Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement

AIMS The coronavirus disease 2019 (COVID-19) has spread rapidly around the globe, causing significant morbidity and mortality. This study aims to describe electrocardiographic (ECG) characteristics of COVID-19 patients and to identify ECG parameters that are associated with cardiac involvement. METHODS AND RESULTS The study included patients who were hospitalized with COVID-19 diagnosis and had cardiac biomarker assessments and simultaneous 12-lead surface ECGs. Sixty-three hospitalized patients (median 53 [inter-quartile range, 43-65] years, 76.2% male) were enrolled, including patients with (n = 23) and without (n = 40) cardiac injury. Patients with cardiac injury were older, had more pre-existing co-morbidities, and had higher mortality than those without cardiac injury. They also had prolonged QTc intervals and more T wave changes. Logistic regression model identified that the number of abnormal T waves (odds ratio (OR), 2.36 [95% confidence interval (CI), 1.38-4.04], P = 0.002) and QTc interval (OR, 1.31 [95% CI, 1.03-1.66], P = 0.027) were independent indicators for cardiac injury. The combination model of these two parameters along with age could well discriminate cardiac injury (area the under curve 0.881, P < 0.001) by receiver operating characteristic analysis. Cox regression model identified that the presence of T wave changes was an independent predictor of mortality (hazard ratio, 3.57 [1.40, 9.11], P = 0.008) after adjustment for age. CONCLUSIONS In COVID-19 patients, presence of cardiac injury at admission is associated with poor clinical outcomes. Repolarization abnormalities on surface ECG such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in further risk stratification

Unveiling the operation mechanism of layered perovskite solar cells

Layered perovskites have been shown to improve the stability of perovskite solar cells while its operation mechanism remains unclear. Here we investigate the process for the conversion of light to electrical current in high performance layered perovskite solar cells by examining its real morphology. The layered perovskite films in this study are found to be a mixture of layered and three dimensional (3D)-like phases with phase separations at micrometer and nanometer scale in both vertical and lateral directions. This phase separation is explained by the surface initiated crystallization process and the competition of the crystallization between 3D-like and layered perovskites. We further propose that the working mechanisms of the layered perovskite solar cells involve energy transfer from layered to 3D-like perovskite network. The impact of morphology on efficiency and stability of the hot-cast layered perovskite solar cells are also discussed to provide guidelines for the future improvement

Glycyrrhizic acid: a natural plant ingredient as a drug candidate to treat COVID-19

The total number of cumulative cases and deaths from the COVID-19 pandemic caused by SARS-CoV-2 is still increasing worldwide. Although many countries have actively implemented vaccination strategies to curb the epidemic, there is no specific efficient therapeutic drug for this virus to effectively reduce deaths. Therefore, the underappreciated macromolecular compounds have become the spotlight of research. Furthermore, the medicinal compounds in plants that provide myriad possibilities to treat human diseases have become of utmost importance. Experience indicates that Traditional Chinese medicine effectively treats SARS and has been used for treating patients with COVID-19 in China. As one of the world’s oldest herbal remedies, licorice is used for treating patients with all stages of COVID-19. Glycyrrhizic acid (GA), the main active compound in licorice, has been proven effective in killing the SARS virus. Meanwhile, as a natural plant molecule, GA can also directly target important protein structures of the SARS-CoV-2 virus and inhibit the replication of SARS-CoV-2. In this review, we summarized the immune synergy of GA and its potential role in treating COVID-19 complications. Besides, we reviewed its anti-inflammatory effects on the immune system and its positive effects in cooperation with various drugs to fight against COVID-19 and its comorbidities. The purpose of this review is to elucidate and suggest that GA can be used as a potential drug during COVID-19 treatment

Observational and Genetic Associations of Modifiable Risk Factors with Aortic Valve Stenosis: A Prospective Cohort Study of 0.5 Million Participants

Background: Observational studies have shown that modifiable risk factors are associated with aortic valve stenosis (AVS). However, the causality behind these associations remains largely unknown. Objectives: To explore the associations of modifiable risk factors, including metabolic factors, biochemical measures, education, and lifestyles with AVS and their potential causal associations. Methods: We enrolled 361,930 British white people with genetic data in the UK biobank. Cox proportional risk regression models were used to estimate the hazard ratios between 28 modifiable risk factors and AVS. We used genetic instruments for modifiable risk factors to determine the potential causal relationships using a one-sample Mendelian randomization (MR) approach. Results: A total of 1602 participants developed AVS during an 8.4-year follow-up. Observational analyses showed higher adiposity, blood pressure, heart rate, low-density lipoprotein, urate, C-reactive protein, creatinine, albumin, and glycated hemoglobin, but lower serum vitamin D, and education, unhealthy lifestyle, and poor sleep quality were related to a higher risk of AVS after adjusting for the Bonferroni correction (p &lt; 0.0013). Genetically predicted 1-SD higher levels of body mass index [HR: 1.09, 95% CI: 1.03 to 1.16], body fat percentage (1.17, 1.03 to 1.33), triglyceride (TG) [1.08, 1.00 to 1.16], low-density lipoprotein (LDL) (1.15, 1.08 to 1.21) and serum total cholesterol (TC) (1.13, 1.02 to 1.25) were associated with a higher risk of AVS, respectively. Genetically determined per category higher insomnia (1.32, 1.13 to 1.55) was also associated with AVS. The abovementioned genetic associations with the incident AVS showed an increasing relationship pattern. Conclusions: This study provides strong evidence for the potential causal roles of cardiometabolic factors in developing AVS, highlighting that an idea of metabolic status through a healthy lifestyle may help prevent AVS

Associations of daily sedentary behavior, physical activity, and sleep with irritable bowel syndrome: A prospective analysis of 362,193 participants

Background: Irritable bowel syndrome (IBS) substantially affects quality of life and requires early prevention. This study aimed to elucidate the relationships between IBS and daily behaviors, including sedentary behavior (SB), physical activity (PA), and sleep. In particular, it seeks to identify healthy behaviors to reduce IBS risk, which previous studies have rarely addressed. Methods: Daily behaviors were retrieved from self-reported data of 362,193 eligible UK Biobank participants. Incident cases were determined by self-report or health care data according to Rome IV criteria. Results: A total of 345,388 participants were IBS-free at baseline, during a median follow-up of 8.45 years, 19,885 incident IBS cases were recorded. When examined individually, SB and shorter (≤7 h/day) or longer (>7 h/day) sleep duration were each positively associated with increased IBS risk, and PA was associated with lower IBS risk. The isotemporal substitution model suggested that replacing SB with other activities could provide further protective effects against IBS risk. Among people sleeping ≤7 h/day, replacing 1 h of SB with equivalent light PA, vigorous PA, or sleep was associated with 8.1% (95% confidence interval (95%CI): 0.901–0.937), 5.8% (95%CI: 0.896–0.991), and 9.2% (95%CI: 0.885–0.932) reduced IBS risk, respectively. For people sleeping >7 h/day, light and vigorous PA were associated with a 4.8% (95%CI: 0.926–0.978) and a 12.0% (95%CI: 0.815–0.949) lower IBS risk, respectively. These benefits were mostly independent of genetic risk for IBS. Conclusion: SB and unhealthy sleep duration are risk factors for IBS. A promising way to mitigate IBS risk for individuals sleeping ≤7 h/day and for those sleeping >7 h/day appears to be by replacing SB with adequate sleep or vigorous PA, respectively, regardless of the genetic predisposition of IBS

Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation

Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage the available labels as prior knowledge, have achieved superior performances than unsupervised methods. In practice, people usually need to identify anomalies on new (sub)graphs to secure their business, but they may lack labels to train an effective detection model. One natural idea is to directly adopt a trained GAD model to the new (sub)graph for testing. However, we find that existing semi-supervised GAD methods suffer from poor generalization issue, i.e., well-trained models could not perform well on an unseen area (i.e., not accessible in training) of the same graph. It may cause great troubles. In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers. Nevertheless, it is a challenging task since only limited labels are available, and the normal background may differ between training and testing data. Accordingly, we propose a data augmentation method named \textit{AugAN} (\uline{Aug}mentation for \uline{A}nomaly and \uline{N}ormal distributions) to enrich training data and boost the generalizability of GAD models. Experiments verify the effectiveness of our method in improving model generalizability.Comment: The updated version is accepted by TKDE 2023. Please refer to arXiv:2306.10534v

Towards Personalized Cold-Start Recommendation with Prompts

Recommender systems play a crucial role in helping users discover information that aligns with their interests based on their past behaviors. However, developing personalized recommendation systems becomes challenging when historical records of user-item interactions are unavailable, leading to what is known as the system cold-start recommendation problem. This issue is particularly prominent in start-up businesses or platforms with insufficient user engagement history. Previous studies focus on user or item cold-start scenarios, where systems could make recommendations for new users or items but are still trained with historical user-item interactions in the same domain, which cannot solve our problem. To bridge the gap, our research introduces an innovative and effective approach, capitalizing on the capabilities of pre-trained language models. We transform the recommendation process into sentiment analysis of natural languages containing information of user profiles and item attributes, where the sentiment polarity is predicted with prompt learning. By harnessing the extensive knowledge housed within language models, the prediction can be made without historical user-item interaction records. A benchmark is also introduced to evaluate the proposed method under the cold-start setting, and the results demonstrate the effectiveness of our method. To the best of our knowledge, this is the first study to tackle the system cold-start recommendation problem. The benchmark and implementation of the method are available at https://github.com/JacksonWuxs/PromptRec.Comment: 8 pages, 2 figure

Dietary-Derived Essential Nutrients and Amyotrophic Lateral Sclerosis: A Two-Sample Mendelian Randomization Study

Previous studies have suggested a close but inconsistent relationship between essential nutrients and the risk of amyotrophic lateral sclerosis (ALS), and whether this association is causal remains unknown. We aimed to investigate the potential causal relation between essential nutrients (essential amino acids, essential fatty acids, essential minerals, and essential vitamins) and the risk of ALS using Mendelian randomization (MR) analysis. Large-scale European-based genome-wide association studies&rsquo; (GWASs) summary data related to ALS (assembling 27,205 ALS patients and 110,881 controls) and essential nutrient concentrations were separately obtained. MR analysis was performed using the inverse variance&ndash;weighted (IVW) method, and sensitivity analysis was conducted by the weighted median method, simple median method, MR&ndash;Egger method and MR&ndash;PRESSO method. We found a causal association between genetically predicted linoleic acid (LA) and the risk of ALS (OR: 1.066; 95% CI: 1.011&ndash;1.125; p = 0.019). An inverse association with ALS risk was noted for vitamin D (OR: 0.899; 95% CI: 0.819&ndash;0.987; p = 0.025) and for vitamin E (OR: 0.461; 95% CI: 0.340&ndash;0.626; p = 6.25 &times; 10&minus;7). The sensitivity analyses illustrated similar trends. No causal effect was observed between essential amino acids and minerals on ALS. Our study profiled the effects of diet-derived circulating nutrients on the risk of ALS and demonstrated that vitamin D and vitamin E are protective against the risk of ALS, and LA is a suggested risk factor for ALS

Accelerated biological aging and risk of depression and anxiety: evidence from 424,299 UK Biobank participants

Abstract Theory predicts that biological processes of aging may contribute to poor mental health in late life. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. At baseline, participants who were biologically older more often experienced depression/anxiety. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation [SD] of KDM-BA acceleration, 95% confidence intervals [CI]: 3.3%–8.5%; 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%–13.0%). Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention