Adverse Outcomes Associated with Cigarette Smoke Radicals Related to Damage to Protein-disulfide Isomerase.

Identification of factors contributing to the development of chronic obstructive pulmonary disease (COPD) is crucial for developing new treatments. An increase in the levels of protein-disulfide isomerase (PDI), a multifaceted endoplasmic reticulum resident chaperone, has been demonstrated in human smokers, presumably as a protective adaptation to cigarette smoke (CS) exposure. We found a similar increase in the levels of PDI in the murine model of COPD. We also found abnormally high levels (4-6 times) of oxidized and sulfenilated forms of PDI in the lungs of murine smokers compared with non-smokers. PDI oxidation progressively increases with age. We begin to delineate the possible role of an increased ratio of oxidized PDI in the age-related onset of COPD by investigating the impact of exposure to CS radicals, such as acrolein (AC), hydroxyquinones (HQ), peroxynitrites (PN), and hydrogen peroxide, on their ability to induce unfolded protein response (UPR) and their effects on the structure and function of PDIs. Exposure to AC, HQ, PN, and CS resulted in cysteine and tyrosine nitrosylation leading to an altered three-dimensional structure of the PDI due to a decrease in helical content and formation of a more random coil structure, resulting in protein unfolding, inhibition of PDI reductase and isomerase activity in vitro and in vivo, and subsequent induction of endoplasmic reticulum stress response. Addition of glutathione prevented the induction of UPR, and AC and HQ induced structural changes in PDI. Exposure to PN and glutathione resulted in conjugation of PDI possibly at active site tyrosine residues. The findings presented here propose a new role of PDI in the pathogenesis of COPD and its age-dependent onset.

Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients.

The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.