Effect of Vascepa (icosapent ethyl) on progression of coronary atherosclerosis in patients with elevated triglycerides (200-499 mg/dL) on statin therapy: Rationale and design of the EVAPORATE study.

Despite reducing progression and promoting regression of coronary atherosclerosis, statin therapy does not fully address residual cardiovascular (CV) risk. High-purity eicosapentaenoic acid (EPA) added to a statin has been shown to reduce CV events and induce regression of coronary atherosclerosis in imaging studies; however, data are from Japanese populations without high triglyceride (TG) levels and baseline EPA serum levels greater than those in North American populations. Icosapent ethyl is a high-purity prescription EPA ethyl ester approved at 4 g/d as an adjunct to diet to reduce TG levels in adults with TG levels >499 mg/dL. The objective of the randomized, double-blind, placebo-controlled EVAPORATE study is to evaluate the effects of icosapent ethyl 4 g/d on atherosclerotic plaque in a North American population of statin-treated patients with coronary atherosclerosis, TG levels of 200 to 499 mg/dL, and low-density lipoprotein cholesterol levels of 40 to 115 mg/dL. The primary endpoint is change in low-attenuation plaque volume measured by multidetector computed tomography angiography. Secondary endpoints include incident plaque rates; quantitative changes in different plaque types and morphology; changes in markers of inflammation, lipids, and lipoproteins; and the relationship between these changes and plaque burden and/or plaque vulnerability. Approximately 80 patients will be followed for 9 to 18 months. The clinical implications of icosapent ethyl 4 g/d treatment added to statin therapy on CV endpoints are being evaluated in the large CV outcomes study REDUCE-IT. EVAPORATE will provide important imaging-derived data that may add relevance to the clinically derived outcomes from REDUCE-IT

Hybridizing two-step growth mixture model and exploratory factor analysis to examine heterogeneity in nonlinear trajectories

Empirical researchers are usually interested in investigating the impacts of baseline covariates have when uncovering sample heterogeneity and separating samples into more homogeneous groups. However, a considerable number of studies in the structural equation modeling (SEM) framework usually start with vague hypotheses in terms of heterogeneity and possible reasons. It suggests that (1) the determination and specification of a proper model with covariates is not straightforward, and (2) the exploration process may be computational intensive given that a model in the SEM framework is usually complicated and the pool of candidate covariates is usually huge in the psychological and educational domain where the SEM framework is widely employed. Following \citet{Bakk2017two}, this article presents a two-step growth mixture model (GMM) that examines the relationship between latent classes of nonlinear trajectories and baseline characteristics. Our simulation studies demonstrate that the proposed model is capable of clustering the nonlinear change patterns, and estimating the parameters of interest unbiasedly, precisely, as well as exhibiting appropriate confidence interval coverage. Considering the pool of candidate covariates is usually huge and highly correlated, this study also proposes implementing exploratory factor analysis (EFA) to reduce the dimension of covariate space. We illustrate how to use the hybrid method, the two-step GMM and EFA, to efficiently explore the heterogeneity of nonlinear trajectories of longitudinal mathematics achievement data.Comment: Draft version 1.6, 08/08/2020. This paper has not been peer reviewed. Please do not copy or cite without author's permissio

Dual Deletion of the Sirtuins SIRT2 and SIRT3 Impacts on Metabolism and Inflammatory Responses of Macrophages and Protects From Endotoxemia.

Sirtuin 2 (SIRT2) and SIRT3 are cytoplasmic and mitochondrial NAD-dependent deacetylases. SIRT2 and SIRT3 target proteins involved in metabolic, proliferation and inflammation pathways and have been implicated in the pathogenesis of neurodegenerative, metabolic and oncologic disorders. Both pro- and anti-inflammatory effects have been attributed to SIRT2 and SIRT3, and single deficiency in SIRT2 or SIRT3 had minor or no impact on antimicrobial innate immune responses. Here, we generated a SIRT2/3 double deficient mouse line to study the interactions between SIRT2 and SIRT3. SIRT2/3 <sup>-/-</sup> mice developed normally and showed subtle alterations of immune cell populations in the bone marrow, thymus, spleen, blood and peritoneal cavity that contained notably more anti-inflammatory B-1a cells and less NK cells. In vitro, SIRT2/3 <sup>-/-</sup> macrophages favored fatty acid oxidation (FAO) over glycolysis and produced increased levels of both proinflammatory and anti-inflammatory cytokines. In line with metabolic adaptation and increased numbers of peritoneal B-1a cells, SIRT2/3 <sup>-/-</sup> mice were robustly protected from endotoxemia. Yet, SIRT2/3 double deficiency did not modify endotoxin tolerance. Overall, these data suggest that sirtuins can act in concert or compensate each other for certain immune functions, a parameter to be considered for drug development. Moreover, inhibitors targeting multiple sirtuins developed for clinical purposes may be useful to treat inflammatory diseases

Impact of the microbial derived short chain fatty acid propionate on host susceptibility to bacterial and fungal infections in vivo.

Short chain fatty acids (SCFAs) produced by intestinal microbes mediate anti-inflammatory effects, but whether they impact on antimicrobial host defenses remains largely unknown. This is of particular concern in light of the attractiveness of developing SCFA-mediated therapies and considering that SCFAs work as inhibitors of histone deacetylases which are known to interfere with host defenses. Here we show that propionate, one of the main SCFAs, dampens the response of innate immune cells to microbial stimulation, inhibiting cytokine and NO production by mouse or human monocytes/macrophages, splenocytes, whole blood and, less efficiently, dendritic cells. In proof of concept studies, propionate neither improved nor worsened morbidity and mortality parameters in models of endotoxemia and infections induced by gram-negative bacteria (Escherichia coli, Klebsiella pneumoniae), gram-positive bacteria (Staphylococcus aureus, Streptococcus pneumoniae) and Candida albicans. Moreover, propionate did not impair the efficacy of passive immunization and natural immunization. Therefore, propionate has no significant impact on host susceptibility to infections and the establishment of protective anti-bacterial responses. These data support the safety of propionate-based therapies, either via direct supplementation or via the diet/microbiota, to treat non-infectious inflammation-related disorders, without increasing the risk of infection

Trained Immunity Confers Broad-Spectrum Protection Against Bacterial Infections.

The innate immune system recalls a challenge to adapt to a secondary challenge, a phenomenon called trained immunity. Training involves cellular metabolic, epigenetic and functional reprogramming, but how broadly trained immunity protects from infections is unknown. For the first time, we addressed whether trained immunity provides protection in a large panel of preclinical models of infections. Mice were trained and subjected to systemic infections, peritonitis, enteritis, and pneumonia induced by Staphylococcus aureus, Listeria monocytogenes, Escherichia coli, Citrobacter rodentium, and Pseudomonas aeruginosa. Bacteria, cytokines, leukocytes, and hematopoietic precursors were quantified in blood, bone marrow, and organs. The role of monocytes/macrophages, granulocytes, and interleukin 1 signaling was investigated using depletion or blocking approaches. Induction of trained immunity protected mice in all preclinical models, including when training and infection were initiated in distant organs. Trained immunity increased bone marrow hematopoietic progenitors, blood Ly6Chigh inflammatory monocytes and granulocytes, and sustained blood antimicrobial responses. Monocytes/macrophages and interleukin 1 signaling were required to protect trained mice from listeriosis. Trained mice were efficiently protected from peritonitis and listeriosis for up to 5 weeks. Trained immunity confers broad-spectrum protection against lethal bacterial infections. These observations support the development of trained immunity-based strategies to improve host defenses

Sirtuin 3 deficiency does not alter host defenses against bacterial and fungal infections.

Sirtuin 3 (SIRT3) is the main mitochondrial deacetylase. SIRT3 regulates cell metabolism and redox homeostasis, and protects from aging and age-associated pathologies. SIRT3 may drive both oncogenic and tumor-suppressive effects. SIRT3 deficiency has been reported to promote chronic inflammation-related disorders, but whether SIRT3 impacts on innate immune responses and host defenses against infections remains essentially unknown. This aspect is of primary importance considering the great interest in developing SIRT3-targeted therapies. Using SIRT3 knockout mice, we show that SIRT3 deficiency does not affect immune cell development and microbial ligand-induced proliferation and cytokine production by splenocytes, macrophages and dendritic cells. Going well along with these observations, SIRT3 deficiency has no major impact on cytokine production, bacterial burden and survival of mice subjected to endotoxemia, Escherichia coli peritonitis, Klebsiella pneumoniae pneumonia, listeriosis and candidiasis of diverse severity. These data suggest that SIRT3 is not critical to fight infections and support the safety of SIRT3-directed therapies based on SIRT3 activators or inhibitors for treating metabolic, oncologic and neurodegenerative diseases without putting patients at risk of infection

Obtaining interpretable parameters from reparameterizing longitudinal models: transformation matrices between growth factors in two parameter-spaces

The linear spline growth model (LSGM), which approximates complex patterns using at least two linear segments, is a popular tool for examining nonlinear change patterns. Among such models, the linear-linear piecewise change pattern is the most straightforward one. An earlier study has proved that other than the intercept and slopes, the knot (or change-point), at which two linear segments join together, can be estimated as a growth factor in a reparameterized longitudinal model in the latent growth curve modeling framework. However, the reparameterized coefficients were no longer directly related to the underlying developmental process and therefore lacked meaningful, substantive interpretation, although they were simple functions of the original parameters. This study proposes transformation matrices between parameters in the original and reparameterized models so that the interpretable coefficients directly related to the underlying change pattern can be derived from reparameterized ones. Additionally, the study extends the existing linear-linear piecewise model to allow for individual measurement occasions, and investigates predictors for the individual-differences in change patterns. We present the proposed methods with simulation studies and a real-world data analysis. Our simulation studies demonstrate that the proposed method can generally provide an unbiased and consistent estimation of model parameters of interest and confidence intervals with satisfactory coverage probabilities. An empirical example using longitudinal mathematics achievement scores shows that the model can estimate the growth factor coefficients and path coefficients directly related to the underlying developmental process, thereby providing meaningful interpretation. For easier implementation, we also provide the corresponding code for the proposed models.Comment: Draft version 1.6, 07/28/2020. This paper has not been peer reviewed. Please do not copy or cite without author's permissio

A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection

Large Language Models (LLMs) have demonstrated great potential for code generation and other software engineering tasks. Vulnerability detection is of crucial importance to maintaining the security, integrity, and trustworthiness of software systems. Precise vulnerability detection requires reasoning about the code, making it a good case study for exploring the limits of LLMs' reasoning capabilities. Although recent work has applied LLMs to vulnerability detection using generic prompting techniques, their full capabilities for this task and the types of errors they make when explaining identified vulnerabilities remain unclear. In this paper, we surveyed eleven LLMs that are state-of-the-art in code generation and commonly used as coding assistants, and evaluated their capabilities for vulnerability detection. We systematically searched for the best-performing prompts, incorporating techniques such as in-context learning and chain-of-thought, and proposed three of our own prompting methods. Our results show that while our prompting methods improved the models' performance, LLMs generally struggled with vulnerability detection. They reported 0.5-0.63 Balanced Accuracy and failed to distinguish between buggy and fixed versions of programs in 76% of cases on average. By comprehensively analyzing and categorizing 287 instances of model reasoning, we found that 57% of LLM responses contained errors, and the models frequently predicted incorrect locations of buggy code and misidentified bug types. LLMs only correctly localized 6 out of 27 bugs in DbgBench, and these 6 bugs were predicted correctly by 70-100% of human participants. These findings suggest that despite their potential for other tasks, LLMs may fail to properly comprehend critical code structures and security-related concepts. Our data and code are available at https://figshare.com/s/78fe02e56e09ec49300b