Interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) mediate the development of numerous inflammatory lung diseases. Since IL-1β is typically activated in situations where TNF-α is produced, it was hypothesized that IL-1β alters TNF-α-induced proinflammatory epithelial cell function by altering TNF receptor shedding and surface abundance. In this study, the impact of IL-1β on TNF-α-mediated chemokine production as well as TNF receptor surface expression and shedding were investigated from mouse pulmonary epithelial cells (MLE-15). Interleukin-1β rapidly and persistently enhanced soluble and surface TNFR2. These effects were dependent on TNFR1 expression. TNFR2 small-interfering RNA (siRNA) shifted IL-1β responses, significantly increasing surface and shed TNFR1 implying IL-1β selectively modifies TNF receptors depending on cellular receptor composition. mRNA expression of both receptors was unaltered by IL-1β up to 24 h or in combination with TNF-α indicating effects were post- transcriptional. Interleukin-1β pretreatment enhanced TNF-α-induced macrophage inflammatory protein (MIP)-2 and KC mRNA expression as well as MIP-2 and KC protein levels at the same time point analyzed. Experiments utilizing siRNA against the TNF receptors and a TNFR1 neutralizing antibody demonstrated TNF-α induced MIP-2 through TNFR1 whereas both receptors may have contributed to KC production. These data suggest IL-1β modulates TNF-α–mediated inflammatory lung diseases by enhancing epithelial cell TNF receptor surface expression.
We determined the role of interleukin-1β (IL-1β) signaling on tumor necrosis factor alpha-induced (TNF-α) lung neutrophil influx as well as neutrophil chemoattractant macrophage inflammatory protein (MIP-2) and KC and soluble TNF-α receptor (TNFR) levels utilizing wildtype (WT), TNF receptor double knockout (TNFR1/TNFR2 KO), and IL-1β KO mice after oropharyngeal instillation with TNF-α. A significant increase in neutrophil accumulation in bronchoalveolar lavage fluid (BALF) and lung interstitium was detected in the WT mice six hours after TNF-α exposure. This correlated with an increase in BALF MIP-2. In contrast, BALF neutrophil numbers were not increased by TNF-α treatment of IL-1β KOs, correlating with a failure to induce BALF MIP-2 and a trend toward increased BALF soluble TNFR1. TNF-α-instillation increased lavage and serum KC and soluble TNFR2 irrespective of IL-1β expression. These results suggest IL-1β contributes, in part, to TNF-α-mediated, chemokine release, and neutrophil recruitment to the lung, potentially associated with altered soluble TNFR1 release into the BALF.
Life insurance provides trillions of dollars of financial security for hundreds of millions of individuals and families worldwide. To simultaneously offer affordable products while managing this financial ecosystem, life-insurance companies use an underwriting process to assess the mortality risk posed by individual applicants. Traditional underwriting is largely based on examining an applicant’s health and behavioral profile. This manual process is incompatible with expectations of a rapid customer experience through digital capabilities. Fortunately, the availability of large historical data sets and the emergence of new data sources provide an unprecedented opportunity for artificial intelligence to transform underwriting in the life-insurance industry with standard measures of mortality risk. We combined one of the largest application data sets in the industry with a responsible artificial intelligence framework to develop a mortality model and life score. We describe how the life score serves as the primary risk-driving engine of deployed algorithmic underwriting systems and demonstrate its high level of accuracy, yielding a nine-percent reduction in claims within the healthiest pool of applicants. Additionally, we argue that, by embracing transparency, the industry can build consumer trust and respond to a dynamic regulatory environment focused on algorithmic decision-making. We present a consumer-facing tool that uses a state-of-the-art method for interpretable machine learning to offer transparency into the life score.
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