Background: Sepsis remains a major health issue without an effective therapy. Ferroptosis, an iron-dependent programmed cell death, has been proposed to be related to the pathogenesis of sepsis. Irisin, a myokine released during exercise, improves mitochondrial function under various conditions. Ferroptosis is closely related to mitochondrial function. However, the role of irisin in sepsis-induced ferroptosis and mitochondrial dysfunction in the liver remained unknown. Thus, we hypothesize that irisin treatment suppresses ferroptosis and improves mitochondrial function in sepsis. Methods: To study this, we first explored the role of serum irisin levels in patients with sepsis, and then determined the effect of irisin administration on ferroptosis and mitochondrial function in the liver of septic mice. Results: Serum irisin levels were decreased and negatively correlated with the APACHE II scores in patients with sepsis. In mice subjected to cecal ligation and puncture (CLP), exogenous irisin administration suppressed ferroptosis, inhibited inflammatory response, decreased reactive oxygen species (ROS)

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This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustness to stochastic identification errors. State-of-the-art methods construct fault estimators with identified state-space models or Markov parameters, but they do not compensate for identification errors. Motivated by this limitation, we first propose a receding horizon fault estimator parameterized by predictor Markov parameters. This estimator provides (asymptotically) unbiased fault estimates as long as the subsystem from faults to outputs has no unstable transmission zeros. When the identified Markov parameters are used to construct the above fault estimator, zero-mean stochastic identification errors appear as model uncertainty multiplied with unknown fault signals and online system inputs/outputs (I/O). Based on this fault estimation error analysis, we formulate a mixed-norm problem for the offline robust design that regards online I/O data as unknown. An alternative online mixed-norm problem is also proposed that can further reduce estimation errors when the online I/O data have large amplitudes, at the cost of increased computational burden. Based on a geometrical interpretation of the two proposed mixed-norm problems, systematic methods to tune the user-defined parameters therein are given to achieve desired performance trade-offs. Simulation examples illustrate the benefits of our proposed methods compared to recent literature.

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