Safety and efficacy of ETC-1002 in hypercholesterolaemic patients: a meta-analysis of randomised controlled trials

Background: Due to the myopathic adverse events of statins, safer alternatives are being studied. Bempedoic acid (ETC-1002) is a novel low-density lipoprotein cholesterol (LDL-C)-lowering agent, currently under trial in hypercholesterolaemic patients. Aims: To investigate the tolerability and efficacy of ETC-1002 in hypercholesterolaemic patients through a systematic review of published randomised controlled trials (RCTs). Methods: Five databases were searched for RCTs that investigated the safety and efficacy of ETC-1002 in hypercholesterol­aemic patients. The retrieved search results were screened, and then data were extracted and analysed (as mean difference [MD] or odds ratio [OR]) using the RevMan software. Results: Five RCTs (625 hypercholesterolaemic patients) were identified. ETC-1002 was superior to placebo in terms of percent­age changes from baseline in serum levels of LDL-C (MD –26.58, 95% confidence interval [CI] –35.50 to –17.66, p < 0.0001), non–high-density lipoprotein cholesterol (MD –21.54, 95% CI –28.48 to –14.6, p < 0.00001), and apolipoprotein-B (MD –15.97, 95% CI –19.36 to –12.57, p < 0.0001). When compared to ezetimibe, ETC-1002 was superior in reducing LDL-C (–30.1 ± 1.3 vs. –21.1 ± 1.3). Regarding safety, ETC-1002 did not increase the risk of all adverse events (OR 0.58, 95% CI 0.37–0.91, p = 0.02) and arthralgia (OR 0.32, 95% CI 0.13–0.81, p = 0.02) compared to placebo. All other adverse events including myalgia, headache, and urinary tract infections were similar between ETC-1002 and placebo groups. The evidence certainty in the assessed outcomes was moderate to high except for lipoprotein(a), free fatty acids, and very low-density lipoprotein particle number (very low certainty). Conclusions: ETC-1002 is a safe and effective lipid-lowering agent and may be a suitable alternative in statin-intolerant pa­tients. Well-designed studies are needed to explore the long-term safety and efficacy of ETC-1002 in these patients

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler

Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a centralized data facility. Nonetheless, because of the disparity of computing resources among different clients (i.e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients. Similarly, asynchronous federated learning algorithms experience degradation in the convergence rate and final model accuracy on non-identically and independently distributed (non-IID) heterogeneous datasets due to stale local models and client drift. To address these limitations in cross-silo federated learning with heterogeneous clients and data, we propose FedCompass, an innovative semi-asynchronous federated learning algorithm with a computing power aware scheduler on the server side, which adaptively assigns varying amounts of training tasks to different clients using the knowledge of the computing power of individual clients. FedCompass ensures that multiple locally trained models from clients are received almost simultaneously as a group for aggregation, effectively reducing the staleness of local models. At the same time, the overall training process remains asynchronous, eliminating prolonged waiting periods from straggler clients. Using diverse non-IID heterogeneous distributed datasets, we demonstrate that FedCompass achieves faster convergence and higher accuracy than other asynchronous algorithms while remaining more efficient than synchronous algorithms when performing federated learning on heterogeneous clients

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://appflx.lin

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction  = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

APPFL: Advanced Privacy-Preserving Federated Learning

<h2>What's Changed</h2> <p>In summary, this is a minor release mainly with the focus on resolving the problem that <code>appfl.comm</code> is not in the current released version, together with few other minor changes with details below</p> <ul> <li>Rename the package from Argonne Privacy-Preserving Federated Learning to <strong><em>Advanced</em></strong> Privacy-Preserving Federated Learning #154</li> <li>Resolve the device error when using DP on GPU #150</li> <li>Resolve <code>appfl.comm ModuleNotFoundError</code> #156</li> </ul>If you use this software, please cite it using the metadata from this file

Associations of short‐ and long‐term mortality with admission blood pressure in Chinese patients with different heart failure subtypes

Abstract It remains unknown whether systolic (SBP) and diastolic (DBP) pressure on admission are associated with short‐ and long‐term mortality in Chinese patients with heart failure with preserved (HFpEF), mildly reduced (HFmrEF), and reduced (HFrEF) ejection fraction. In 2706 HF patients (39.1% women; mean age, 68.8 years), we assessed the risk of 30‐day, 1‐year, and long‐term (> 1 year) mortality with 1‐SD increment in SBP and DBP, using multivariable logistic and Cox regression, respectively. During a median follow‐up of 4.1 years, 1341 patients died. The 30‐day, 1‐year, and long‐term mortality were 3.5%, 16.7%, and 39.4%, respectively. In multivariable‐adjusted analyses additionally accounted for DBP or SBP, a higher SBP conferred a higher risk of long‐term mortality (hazard ratio, 1.11; 95% CI, 1.02‐1.22; p = .017) and a lower DBP was associated with a higher risk of all types of mortality (p ≤ .011) in all HF patients. Independent of potential confounders including DBP or SBP, in patients with HFpEF, higher SBP and lower DBP levels predicted a higher risk of long‐term mortality with hazard ratios amounting to 1.16 (95% CI, 1.04–1.29; p = .007) and .89 (95% CI, .80–.99; p = .028), respectively. In patients with HFmrEF and HFrEF, irrespective of adjustments of potential confounders, DBP was associated with 1‐year mortality with odds ratios ranging from .49 to .62 (p ≤ .006). In conclusion, lower DBP and higher SBP levels on admission were associated with a higher risk of different types of all‐cause mortality in Chinese patients with different HF subtypes. Our observations highlight that admission BP may help to improve risk stratification

APPFL: Argonne Privacy-Preserving Federated Learning

<h2>What's Changed</h2> <p>In summary, this is a release of a major change of the APPFL repository with the refactor of the codebase and the addition of several new capabilities. In details:</p> <ul> <li>Add examples on CELEBA and FEMNIST datasets with a new MPI communicator for large models by @yim0331</li> <li>Add asynchronous FL algorithms <a href="https://arxiv.org/pdf/1903.03934.pdf">FedAsync</a>, <a href="https://proceedings.mlr.press/v151/nguyen22b/nguyen22b.pdf">FedBuffer</a>, and <a href="https://arxiv.org/pdf/2309.14675.pdf">FedCompass</a> by @Zilinghan and @ShellyRiver</li> <li>Add example for personalized FL by @shourya01</li> <li>Add globus compute (formerly funcX) as a communicator by @Zilinghan and @hthieu166</li> <li>Allow use to use custom loss and custom evaluation metric in the FL experiments by @Zilinghan</li> </ul>If you use this software, please cite it using the metadata from this file