The liver is believed to promote tolerance, which may be beneficial due to its constant exposure to foreign Ags from the portal circulation. Although dendritic cells (DCs) are critical mediators of immune responses, little is known about human liver DCs. We compared freshly purified liver DCs from surgical specimens with autologous blood DCs. Liver and blood DCs were equally immature, but had distinct subset compositions. BDCA-1+ DCs represented the most prevalent liver DC subset, whereas the majority of peripheral blood DCs were CD16+. Upon TLR4 ligation, blood DCs secreted multiple proinflammatory cytokines, whereas liver DCs produced substantial amounts of IL-10. Liver DCs induced less proliferation of allogeneic T cells both in a primary MLR and after restimulation. Similarly, Ag-specific CD4+ T cells were less responsive to restimulation when initially stimulated by autologous liver DCs rather than blood DCs. In addition, liver DCs generated more suppressive CD4+ CD25+FoxP3+ T regulatory cells and IL-4-producing Th2 cells via an IL-10-dependent mechanism. Our findings are critical to understanding hepatic immunity and demonstrate that human liver DCs promote immunologic hyporesponsiveness that may contribute to hepatic tolerance.

Safe performance limits of soldiers and athletes have typically relied on predictive work-rest models of ambient conditions, average work intensity, and characteristics of the population. Bioengineering advances in noninvasive sensor technologies, including miniaturization, reduced cost, power requirements, and comfort, now make it possible to produce individual predictions of safe thermal-work limits. These precision medicine assessments depend on the development of thoughtful algorithms based on physics and physiology. Both physiological telemetry and thermal-strain indexes have been available for >50 years, but greater computing power and better wearable sensors now make it possible to provide actionable information at the individual level. Core temperature can be practically estimated from time series heart rate data and, using an adaptive physiological strain index, provides meaningful predictions of safe work limits that cannot be predicted from only core temperature or heart rate measurements. Early adopters of this technology include specialized occupations where individuals operate in complete encapsulation such as chemical protective suits. Emerging technologies that focus on heat flux measurements at the skin show even greater potential for estimating thermal-work strain using a parsimonious sensor set. Applications of these wearable technologies include many sports and military training venues where inexperienced individuals can learn effective work pacing strategies and train to safe personal limits. The same strategies can also provide a technologically based performance edge for experienced workers and athletes faced with novel and nonintuitive physiological challenges, such as health care providers in full protective clothing treating Ebola patients in West Africa in 2014. NEW & NOTEWORTHY This mini-review details how the application of computational techniques borrowed from signal processing and control theory can provide meaningful advances for the applied physiological problem of real-time thermal-work strain monitoring. The work examines the development of practical core body temperature estimation techniques and how these can be used in combination with current and updated thermal-work strain indexes to provide objective state assessments and to optimize work rest schedules for a given task.

The physiological burden created by heat strain and physical exercise, also called thermal-work strain, was quantified for 10 male Marines (age 21.9 ± 2.3 years, height 180.3 ± 5.2 cm, and weight 85.2 ± 10.8 kg) during three dismounted missions in Helmand Province, Afghanistan. Heart rate (HR) and core body temperature (T core) were recorded every 15 seconds (Equivital EQ-01; Hidalgo, Cambridge, United Kingdom) during periods of light, moderate, and heavy work and used to estimate metabolic rate. Meteorological measures, clothing characteristics, anthropometrics, and estimated metabolic rates were used to predict T core for the same missions during March (spring) and July (summer) conditions. Thermal-work strain was quantified from HR and T core values using the Physiological Strain Index (PSI) developed by Moran et al. July PSI and T core values were predicted and not observed due to lack of access to in-theater warfighters at that time. Our methods quantify and compare the predicted and observed thermal-work strain resulting from environment and worn or carried equipment and illustrate that a small increase in ambient temperature and solar load might result in increased thermal-work strain.