[Use of machine learning for the prediction of stress using the example of logistics]. / Einsatz von Maschinellem Lernen für die Vorhersage von Stress am Beispiel der Logistik.

Stress and its complex effects have been researched since the beginning of the 20th century. The manifold psychological and physical stressors in the world of work can, in sum, lead to disorders of the organism and to illness. Since the physical and subjective consequences of stress vary individually, no absolute threshold values can be determined. Machine learning (ML) methods are used in this article to research the systematic recognition of patterns of physiological and subjective stress parameters and to predict stress. The logistics sector serves as a practical application case in which stress factors are often rooted in the activity and work organisation. One design element of the prevention of stress is the work break. ML methods are used to investigate the extent to which stress can be predicted on the basis of physiological and subjective parameters in order to recommend breaks individually. The article presents the interim status of a software solution for dynamic break management for logistics.Practical Relevance: The aim of the software solution "Dynamic Break" is to preventively prevent stress resulting from mental and physical stress factors in logistics and to keep employees healthy, satisfied, fit for work and productive in the long term. Individualized rest breaks as a design element can support companies in deploying human resources more flexibly in line with the dynamic requirements of logistics.

Endogenous Il10 alleviates the systemic antiviral cellular immune response and T cell-mediated immunopathology in select organs of acutely LCMV-infected mice.

The immunoregulatory cytokine IL-10 suppresses T-cell immunity. The complementary question, whether IL-10 is also involved in limiting the collateral damage of vigorous T cell responses, has not been addressed in detail. Here, we report that the particularly strong virus-specific immune response during acute primary infection with the lymphocytic choriomeningitis virus (LCMV) in mice is significantly further increased in Il10-deficient mice, particularly regarding frequencies and cytotoxic activity of CD8(+) T cells. This increase results in exacerbating immunopathology in select organs, ranging from transient local swelling to an increased risk for mortality. Remarkably, LCMV-induced, T cell-mediated hepatitis is not affected by endogenous Il10. The alleviating effect of Il10 on LCMV-induced immunopathology was found to be operative in delayed-type hypersensitivity footpad-swelling reaction and in debilitating meningitis in mice of both the C57BL/6 and BALB/c strains. These strains are prototypic counterpoles for genetically imprinted type 1-biased versus type 2-biased T cell-mediated immune responses against various infectious pathogens. However, during acute LCMV infection, neither systemic cytokine patterns nor the impact of Il10 on LCMV-induced immunopathology differed conspicuously between these two strains of mice. This study documents a physiological role of Il10 in the regulation of a balanced T-cell response limiting immunopathological damage.