Biomarker analysis of fucosylated kininogen through depletion of lectin reactive heterophilic antibodies in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) accounts for >700,000 deaths worldwide, largely related to poor rates of diagnosis. Our previous work identified glycoproteins with increased levels of fucosylation in HCC. Plate-based assays to measure this change were compromised by increased levels of heterophilic antibodies with glycan lacking terminal galactose residues, which allowed for increased binding to the lectins used in these assays. To address this issue, we developed a multi-step protein A/G incubation and filtration method to remove the contaminating signal. However, this method was time consuming and expensive so alternative methods were desired. Herein, we describe a simple method relying on PEG precipitation that allows for the removal of IgG and IgM but retention of glycoproteins of interest. This method was tested on three sample sets, two internal and one external. PEG depletion of heterophilic IgG and IgM reduced in the coefficient of variation as observed with the protein A/G filtration method from 26.82% to 7.50% and allowed for the measurement of fucosylated protein. This method allowed for the measurement of fucosylated kininogen, which could serve as a biomarker of HCC. In conclusion, a new and simple method for the depletion of heterophilic IgG and IgM was developed and allowed for the analysis of fucosylated kininogen in patients with liver disease.

Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements.

This paper addresses the problem of state estimation for a class of discrete-time stochastic complex networks with a constrained and randomly varying coupling and uncertain measurements. The randomly varying coupling is governed by a Markov chain, and the capacity constraint is handled by introducing a logarithmic quantizer. The uncertainty of measurements is modeled by a multiplicative noise. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the coupling information, and an augmented estimation error system is obtained using the Kronecker product. Sufficient conditions are established, which guarantee that the estimation error system is stochastically stable and achieves the strict (Q, S, R)-γ-dissipativity. Then, the estimator gains are derived using the linear matrix inequality method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed new design techniques.