Role of MicroRNAs as post transcription regulators of matrix metalloproteinases and their association in tuberculous meningitis.

Matrix metalloproteinases (MMPs) have a role in driving neuroinflammation in infectious as well as non-infectious diseases; however, recent reports have potentiated the role of microRNAs in regulating MMPs at post-transcriptional levels, leading to dysregulation of crucial MMP functions like tissue remodelling, blood brain barrier integrity, etc. In present study, microRNAs regulating MMPs (MMP2 and MMP3) were selected from database search followed by literature support. Expression of these microRNAs i.e., hsa-miR-495-3p, hsa-miR-132-3p and hsa-miR-21-5p was assessed by RT-PCR and the protein levels of MMPs were assessed by ELISA in the cerebrospinal fluid (CSF) of tuberculous meningitis (TBM) patients, healthy controls (HC) and non-infectious neuroinflammatory disease (NID) patients. The expression of hsa-miR-495-3p and hsa-miR-132-3p showed downregulation in TBM while hsa-miR-21-5p was overexpressed as compared to healthy controls. Moreover, MMP levels were found to be deranged with a significant increase in MMP3 levels in the TBM and NID patients compared to HC group. These observations highlight dysregulated microRNAs (hsa-miR-495-3p, hsa-miR-21-5p and hsa-miR-132-3p) levels might impair the levels of MMPs (MMP2 and MMP3) leading to neuroinflammation in TBM and NID population. These findings can further be applied to target these microRNAs for developing newer treatment modalities for better complication management.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.