence of neighbor selection on GCN performance, focusing on the analysis of edge distribution through theoretical and empirical approaches. Based on our findings, we propose a novel GCN model called Graph Convolution Network with Improved Edge Distribution (GCN-IED). GCN-IED incorporates both direct edges, which rely on local neighborhood similarity, and hidden edges, obtained by aggregating information from multi-hop neighbors. We extensively evaluate GCN-IED on diverse graph benchmark datasets and observe its superior performance compared to other state-of-the-art GCN methods on heterophilous datasets. Our GCN-IED model, which considers the role of neighbors and optimizes edge distribution, provides valuable insights for enhancing graph representation learning and achieving superior performance on heterophilous graphs.

Network pharmacological and molecular docking verification of the mechanism of Osteoking in preventing deep vein thrombosis of lower limb

OBJECTIVE

The aim of this study was to predict the mechanism of Osteoking in preventing deep vein thrombosis (DVT) of the lower limb by network pharmacology and molecular docking.

MATERIALS AND METHODS

The relevant active components and targets of Osteoking were collected through the TCMSP database, and the relevant disease targets of DVT were collected through the GeneCards, OMIM, and DisGeNET databases. The intersecting gene targets of Osteoking and DVT were obtained using Venny 2.1.0 software. PPI network construction and core target selection using Cytoscape 3.9.0 software. The Metascape database was used for GO and KEGG enrichment analysis of relevant targets. Finally, the molecular docking of the main active components and key targets was carried out.

RESULTS

There are 361 potential targets and 71 core targets of Osteoking in preventing deep vein thrombosis of the lower limb. Signal pathways are involved in various diseases such as cancer, diabetic complications, atherosclerosis, and more. Some of the most common pathways include AGE-RAGE signaling pathway and Calcium signaling pathway. Molecular docking results showed that the main active components of Osteoking had relatively stable binding activities with the key targets.

CONCLUSIONS

Osteoking can play a role through multiple targets and multiple signal pathways to prevent the formation of deep venous thrombosis of the lower limb after fracture.

Temporal Structures in Positron Spectra and Charge-Sign Effects in Galactic Cosmic Rays

We present the precision measurements of 11 years of daily cosmic positron fluxes in the rigidity range from 1.00 to 41.9 GV based on 3.4×10^{6} positrons collected with the Alpha Magnetic Spectrometer (AMS) aboard the International Space Station. The positron fluxes show distinctly different time variations from the electron fluxes at short and long timescales. A hysteresis between the electron fluxes and the positron fluxes is observed with a significance greater than 5σ at rigidities below 8.5 GV. On the contrary, the positron fluxes and the proton fluxes show similar time variation. Remarkably, we found that positron fluxes are modulated more than proton fluxes with a significance greater than 5σ for rigidities below 7 GV. These continuous daily positron fluxes, together with AMS daily electron, proton, and helium fluxes over an 11-year solar cycle, provide unique input to the understanding of both the charge-sign and mass dependencies of cosmic rays in the heliosphere.

Efficacy of Postoperative Radiotherapy for Patients with New N2 Descriptors of Subclassification in Completely Resected Non-Small Cell Lung Cancer: A Real-World Study

PURPOSE/OBJECTIVE(S)

Patients with N2 non-small cell lung cancer (NSCLC) were heterogeneous groups and required further stratification. The International Society for the Study of Lung Cancer (IASLC) added new descriptors of three sub-stages for stage N2 NSCLC: N2 at a single station without N1 involvement (N2a1), N2 at a single station with N1 involvement (N2a2), and N2 at multiple stations (N2b). This study aimed to investigate the efficacy of postoperative radiotherapy (PORT) for patients with these N2 descriptors.

MATERIALS/METHODS

Patients with histologically confirmed NSCLC after complete resection and divided into PORT group and non-PORT group. The primary endpoint was DFS. The second endpoints were overall survival (OS) and locoregional recurrence-free survival (LRFS). Propensity-score matching (PSM) of baseline characteristics between the PORT and non-PORT groups was used for validation.

RESULTS

Totally 1832 patients were enrolled, including 308 N2a1 patients, 682 N2a2 patients, and 842 N2b patients. The median follow-up time was 50.1 months. The survival outcomes of the PORT and non-PORT groups before PSM were shown in Table 1. For patients with N2a1, PORT could not improve the DFS (median DFS of the PORT group and the non-PORT group: not reached vs. 46.8 months, P = 0.41), OS (P = 0.85), or LRFS (P = 0.32), which were consistent with the multivariate analysis and data after the PSM. For patients with N2a2, PORT significantly improved the DFS (median DFS 29.7 vs. 22.2 months, P = 0.02), OS (P = 0.03), and LRFS (P = 0.01). The multivariate analysis and data after the PSM confirmed the benefits in DFS and LRFS, but no benefit was observed in OS (multivariate analysis: HR 0.79, P = 0.18; median OS after PSM: 103.7 vs. 63.1 months, P = 0.34). For patients with N2b, PORT could not improve the DFS (median DFS 20.6 vs. 21.2 months, P = 0.39) but significantly improved the OS (P<0.001) and LRFS (P<0.001). However, the multivariate analysis showed that PORT significantly improved DFS (HR 0.81, P = 0.03), consistent with the data after the PSM (median DFS 20.6 and 17.6 months, P = 0.04).

CONCLUSION

PORT significantly improved the DFS and LRFS in patients with N2a2 and significantly improved the DFS, LRFS, and OS in patients with N2b. Patients with N2a1 could not benefit from PORT.

Effect of PD-L1 Expression for the PD-1/L1 Inhibitors on Non-small Cell Lung Cancer: A Meta-analysis Based on Randomised Controlled Trials

AIMS

As PD-L1 expression has been proposed as one of the cancer biomarkers for non-small cell lung cancer (NSCLC), the predictive value of tumour proportional score (TPS) in the effect of immunotherapy [programmed death protein-1/ligand 1 (PD-1/L1) inhibitors] for NSCLC is worth exploring further. Here, we aimed to summarise the outcomes of current NSCLC randomised controlled trials (RCTs) and explore the predictive value of TPS in clinical immunotherapy, including immune checkpoint inhibitors (ICIs) with or without chemotherapy.

MATERIALS AND METHODS

RCTs published by PubMed, Medline, Embase and Scopus before February 2023 comparing immunotherapy (PD-1/L1 with or without other therapy) versus a control group in advanced or metastatic NSCLC were included to assess the prognosis according to the patients' TPS with 1% and 50% as the thresholds. The primary endpoints were overall survival and progression-free survival.

RESULTS

In total, 28 RCTs containing 17 266 participants with advanced or metastatic NSCLC were included in this meta-analysis. Statistical results showed that compared with TPS <1%, ≥1% or within 1-49%, patients with TPS ≥50% benefited more significantly from the immunotherapy. A subgroup analysis showed that when TPS was <1%, ≥1% or within 1-49%, ICIs + chemotherapy had better efficacy than ICIs alone; PD-1 (such as pembrolizumab) inhibitors had better efficacy than PD-L1 inhibitors (such as atezolizumab).

CONCLUSION

The efficacy of immunotherapy (PD-1/L1 inhibitors) for advanced or metastatic NSCLC is influenced by TPS.

Role and Modality of Combining Radiotherapy with Immunotherapy in Stage III-IV Unresectable Small Cell Lung Cancer

PURPOSE/OBJECTIVE(S)

The combination of radiotherapy and immunotherapy was rarely reported in the management of small cell lung cancer (SCLC). We retrospectively assessed the role and modality of this combination in Stage III-IV unresectable SCLC.

MATERIALS/METHODS

Patients with stage III and IV SCLC were enrolled according to AJCC 8th edition. Both efficacy and safety of immunotherapy combined with radiotherapy were evaluated. Thereinto, patients received first-line chemo-immunotherapy and sequential thoracic consolidation radiotherapy (TCRT) were further evaluated. Survival and descriptive analyses were performed.

RESULTS

Between January 1, 2019 and December 31, 2021, 51 patients were included in our analysis. Median follow-up was 28.0 months (95% CI 22.8-33.2). Patients received radiotherapy in treatment course had a prolonged 2-year overall survival (OS). And in the first-line immunotherapy cohort of 27 patients, the addition of TCRT significantly improved 2y-OS (72.22% vs. 13.89%, p = 0.0048), 2y-locoregional recurrence free survival (LRRFS) (90.00% vs 48.00%, p = 0.011), and 2y-distance progression free survival (DPFS) (66.67% vs. 16.67%, p = 0.039). Subgroup analyses showed that TCRT rendered superior outcomes regardless of brain metastases. Dose-escalation (45 Gy/15f) and earlier radiotherapy seemed to improve the benefit. Of 70.37% (19/27) patients experienced disease progression in the TCRT evaluation cohort, 63.16% (12/19) patients failed in brain. A tendency toward better OS and superior brain metastases free survival (BMFS) were observed after receiving prophylactic cranial irradiation (PCI). Finally, the most common grade 2 or higher toxic effects were pneumonitis in all patients (11.76% of immune-related vs. 7.84% of radiation related).

CONCLUSION

Earlier addition of TCRT to immunotherapy could significantly improve survival and extracranial control for stage IIIA-IVB unresectable SCLC patients, with no increased risk of adverse events. In the era of immunotherapy, PCI may still be a recommended strategy. Further investigation is warranted.

Dosimetric Analysis of Proton Beam Therapy vs. Photon Radiotherapy for Cardiac Tumors with or without Deep Inspiratory Breath Holding: A Case Report

PURPOSE/OBJECTIVE(S)

Proton beam therapy (PBT) has been demonstrated to deliver equivalent dosimetric radiation with the benefit of improved sparing of organs at risk (OAR). Deep inspiration breath holding (DIBH) is a commonly used method for reducing the radiation dose to the heart and lungs. However, few studies have ever reported the usage of DIBH combined with proton beam therapy in cardiac tumors. The purpose of this case report is to compare the dosimetric differences between photon radiotherapy and proton radiation therapy (PBT) with or without deep inspiration breath holding.

MATERIALS/METHODS

A 66-year-old female patient with cardiac tumors was recruited, and the prescribed dose of radiotherapy for cardiac tumors was 95%PGTV 50Gy/2.5Gy/20f. Two simulation CT scans were collected during free breath (FB) and DIBH. And the target area was delineated on deep inspiratory breath holding image (DIBH-CT) and free breathing image (FB-CT). The target area of FB-CT was modified by referring to the ten-time phases of 4D-CT. Finally, IMRT, VMAT and PBT plans (DIBH-IMRT, DIBH-VAMT, DIBH-PBT, FB-IMRT, FB-VAMT, FB-PBT) were generated on the above images, and the organs at risk were limited as follows: lungs V20 ≤20%, lungs mean ≤11 Gy, heart V30 ≤40%, coronary artery mean ≤26 Gy, spinal cord ≤30 Gy, and left breast mean ≤5 Gy.

RESULTS

All of the six plans satisfied most of the treatment planning goals. DIBH resulted in a dose reduction in all organs at risk including the heart, lungs, coronary artery (CA), spinal cord and breasts, when compared with FB using IMRT, VMAT, or PBT. Compared with the FB, DIBH provided a significant reduction in the mean dose of coronary artery (CA mean for DIBH-IMRT vs FB-IMRT = 28.32 Gy vs 42.66 Gy, CA mean for DIBH-VMAT vs FB-VAMT = 26.44Gy vs 40.85Gy, CA mean for DIBH-PBT vs FB-PBT = 27.71Gy vs 39.51Gy). Similarly, when compared with IMRT or VMAT in either FB or DIBH, PBT reduced radiation doses for all of the OAR. In comparison, the difference was less significant between IMRT and VMAT technique. Pitmen compared with IMRT and VMAT, reduced significantly the max dose of spinal cord, lungs V5, breast-L/R mean. Totally, DIBH-PBT was observed sufficient dose coverage and better sparing of organs at risk.

CONCLUSION

PBT combined with DIBH technique gained an advantage in the sparing of OAR for cardiac tumors, especially in coronary protection. The possibility of broader application of PBT with DIBH in clinical practice is currently being evaluated and further studies are needed.

Local Causal Discovery in Multiple Manipulated Datasets

We consider the problem of distinguishing direct causes from direct effects of a target variable of interest from multiple manipulated datasets with unknown manipulated variables and nonidentical data distributions. Recent studies have shown that datasets attained from manipulated experiments (i.e., manipulated data) contain richer causal information than observational data for causal structure learning. Thus, in this article, we propose a new algorithm, which makes full use of the interventional properties of a causal model to discover the direct causes and direct effects of a target variable from multiple datasets with different manipulations. It is more suited to real-world cases and is also a challenge to be addressed in this article. First, we apply the backward framework to learn parents and children (PC) of a given target from multiple manipulated datasets. Second, we orient some edges connected to the target in advance through the assumption that the target variable is not manipulated and then orient the remaining undirected edges by finding invariant V-structures from multiple datasets. Third, we analyze the correctness of the proposed algorithm. To the best of our knowledge, the proposed algorithm is the first that can identify the local causal structure of a given target from multiple manipulated datasets with unknown manipulated variables. Experimental results on standard Bayesian networks validate the effectiveness of our algorithm.

The Causes of Death and Conditional Survival for Long-Term Survivors of Thymoma

PURPOSE/OBJECTIVE(S)

Data on the morality cause for long-time survival of thymoma is limited. The previous study hinted that thymoma may be a chronic disease rather than a curable cancer. we performed a large-scale retrospective analysis to assess long-term cause of death in patients with thymoma.

MATERIALS/METHODS

This study reviewed thymoma patients from the Surveillance, Epidemiology, and End Results (SEER) database between January1975 and December 2016.Conditional survival and annual hazard rates was calculated with Kaplan-Meier, and cause-specific mortality was performed using Fine-Gray competing risks analysis.

RESULTS

Of 3105 patients were identified (median [range] age,58 (18-93), years), 1615 (52.0%) were male,1028(33.1%) were 65 years or older and 1360(43.8%)patients was at locally advanced (IIB-III) disease. The 10-year overall survival (OS) and cancer-specific survival (CSS) rates were 55.5% (95% CI, 53.4-57.6%) and 74.4% (95% CI, 72.4-76.3%) respectively. Smoothed hazard showed that the annual overall death hazard of death increased steadily, but the hazard of thymoma-related death began to decline at about 4 years and is exceeded by other causes at death. However, the annual risk of death by thymoma remain about 1-2% at 5-25 years. Similarly, the conditional OS increased slowly with increased survival time however the cancer-specific survival based decreased slowly. The cumulative incidence of the most common causes of death was 23.1% for thymoma, 5.4% for heart of disease, and 3.9% for the second cancer in 10 years, 28.5%,8.3 and 7.0% in 15 years, and 31.8%,11.8% and 10.8% in 25 years. After 5 years of survival, the death of heart was the main cause of non-thymoma death. The 10-years survivors' older patients (≥65 years) or with radiotherapy suffered more heart specific death (adjust P< 0.001, P = 0.015, respectively).

CONCLUSION

The risk of cancer-specific death and other causes of death shift over time for patients with thymoma. The non-cancer cause, especially heart diseases which may be the vital competing cause of death, increased with prolongation of survival time.

Study of the ^{22}Mg Waiting Point Relevant for X-Ray Burst Nucleosynthesis via the ^{22}Mg(α,p)^{25}Al Reaction

The ^{22}Mg(α,p)^{25}Al reaction rate has been identified as a major source of uncertainty for understanding the nucleosynthesis flow in Type-I x-ray bursts. We report a direct measurement of the energy- and angle-integrated cross sections of this reaction in a 3.3-6.9 MeV center-of-mass energy range using the MUlti-Sampling Ionization Chamber (MUSIC). The new ^{22}Mg(α,p)^{25}Al reaction rate is a factor of ∼4 higher than the previous direct measurement of this reaction within temperatures relevant for x-ray bursts, resulting in the ^{22}Mg waiting point of x-ray burst nucleosynthesis flow to be significantly bypassed via the (α,p) reaction.

K -Relations-Based Consensus Clustering With Entropy-Norm Regularizers

Consensus clustering is to find a high quality and robust partition that is in agreement with multiple existing base clusterings. However, its computational cost is often very expensive and the quality of the final clustering is easily affected by uncertain consensus relations between clusters. In order to solve these problems, we develop a new k -type algorithm, called k -relations-based consensus clustering with double entropy-norm regularizers (KRCC-DE). In this algorithm, we build an optimization model to learn a consensus-relation matrix between final and base clusters and employ double entropy-norm regularizers to control the distribution of these consensus relations, which can reduce the impact of the uncertain consensus relations. The proposed algorithm uses an iterative strategy with strict updating formulas to get the optimal solution. Since its computation complexity is linear with the number of objects, base clusters, or final clusters, it can take low computational costs to effectively solve the consensus clustering problem. In experimental analysis, we compared the proposed algorithm with other k -type-based and global-search consensus clustering algorithms on benchmark datasets. The experimental results illustrate that the proposed algorithm can balance the quality of the final clustering and its computational cost well.

[The application value of shear wave dispersion and shear wave elastography combined with serological indicators in the evaluation of liver fibrosis]

Objective: To explore the application value of shear wave dispersion (SWD) and shear wave elastography (SWE) combined with serological indicators in the evaluation of liver fibrosis. Methods: A total of 219 patients with liver disorders who underwent liver biopsy were prospectively collected in Huashan Hospital, Fudan University from January 2021 to September 2022, including 130 males and 89 females, aged from 18 to 76 (42±12) years. All patients underwent SWD and SWE examinations before liver biopsy. Serological indicators including alanine aminotransferase(ALT), aspartate aminotransferase(AST), alkaline phosphatase(ALP)) and γ-glutamyl transpeptadase (GGT) were also collected. Based on pathological diagnosis of liver fibrosis stage (from S0 to S4), the distribution of dispersion slope and liver elastic modulus at different fibrosis stages were analyzed in all patients. All patients were divided 7: 3 into training set (156 cases) and validation set (63 cases) in chronological order. In training set, factors influencing liver fibrosis≥S2 stage and S4 stage were analysed using binary logistic regression. The predictive models were established for diagnosing liver fibrosis≥S2 stage and S4 stage by using R language, and the models were evaluated by the area under curve (AUC) and calibrated for validation. Results: The dispersion slope and elastic modulus increased with the severity of fibrosis, with statistically significant differences in different fibrosis stages (both P<0.001). In training set, dispersion slope, elastic modulus, ALT, AST, and GGT were influential factors in liver fibrosis≥S2 stage and S4 stage(both P<0.05), and prediction models were constructed based on these indicators. In training set, the AUCs of the predictive model, SWD and SWE for diagnosingliver fibrosis≥S2 stage were 0.743 (95%CI: 0.665-0.821), 0.709 (95%CI: 0.628-0.790) and 0.725 (95%CI: 0.647-0.804), respectively; for diagnosing liver fibrosis S4 stage, the AUCs were 0.988 (95%CI: 0.968-1.000), 0.908 (95%CI: 0.852-0.963) and 0.974 (95%CI: 0.945-1.000), respectively. In validation set, the AUC of the predictive model, SWD and SWE for diagnosing liver fibrosis≥S2 stage were 08.735 (95%CI: 0.612-0.859), 0.658 (95%CI:0.522-0.793) and 0.699 (95%CI:0.570-0.828), respectively; for diagnosing liver fibrosis S4 stage, the AUC were 0.976 (95%CI: 0.937-1.000), 0.872 (95%CI: 0.757-0.988) and 0.948 (95%CI: 0.889-1.000), respectively. The calibration curves of the prediction models were consistent in the training and validation sets. Conclusion: The predictive model of SWD and SWE combined with serological indicators is helpful in the diagnosis of stage of liver fibrosis non-invasively.

[Epidemiological characteristics of imported malaria cases after malaria elimination in Yixing City of Jiangsu Province]

OBJECTIVE

To analyse the epidemiological characteristics of imported malaria cases after malaria elimination in Yixing City, Jiangsu Province, so as to provide reference for malaria prevention and control in grassroots healthcare institutions.

METHODS

All data pertaining to malaria cases reported in Yixing City from 2016 to 2022 were retrieved from Chinese Disease Control and Prevention Information System, and the data pertaining to vector monitoring and human malaria parasite infections from 2016 to 2022 were collected for a descriptive statistical analysis.

RESULTS

A total of 14 imported malaria cases were reported in Yixing City from 2016 to 2022, including 12 cases with Plasmodium falciparum malaria, one case with P. vivax malaria and one case with P. ovale malaria, and all cases acquired infections in Africa and then returned to Yixing City. Malaria cases were reported across 2016 to 2022 except in 2020 and 2021. Malaria cases were predominantly reported during the period between December and February of the next year, and workers were the predominant occupation. The institutions where malaria was initially diagnosed included county-level general hospitals, county-level disease prevention and control institutions and grassroots healthcare centers, and there were 10 cases with definitive diagnosis of malaria on the day of initial diagnosis, with a 64.29% (9/14) correct rate of initial diagnosis. There were 5 cases diagnosed with severe malaria, and the standardized response rate was 100.00% following the "1-3-7" surveillance and response strategy. Of all malaria vectors, only Anopheles sinensis was monitored in Yixing City from 2016 to 2022, and all humans were tested negative for blood smears exceptimportedmalariacases.

CONCLUSIONS

The correct rate of initial malaria diagnosis was not high in healthcare institutions in Yixing City from 2016 to 2022, and there are still multiple challenges for prevention of re-establishment of imported malaria.

Adaptive Prototype Interaction Network for Few-Shot Knowledge Graph Completion

Few-shot knowledge graph completion (FKGC), which aims to infer new triples for a relation using only a few reference triples of the relation, has attracted much attention in recent years. Most existing FKGC methods learn a transferable embedding space, where entity pairs belonging to the same relations are close to each other. In real-world knowledge graphs (KGs), however, some relations may involve multiple semantics, and their entity pairs are not always close due to having different meanings. Hence, the existing FKGC methods may yield suboptimal performance when handling multiple semantic relations in the few-shot scenario. To solve this problem, we propose a new method named adaptive prototype interaction network (APINet) for FKGC. Our model consists of two major components: 1) an interaction attention encoder (InterAE) to capture the underlying relational semantics of entity pairs by modeling the interactive information between head and tail entities and 2) an adaptive prototype net (APNet) to generate relation prototypes adaptive to different query triples by extracting query-relevant reference pairs and reducing the data inconsistency between support and query sets. Experimental results on two public datasets demonstrate that APINet outperforms several state-of-the-art FKGC methods. The ablation study demonstrates the rationality and effectiveness of each component of APINet.

Properties of Cosmic-Ray Sulfur and Determination of the Composition of Primary Cosmic-Ray Carbon, Neon, Magnesium, and Sulfur: Ten-Year Results from the Alpha Magnetic Spectrometer

We report the properties of primary cosmic-ray sulfur (S) in the rigidity range 2.15 GV to 3.0 TV based on 0.38×10^{6} sulfur nuclei collected by the Alpha Magnetic Spectrometer experiment (AMS). We observed that above 90 GV the rigidity dependence of the S flux is identical to the rigidity dependence of Ne-Mg-Si fluxes, which is different from the rigidity dependence of the He-C-O-Fe fluxes. We found that, similar to N, Na, and Al cosmic rays, over the entire rigidity range, the traditional primary cosmic rays S, Ne, Mg, and C all have sizeable secondary components, and the S, Ne, and Mg fluxes are well described by the weighted sum of the primary silicon flux and the secondary fluorine flux, and the C flux is well described by the weighted sum of the primary oxygen flux and the secondary boron flux. The primary and secondary contributions of the traditional primary cosmic-ray fluxes of C, Ne, Mg, and S (even Z elements) are distinctly different from the primary and secondary contributions of the N, Na, and Al (odd Z elements) fluxes. The abundance ratio at the source for S/Si is 0.167±0.006, for Ne/Si is 0.833±0.025, for Mg/Si is 0.994±0.029, and for C/O is 0.836±0.025. These values are determined independent of cosmic-ray propagation.

RSS-Bagging: Improving Generalization Through the Fisher Information of Training Data

The bagging method has received much application and attention in recent years due to its good performance and simple framework. It has facilitated the advanced random forest method and accuracy-diversity ensemble theory. Bagging is an ensemble method based on simple random sampling (SRS) method with replacement. However, SRS is the most foundation sampling method in the field of statistics, where exists some other advanced sampling methods for probability density estimation. In imbalanced ensemble learning, down-sampling, over-sampling, and SMOTE methods have been proposed for generating base training set. However, these methods aim at changing the underlying distribution of data rather than simulating it better. The ranked set sampling (RSS) method uses auxiliary information to get more effective samples. The purpose of this article is to propose a bagging ensemble method based on RSS, which uses the ordering of objects related to the class to obtain more effective training sets. To explain its performance, we give a generalization bound of ensemble from the perspective of posterior probability estimation and Fisher information. On the basis of RSS sample having a higher Fisher information than SRS sample, the presented bound theoretically explains the better performance of RSS-Bagging. The experiments on 12 benchmark datasets demonstrate that RSS-Bagging statistically performs better than SRS-Bagging when the base classifiers are multinomial logistic regression (MLR) and support vector machine (SVM).

[Association between vitamin D level and blood eosinophil count in healthy population and patients with chronic obstructive pulmonary disease]

OBJECTIVE

To investigate the prevalence of vitamin D deficiency and its association with blood eosinophil count in healthy population and patients with chronic obstructive pulmonary disease (COPD).

METHODS

We analyzed the data of a total 6163 healthy individuals undergoing routine physical examination in our hospital between October, 2017 and December, 2021, who were divided according to their serum 25(OH)D level into severe vitamin D deficiency group (< 10 ng/mL), deficiency group (< 20 ng/mL), insufficient group (< 30 ng/mL) and normal group (≥30 ng/mL). We also retrospectively collected the data of 67 COPD patients admitted in our department from April and June, 2021, with 67 healthy individuals undergoing physical examination in the same period as the control group. Routine blood test results, body mass index (BMI) and other parameters were obtained from all the subjects, and logistic regression models were used to investigate the association between 25(OH)D levels and eosinophil count.

RESULTS

The overall abnormal rate of 25(OH)D level (< 30 ng/mL) in the healthy individuals was 85.31%, and the rate was significantly higher in women (89.29%) than in men. Serum 25(OH)D levels in June, July, and August were significantly higher than those in December, January, and February. In the healthy individuals, blood eosinophil counts were the lowest in severe 25(OH)D deficiency group, followed by the deficiency group and insufficient group, and were the highest in the normal group (P < 0.05). Multivariable regression analysis showed that an older age, a higher BMI, and elevated vitamin D levels were all risk factors for elevated blood eosinophils in the healthy individuals. The patients with COPD had lower serum 25(OH)D levels than the healthy individuals (19.66±7.87 vs 26.39±9.28 ng/mL) and a significantly higher abnormal rate of serum 25(OH)D (91% vs 71%; P < 0.05). A reduced serum 25(OH)D level was a risk factor for COPD. Blood eosinophils, sex and BMI were not significantly correlated with serum 25(OH)D level in patients with COPD.

CONCLUSION

Vitamin D deficiency is common in both healthy individuals and COPD patients, and the correlations of vitamin D level with sex, BMI and blood eosinophils differ obviously between healthy individuals and COPD patients.

Temporal Structures in Electron Spectra and Charge Sign Effects in Galactic Cosmic Rays

We present the precision measurements of 11 years of daily cosmic electron fluxes in the rigidity interval from 1.00 to 41.9 GV based on 2.0×10^{8} electrons collected with the Alpha Magnetic Spectrometer (AMS) aboard the International Space Station. The electron fluxes exhibit variations on multiple timescales. Recurrent electron flux variations with periods of 27 days, 13.5 days, and 9 days are observed. We find that the electron fluxes show distinctly different time variations from the proton fluxes. Remarkably, a hysteresis between the electron flux and the proton flux is observed with a significance of greater than 6σ at rigidities below 8.5 GV. Furthermore, significant structures in the electron-proton hysteresis are observed corresponding to sharp structures in both fluxes. This continuous daily electron data provide unique input to the understanding of the charge sign dependence of cosmic rays over an 11-year solar cycle.

Self-Constrained Spectral Clustering

As a leading graph clustering technique, spectral clustering is one of the most widely used clustering methods to capture complex clusters in data. Some additional prior information can help it to further reduce the difference between its clustering results and users' expectations. However, it is hard to get the prior information under unsupervised scene to guide the clustering process. To solve this problem, we propose a self-constrained spectral clustering algorithm. In this algorithm, we extend the objective function of spectral clustering by adding pairwise and label self-constrained terms to it. We provide the theoretical analysis to show the roles of the self-constrained terms and the extensibility of the proposed algorithm. Based on the new objective function, we build an optimization model for self-constrained spectral clustering so that we can simultaneously learn the clustering results and constraints. Furthermore, we propose an iterative method to solve the new optimization problem. Compared to other existing versions of spectral clustering algorithms, the new algorithm can discover a high-quality cluster structure of a data set without prior information. Extensive experiments on benchmark data sets illustrate the effectiveness of the proposed algorithm.

Nitrogen Preference of Dominant Species during Hailuogou Glacier Retreat Succession on the Eastern Tibetan Plateau

Plant nitrogen (N) uptake preference is a key factor affecting plant nutrient acquisition, vegetation composition and ecosystem function. However, few studies have investigated the contribution of different N sources to plant N strategies, especially during the process of primary succession of a glacial retreat area. By measuring the natural abundance of N isotopes (δ¹⁵N) of dominant plants and soil, we estimated the relative contribution of different N forms (ammonium-NH₄⁺, nitrate-NO₃^- and soluble organic N-DON) and absorption preferences of nine dominant plants of three stages (12, 40 and 120 years old) of the Hailuogou glacier retreat area. Along with the chronosequence of primary succession, dominant plants preferred to absorb NO₃^- in the early (73.5%) and middle (46.5%) stages. At the late stage, soil NH₄⁺ contributed more than 60.0%, In addition, the contribution of DON to the total N uptake of plants was nearly 19.4%. Thus, the dominant plants' preference for NO₃^- in the first two stages changes to NH₄⁺ in the late stages during primary succession. The contribution of DON to the N source of dominant plants should not be ignored. It suggests that the shift of N uptake preference of dominant plants may reflect the adjustment of their N acquisition strategy, in response to the changes in their physiological traits and soil nutrient conditions. Better knowledge of plant preferences for different N forms could significantly improve our understanding on the potential feedbacks of plant N acquisition strategies to environmental changes, and provide valuable suggestions for the sustainable management of plantations during different successional stages.

Generalization Performance of Pure Accuracy and its Application in Selective Ensemble Learning

The pure accuracy measure is used to eliminate random consistency from the accuracy measure. Biases to both majority and minority classes in the pure accuracy are lower than that in the accuracy measure. In this paper, we demonstrate that compared with the accuracy measure and F-measure, the pure accuracy measure is class distribution insensitive and discriminative for good classifiers. The advantages make the pure accuracy measure suitable for traditional classification. Further, we mainly focus on two points: exploring a tighter generalization bound on pure accuracy based learning paradigm and designing a learning algorithm based on the pure accuracy measure. Particularly, with the self-bounding property, we build an algorithm-independent generalization bound on the pure accuracy measure, which is tighter than the existing bound of an order O(1/√N) (N is the number of instances). The proposed bound is free from making a smoothness or convex assumption on the hypothesis functions. In addition, we design a learning algorithm optimizing the pure accuracy measure and use it in the selective ensemble learning setting. The experiments on sixteen benchmark data sets and four image data sets demonstrate that the proposed method statistically performs better than the other eight representative benchmark algorithms.

Evaluating Classification Model Against Bayes Error Rate

For a classification task, we usually select an appropriate classifier via model selection. How to evaluate whether the chosen classifier is optimal? One can answer this question via Bayes error rate (BER). Unfortunately, estimating BER is a fundamental conundrum. Most existing BER estimators focus on giving the upper and lower bounds of the BER. However, evaluating whether the selected classifier is optimal based on these bounds is hard. In this paper, we aim to learn the exact BER instead of bounds on BER. The core of our method is to transform the BER calculation problem into a noise recognition problem. Specifically, we define a type of noise called Bayes noise and prove that the proportion of Bayes noisy samples in a data set is statistically consistent with the BER of the data set. To recognize the Bayes noisy samples, we present a method consisting of two parts: selecting reliable samples based on percolation theory and then employing a label propagation algorithm to recognize the Bayes noisy samples based on the selected reliable samples. The superiority of the proposed method compared to the existing BER estimators is verified on extensive synthetic, benchmark, and image data sets.

RIB induced reactions: Studying astrophysical reactions with low-energy RI beam at CRIB

Astrophysical reactions involving radioactive isotopes (RI) often play an important role in high-temperature stellar environments. The experimental studies on the reaction rates for those are still limited mainly due to the technical difficulties in producing high-quality RI beams. A direct measurement of those reactions would be still challenging in many cases, however, we can make a reliable evaluation of the reaction rates by an indirect method or by studying the resonance prorerties. Here we ntroduce recent examples of experimental studies on such RI-involving astrophysical reactions, performed at Center for Nuclear Study, the University of Tokyo, using the low-energy RI beam separator CRIB. One is for the neutron-induced destruction reactions of 7Be in the Big-Bang nucleosynthesis, and the other is the study on the 22Mg(α, p) reaction relevant in X-ray bursts, which was performed with the resonant scattering method from the inverse reaction channel.

[The prevalence of atrial cardiomyopathy in patients with different types of acute ischemic stroke and its relationship with cryptogenic stroke]

Objective: To investigate the prevalence of atrial cardiomyopathy in patients with different types of acute ischemic stroke and its relationship with cryptogenic stroke. Methods: Patients with acute ischemic stroke within 7 days of onset who were admitted to the Department of Neurology of the First Affiliated Hospital of Zhengzhou University from January to September 2019 were prospectively and consecutively enrolled. All included patients were classified according to TOAST classification of ischemic stroke. Chi-square test was used to compare the prevalence of atrial cardiomyopathy among patients with different TOAST classifications. Multivariate logistic regression model was used to analyze the related factors of cryptogenic stroke. Results: A total of 1 098 patients with acute ischemic stroke were enrolled in the study, including 661 males and 437 females, with a median age [M(Q₁,Q₃)] of 61 (53, 68) years. The prevalence of atrial cardiomyopathy in patients with cryptogenic stroke[53.5% (46/86)] was higher than that in patients with large artery atherosclerosis [38.0%(63/166), P=0.018] and small vessel occlusion [19.4%(37/191), P<0.001], but was lower than that of patients in the cardioembolic group [97.3% (72/74), P<0.001]. Multivariate logistic regression analysis showed that atrial cardiomyopathy was an associated factor for cryptogenic stroke (OR=2.945, 95%CI: 1.766-4.911, P<0.001). Conclusions: Atrial cardiomyopathy is associated with cryptogenic stroke. The prevalence of atrial cardiomyopathy in patients with cryptogenic stroke is higher than that in patients with large artery atherosclerosis and small vessel occlusion, but lower than that in patients with cardiac embolism.

AF: An Association-Based Fusion Method for Multi-Modal Classification

Multi-modal classification (MMC) aims to integrate the complementary information from different modalities to improve classification performance. Existing MMC methods can be grouped into two categories: traditional methods and deep learning-based methods. The traditional methods often implement fusion in a low-level original space. Besides, they mostly focus on the inter-modal fusion and neglect the intra-modal fusion. Thus, the representation capacity of fused features induced by them is insufficient. The deep learning-based methods implement the fusion in a high-level feature space where the associations among features are considered, while the whole process is implicit and the fused space lacks interpretability. Based on these observations, we propose a novel interpretative association-based fusion method for MMC, named AF. In AF, both the association information and the high-order information extracted from feature space are simultaneously encoded into a new feature space to help to train an MMC model in an explicit manner. Moreover, AF is a general fusion framework, and most existing MMC methods can be embedded into it to improve their performance. Finally, the effectiveness and the generality of AF are validated on 22 datasets, four typically traditional MMC methods adopting best modality, early, late and model fusion strategies and a deep learning-based MMC method.

[Comparison study for the proportion of underdiagnosed zonulopathy in angle closure glaucoma]

Objective: To analyze the proportion and clinical characteristics of underdiagnosed zonulopathy in angle closure glaucoma (ACG) patients and to explore the related risk factors. Methods: Case-control study. Continuous cases of ACG patients who underwent phacoemulsification combined with intraocular lens implantation and goniosynechialysis surgery [ACG group, including acute angle closure glaucoma (AACG) and chronic angle closure glaucoma (CACG)] from November 1, 2020 to October 31, 2021 and age-related cataract patients who underwent phacoemulsification combined with intraocular lens implantation surgery in the same period (control group) were included. The diagnosis of zonulopathy was determined according to the intraoperative signs such as wrinkles of the anterior capsule during continuous circular capsulorhexis. The proportion of zonulopathy, preoperative diagnosis rate of zonulopathy, demographic characteristics, anterior chamber depth (ACD), axis length, difference of ACD in both eyes (ACD of the contralateral eye minus ACD of the operated eye) were compared between the two groups. The related risk factors were explored. The paired t-test (comparison between two groups of normally distributed data), non-parametric test (comparison between two groups of non-normally distributed data), Chi-square test (categorical variables), univariate and multivariate logistic regression analysis were used. Results: There were 104 ACG patients (104 eyes), including 63 AACG patients (63 eyes) and 41 CACG patients (41 eyes), and 117 controls (117 eyes). There was no significant difference in age (P=0.29) and gender (P=0.07) between the two groups. The ACG group had shallower anterior chamber (P<0.001), shorter axial length (P<0.001) and more ACD difference in both eyes (P<0.001). In the ACG group, the proportion of zonulopathy was 46.2% (48/104), which was significantly higher than that (6.0%, 7/117) in the control group (P<0.001). In the control group, only zonular laxity was found, while in the ACG group, besides the predominant zonular laxity (68.8%, 33/48), there was zonular dehiscence (31.3%, 15/48). The eyes with AACG (57.1%, 36/63) had a higher proportion of zonulopathy than those with CACG (29.3%, 12/41) (P=0.006). In the ACG group, only 14 cases (29.8%) were diagnosed preoperatively according to slit lamp examination and/or ultrasound biomicroscopy. The proportion of underdiagnosed zonulopathy was 70.8% in the ACG group (34/48). A smaller ACD was found to be related to the zonulopathy in the ACG group. All AACG cases with an ACD ≤2.0 mm and CACG cases with an ACD ≤1.9 mm had zonulopathy. Multivariate logistic regression showed that the ACD difference in both eyes (P=0.025) and the diagnosis of ACG (AACG vs. cataract, P<0.001; CACG vs. cataract, P=0.023) were independent risk factors associated with zonulopathy. Conclusions: The proportion of underdiagnosed zonulopathy among ACG patients is high. Better preoperative diagnostic methods for zonulopathy are needed. Zonulopathy is common in ACG patients, especially in AACG patients, suggesting that zonulopathy may be related to the pathogenesis of ACG. The shallower the ACD, the riskier the zonulopathy. ACD differences between two eyes and ACG types (including AACG and CACG) were related risk factors of zonulopathy.(This article was published ahead of print on the Online-First Publishing Platform for Excellent Scientific Researches of Chinese Medical Association Publishing House on March 11, 2022).

β-Delayed One and Two Neutron Emission Probabilities Southeast of ^{132}Sn and the Odd-Even Systematics in r-Process Nuclide Abundances

The β-delayed one- and two-neutron emission probabilities (P_{1n} and P_{2n}) of 20 neutron-rich nuclei with N≥82 have been measured at the RIBF facility of the RIKEN Nishina Center. P_{1n} of ^{130,131}Ag, ^{133,134}Cd, ^{135,136}In, and ^{138,139}Sn were determined for the first time, and stringent upper limits were placed on P_{2n} for nearly all cases. β-delayed two-neutron emission (β2n) was unambiguously identified in ^{133}Cd and ^{135,136}In, and their P_{2n} were measured. Weak β2n was also detected from ^{137,138}Sn. Our results highlight the effect of the N=82 and Z=50 shell closures on β-delayed neutron emission probability and provide stringent benchmarks for newly developed macroscopic-microscopic and self-consistent global models with the inclusion of a statistical treatment of neutron and γ emission. The impact of our measurements on r-process nucleosynthesis was studied in a neutron star merger scenario. Our P_{1n} and P_{2n} have a direct impact on the odd-even staggering of the final abundance, improving the agreement between calculated and observed Solar System abundances. The odd isotope fraction of Ba in r-process-enhanced (r-II) stars is also better reproduced using our new data.

Direct Measurement of Resonances in ^{7}Be(α,γ)^{11}C Relevant to νp-Process Nucleosynthesis

We have performed the first direct measurement of two resonances of the ^{7}Be(α,γ)^{11}C reaction with unknown strengths using an intense radioactive ^{7}Be beam and the DRAGON recoil separator. We report on the first measurement of the 1155 and 1110 keV resonance strengths of 1.73±0.25(stat)±0.40(syst)  eV and 125_{-25}^{+27}(stat)±15(syst)  meV, respectively. The present results have reduced the uncertainty in the ^{7}Be(α,γ)^{11}C reaction rate to ∼9.4%-10.7% over T=1.5-3  GK, which is relevant for nucleosynthesis in the neutrino-driven outflows of core-collapse supernovae (νp process). We find no effect of the new, constrained reaction rate on νp-process nucleosynthesis.

Catheter ablation for atrial fibrillation in adult congenital heart disease: an international registry study

Background

Life expectancies for patients with congenital heart disease (CHD) have dramatically increased in recent years, accompanied by a rise in atrial fibrillation (AF) prevalence. Data on AF ablation strategy and outcomes are limited in CHD.

Purpose

We aimed to investigate the characteristics of CHD patients presenting for AF ablation and their outcomes.

Methods

A multicenter, retrospective analysis was performed of CHD patients undergoing AF ablation between 2004 and 2020 at 13 participating centers. The severity of CHD was classified using the 2014 PACES/HRS guidelines. Clinical data were collected including ablation strategy and follow up. One-year procedural success was defined as freedom from AF in the absence of antiarrhythmic drugs (AADs, complete) or including previously failed AADs (partial).

Results

Of 240 patients, 127 (53.4%) had persistent AF, 62.5% were male, and mean age was 55.2±0.9 years. CHD complexity categories included 147 (61.3%) simple, 69 (28.8%) intermediate and 25 (10.4%) severe. The most common CHD type was atrial septal defect (n=78). More complex CHD conditions included transposition of the great arteries (n=14), anomalous pulmonary veins (n=13), tetralogy of Fallot (n=8), cor triatriatum (n=7), single ventricle physiology (n=2), among others. The majority (71.3%) of patients had AF despite at least one AAD. 46 patients (22.1%) had a reduced systemic ventricular ejection fraction &lt;50%, and the mean left atrial diameter was 44.1±0.7 mm. PV isolation (PVI) was performed in 227 patients (94.6%); additional ablation strategies included left atrial linear ablations (25.4%), CFAE (19.2%), and cavotricuspid isthmus ablation (40.8). One-year complete and partial success rates were 45.0% and 20.5%, respectively, with no significant difference in the rate of complete success between complexity groups. Overall, 38 patients (15.8%) required more than one ablation procedure. There were 3 (1.3%) major and 13 (5.4%) minor procedural complications.

Conclusion

AF ablation in this complex population was safe and resulted in AF control in the majority of patients. Future work should address the most appropriate ablation targets in the challenging population.

Funding Acknowledgement

Type of funding sources: None.

Cathelicidin LL-37 improves bone metabolic balance in rats with ovariectomy-induced osteoporosis via the Wnt/beta-catenin pathway

Osteoporosis is a bone disease characterized by low bone mineral density (BMD) and impaired bone microarchitecture due to the abnormal activity of osteoclasts. Cathelicidins are antimicrobial peptides present in the lysosomes of macrophages and polymorphonuclear leukocytes. LL-37, a cathelicidin, induces various biological effects, including modulation of the immune system, angiogenesis, wound healing, cancer growth, as well as inflammation, and bone loss. A previous study reported direct involvement of LL-37 suppressing osteoclastogenesis in humans. Here, we examined the role of LL-37 in the treatment of osteoporosis using an ovariectomy (OVX) rat model. Our results showed that LL-37 significantly reduced bone loss and pathological injury in OVX rats with osteoporosis. Furthermore, we found that LL-37 significantly increased the activity of the Wnt/beta-catenin pathway in OVX rats with osteoporosis, including the increased expression of beta-catenin, Osterix (Osx), and Runt-related transcription factor 2 (Runx2), whereas XAV-939, an inhibitor of the Wnt/beta-catenin pathway, significantly blocked the effects of LL-37 on bone loss and abnormal bone metabolism. Altogether, our findings suggested that LL-37 exerted a protective role in regulating bone loss and abnormal bone metabolism in rats with osteoporosis by activating the Wnt/beta-catenin pathway.

[Impact of multi-leaf collimator positioning accuracy on quality control of volumetric modulation arc therapy plan for cervical cancer treated with Elekta linear accelerator]

OBJECTIVE

To investigate the influence of positioning accuracy of the multi-leaf collimators (MLC) on the passing rate of the plan dose verification for volumetric modulation arc therapy (VMAT) of cervical cancer using an Elekta linear accelerator.

METHODS

The dose distributions were measured using Sun Nuclear's Mapcheck and Arccheck semiconductors matrix before and after MLC calibration in30 cervical cancer patients undergoing VMAT. Dosimetric comparisons were performed with 2D and 3D gamma passing rates of 3%, 3 mm and 2%, and 2 mm. The 3D gamma distribution was reconstructed with respect to the patient's anatomy using 3DVH software to evaluate the possible influence of MLC positioning accuracy.

RESULTS

Before and after MLC calibration, the gamma passing rates of Mapcheck were (88.80±1.81)% and (99.25 ± 0.53)% under 3% and 3 mm standard, respectively, with an average increase of 10.45%. The corresponding gamma passing rates of Arccheck were (87.61±1.98)% and (98.13±0.99)%, respectively, with an average increase of 10.52%. The gamma passing rates of 3DVH were (89.87±2.28)% and (98.3±1.15)%, respectively, with an average increase of 8.43%.

CONCLUSION

The MLC positioning accuracy is one of the main factors influencing dosimetric accuracy of VMAT for cervical cancer. The application of Autocal software facilitates MLC calibration and improves the accuracy and safety of VMAT delivery for cervical cancer.