MPO77 As a Diagnostic Tool for COPD and ICS Withdrawal

The MPO 77 protein is involved in the formation of atherosclerotic plaques in the human aorta. MPO binds to the apolipoprotein A1 protein, forming an Apolipoprotein A1-MPO complex that mediates the formation of foam cells in atherosclerotic plaques and facilitates their degradation. MPO also oxidizes cholesterol, which is an important step in the synthesis of proatherogenic low-density lipoprotein (LDL). It also acts to promote the cleavage of other atherosclerotic proteins, including fibrin and thrombin. High levels of MPO are associated with an increased risk of cardiovascular disease, coronary heart disease, and stroke.

Pulmonary biomarkers sampled in stable COPD patients have shown promise as diagnostic tools for frequent ECOPD and predictors of ICS withdrawal [76–77]. FeNO, exhaled volatile organic compounds, sputum MPO, and BAL eosinophilia are all associated with frequent ECOPD and may indicate patients who would benefit from ICS therapy. Nevertheless, these biomarkers have not been validated for clinical use. Trying a lot of visit

A more reliable tool might be an in vitro test based on the recognition of MPO antibodies by native or recombinant human MPO (rMPO). Audrain et al reported that 95% of serum samples from PR3-ANCA negative pauci-immune NCGN patients recognized rMPO. The sensitivity of this technique is superior to that of the traditional ELISA methods currently used for MPO-ANCA detection, and it can be performed with a single capture antibody, thereby increasing laboratory efficiency.

The computational bottleneck in DMRG is the calculation of the MPS tensor network, which includes the central MPS tensor Cn for each site, the left and right environment Hamiltonian tensors Ln and Rn, and the MPO tensor Hn. For a given bond dimension D, the time required to compute the tensor network using a standard algorithm increases exponentially with system size, as shown in Fig. 4. To reduce runtimes, we implemented a new parallelization of DMRG that uses the 16 GB of high-bandwidth memory on each TPU core and distributes MPS tensor optimization across multiple TPU cores. We estimated that optimizing a single MPS tensor takes about 2 min on a TPU pod consisting of 1024 TPU v3 cores.