Background:
Hepatocellular carcinoma (HCC) ranks as 5th most common cancer. Chronic infection by hepatitis viruses especially HBV and HCV lead to complicated microenvironment which may lead to hepatocarcinogenesis. Lnc RNAs and serum Amyloid A (SAA) have been suggested to have role in tumorogenesis and progression. In this work we investigated the role of lnc RNA AF085935 and SAA as a diagnostic biomarkers in sera of HBV, HCV, HCC patients.
Subjects and Methods: RT-PCR technique was used to assess Lnc RNA AF085935 quantitative expression and ELISA technique was used to detect expression level of SAA at sera of 149 adult Egyptian subjects. They were divided as 35 HCC patients on top of HCV and 24 HCC patients on top of HBV, 35 patients with HBV and 35 patients with HCV and 20 healthy donors.
Results: The level of LncRNA-AF085935 and SAA were high significantly upregulated in HCC patients, HCV patients and HBV patients compared with that in normal controls (p value = <0.001). In addition, higher expressions of Lnc RNA-AF085935 and SAA were observed in HCC patients than in HCV and HBV patients. SAA showed a relatively high accuracy for HCC screening (area under the curve (AUC) = 0.927, 95 % confidence interval (CI) = 0.858-0.997) to be differentiated from normal control. LncRNA-AF085935 showed a relatively higher accuracy for HCC screening ( AUC= 0.931, 95 % CI  = 0.862-1.00) than SAA (AUC = 0.74, 95 % CI = 0.587-0.894) from HBV patients.
Conclusion: Lnc RNA-AF085935 and SAA were observed with an aberrant serum level in sera of HCC, HCV and HBV patients. Lnc RNA-AF085935 and/or SAA can be used as potential biomarkers in HCC tumorogenesis.

Due to the strong nonlinearities of the boiler-turbine system, feasibility and high response capability are hard to realize using traditional linear controllers. Advanced controllers that can ensure stability and feasibility without violating system constraints, and enhance the system’s dynamic output performance, are needed. Therefore, this paper proposes a stable input/output feedback linearization (IOFL) model predictive controller (MPC) technique for the boiler-turbine system. This nonlinear system is decoupled using the IOFL method into a novel linearized model, which is then used for constituting the proposed stable IOFL MPC problem. The proposed scheme uses a constraint mapping method that converts actual input limits into limits on the basis of the control output variable to ensure a feasible solution over the whole prediction horizon. Moreover, a min-max MPC technique in the form of linear matrix inequality with the realization of the input rate-of-change constraints is utilized to ensure the boiler-turbine unit’s stability. The process model and proposed control scheme are executed using MATLAB and the simulation results demonstrate that the proposed controller has an enhanced dynamic output performance compared with an advanced control scheme under various load variations.