Efficacy of COVID-19 vaccines in patients taking immunosuppressants.

OBJECTIVES: We intended to assess the effectiveness of all three US Food and Drug Administration approved COVID-19 vaccines at preventing SARS-CoV-2 infection and COVID-19 hospitalisation in a large cohort of individuals on immunosuppressants for a diverse range of conditions. METHODS: We studied the effectiveness of BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna) and Ad26.COV2.S (Johnson & Johnson-Janssen) vaccines among individuals who take immunosuppressants (including disease-modifying antirheumatic drugs and glucocorticoids) by comparing vaccinated (n=97688) and unvaccinated (n=42094) individuals in the Michigan Medicine healthcare system from 1 January to 7 December 2021, using Cox proportional hazards modelling with time-varying covariates. RESULTS: Among vaccinated and unvaccinated individuals, taking immunosuppressants increased the risk of SARS-CoV-2 infection (adjusted HR (aHR)=2.17, 95% CI 1.69 to 2.79 for fully vaccinated and aHR=1.40, 95% CI 1.07 to 1.83 for unvaccinated). Among individuals taking immunosuppressants, we found: (1) vaccination reduced the risk of SARS-CoV-2 infection (aHR=0.55, 95% CI 0.39 to 0.78); (2) the BNT162b2 and mRNA-1273 vaccines were highly effective at reducing the risk of SARS-CoV-2 infection (n=2046, aHR=0.59, 95% CI 0.38 to 0.91 for BNT162b2; n=2064, aHR=0.52, 95% CI 0.33 to 0.82 for mRNA-1273); (3) with a smaller sample size (n=173), Ad26.COV2.S vaccine protection did not reach statistical significance (aHR=0.34, 95% CI 0.09 to 1.30, p=0.17); and (4) receiving a booster dose reduced the risk of SARS-CoV-2 infection (aHR=0.42, 95% CI 0.24 to 0.76). CONCLUSIONS: The mRNA-1273 and BNT162b2 vaccines are effective in individuals who take immunosuppressants. However, individuals who are vaccinated but on immunosuppressants are still at higher risk of SARS-CoV-2 infection and COVID-19 hospitalisation than the broader vaccinated population. Booster doses are effective and crucially important for individuals on immunosuppressants.

Regional sex differences in neurochemical profiles of healthy mice measured by magnetic resonance spectroscopy at 9.4 tesla.

Objective: To determine sex differences in the neurochemical concentrations measured by in vivo proton magnetic resonance spectroscopy (1H MRS) of healthy mice on a genetic background commonly used for neurodegenerative disease models. Methods: 1H MRS data collected from wild type mice with C57BL/6 or related genetic backgrounds in seven prior studies were used in this retrospective analysis. To be included, data had to be collected at 9.4 tesla magnetic field using advanced 1H MRS protocols, with isoflurane anesthesia and similar animal handling protocols, and a similar number of datasets from male and female mice had to be available for the brain regions analyzed. Overall, 155 spectra from female mice and 166 spectra from male mice (321 in total), collected from six brain regions (brainstem, cerebellum, cortex, hippocampus, hypothalamus, and striatum) at various ages were included. Results: Concentrations of taurine, total creatine (creatine + phosphocreatine), ascorbate, glucose and glutamate were consistently higher in male vs. female mice in most brain regions. Striatum was an exception with similar total creatine in male and female mice. The sex difference pattern in the hypothalamus was notably different from other regions. Interaction between sex and age was significant for total creatine and taurine in the cerebellum and hippocampus. Conclusion: Sex differences in regional neurochemical levels are small but significant and age-dependent, with consistent male-female differences across most brain regions. The neuroendocrine region hypothalamus displays a different pattern of sex differences in neurochemical levels. Differences in energy metabolism and cellular density may underlie the differences, with higher metabolic rates in females and higher osmoregulatory and antioxidant capacity in males.

FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models.

Graph mining is an essential component of recommender systems and search engines. Outputs of graph mining models typically provide a ranked list sorted by each item's relevance or utility. However, recent research has identified issues of algorithmic bias in such models, and new graph mining algorithms have been proposed to correct for bias. As such, algorithm developers need tools that can help them uncover potential biases in their models while also exploring the impacts of correcting for biases when employing fairness-aware algorithms. In this paper, we present FairRankVis, a visual analytics framework designed to enable the exploration of multi-class bias in graph mining algorithms. We support both group and individual fairness levels of comparison. Our framework is designed to enable model developers to compare multi-class fairness between algorithms (for example, comparing PageRank with a debiased PageRank algorithm) to assess the impacts of algorithmic debiasing with respect to group and individual fairness. We demonstrate our framework through two usage scenarios inspecting algorithmic fairness.

Auditing the Sensitivity of Graph-based Ranking with Visual Analytics.

Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall we recommend to a given user on an e-commerce platform? The answers to such questions are typically returned in the form of a ranked list, and graph-based ranking methods are widely used in industrial information retrieval settings. However, these ranking algorithms have a variety of sensitivities, and even small changes in rank can lead to vast reductions in product sales and page hits. As such, there is a need for tools and methods that can help model developers and analysts explore the sensitivities of graph ranking algorithms with respect to perturbations within the graph structure. In this paper, we present a visual analytics framework for explaining and exploring the sensitivity of any graph-based ranking algorithm by performing perturbation-based what-if analysis. We demonstrate our framework through three case studies inspecting the sensitivity of two classic graph-based ranking algorithms (PageRank and HITS) as applied to rankings in political news media and social networks.

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics.

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks. As the deployment of artificial intelligence technologies becomes ubiquitous, it is unsurprising that adversaries have begun developing methods to manipulate machine learning models to their advantage. While the visual analytics community has developed methods for opening the black box of machine learning models, little work has focused on helping the user understand their model vulnerabilities in the context of adversarial attacks. In this paper, we present a visual analytics framework for explaining and exploring model vulnerabilities to adversarial attacks. Our framework employs a multi-faceted visualization scheme designed to support the analysis of data poisoning attacks from the perspective of models, data instances, features, and local structures. We demonstrate our framework through two case studies on binary classifiers and illustrate model vulnerabilities with respect to varying attack strategies.