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1.
Turk J Pharm Sci ; 16(2): 196-205, 2019 Jun.
Article in English | MEDLINE | ID: mdl-32454714

ABSTRACT

OBJECTIVES: Mycobacterium tuberculosis is the causative organism of tuberculosis, which is the most lethal disease after cancer in the current decade. The development of multidrug and broadly drug-resistant strains is making the problem of tuberculosis more and more critical. In the last 40 years, only one molecule has been added to the treatment regimen. Generally, drug design and development programs target proteins whose function is known to be essential to the bacterial cell. M. tuberculosis possesses specialized protein export systems like the SecA2 export pathway and ESX pathways. MATERIALS AND METHODS: In the present communication, rational development of an antimycobacterial agent's targeting protein export system was carried out by integrating pocket modeling and virtual analysis. RESULTS: The 23 identified potential lead compounds were synthesized, characterized by physicochemical and spectroscopic methods like infrared and nuclear magnetic resonance spectroscopy, and further screened for antimycobacterial activity using isoniazid as standard. All the designed compounds showed profound antimycobacterial activity. CONCLUSION: We found that Q30, M9, M26, U8, and R26 molecules had significant desirable biological activity and specific interactions with Sec of mycobacteria. Further optimization of these leads is necessary for the development of potential antimycobacterial drug candidates with fewer side effects.

2.
Front Neurosci ; 12: 160, 2018.
Article in English | MEDLINE | ID: mdl-29643760

ABSTRACT

This paper presents a real-time, low-complexity neuromorphic speech recognition system using a spiking silicon cochlea, a feature extraction module and a population encoding method based Neural Engineering Framework (NEF)/Extreme Learning Machine (ELM) classifier IC. Several feature extraction methods with varying memory and computational complexity are presented along with their corresponding classification accuracies. On the N-TIDIGITS18 dataset, we show that a fixed bin size based feature extraction method that votes across both time and spike count features can achieve an accuracy of 95% in software similar to previously report methods that use fixed number of bins per sample while using ~3× less energy and ~25× less memory for feature extraction (~1.5× less overall). Hardware measurements for the same topology show a slightly reduced accuracy of 94% that can be attributed to the extra correlations in hardware random weights. The hardware accuracy can be increased by further increasing the number of hidden nodes in ELM at the cost of memory and energy.

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