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1.
In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.  相似文献   
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The coronavirus 2019 pandemic has affected almost every aspect of health care delivery in the United States, and the emergency medicine system has been hit particularly hard while dealing with this public health crisis. In an unprecedented time in our history, medical systems and clinicians have been asked to be creative, flexible, and innovative, all while continuing to uphold the important standards in the US health care system. To continue providing quality services to patients during this extraordinary time, care providers, organizations, administrators, and insurers have needed to alter longstanding models and procedures to respond to the dynamics of a pandemic. The Emergency Medicine Treatment and Active Labor Act of 1986, or EMTALA, is 1 example of where these alterations have allowed health care facilities and clinicians to continue their work of caring for patients while protecting both the patients and the clinicians themselves from infectious exposures at the same time.  相似文献   
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Background

Resistin is an immunometabolic mediator that is elevated in several inflammatory disorders. A ligand for Toll-like receptor 4, resistin modulates the recruitment and activation of myeloid cells, notably neutrophils. Neutrophils are major drivers of cystic fibrosis (CF) lung disease, in part due to the release of human neutrophil elastase- and myeloperoxidase-rich primary granules, leading to tissue damage. Here we assessed the relationship of resistin to CF lung disease.

Methods

Resistin levels were measured in plasma and sputum from three retrospective CF cohorts spanning a wide range of disease. We also assessed the ability of neutrophils to secrete resistin upon activation in vitro. Finally, we constructed a multivariate model assessing the relationship between resistin levels and lung function.

Results

Plasma resistin levels were only marginally higher in CF than in healthy control subjects. By contrast, sputum resistin levels were very high in CF, reaching 50–100 fold higher levels than in plasma. Among CF patients, higher plasma resistin levels were associated with allergic bronchopulmonary aspergillosis, and higher sputum resistin levels were associated with CF-related diabetes. Mechanistically, in vitro release of neutrophil primary granules was concomitant with resistin secretion. Overall, sputum resistin levels were negatively correlated with CF lung function, independently of other variables (age, sex, and genotype).

Conclusions

Our data establish relationships between resistin levels in the plasma and sputum of CF patients that correlate with disease status, and identify resistin as a novel mechanistic link between neutrophilic inflammation and lung disease in CF.  相似文献   
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