Towards Understanding the Structure, Dynamics and Bio-activity of Diabetic Drug Metformin

Small molecules are often found to exhibit extraordinarily diverse biological activities. Metformin is one of them. It is widely used as anti-diabetic drug for type-two diabetes. In addition to that, metformin hydrochloride shows anti-tumour activities and increases the survival rate of patients suffering from certain types of cancer namely colorectal, breast, pancreas and prostate cancer. However, theoretical studies of structure and dynamics of metformin have not yet been fully explored. In this work, we investigate the characteristic structural and dynamical features of three mono-protonated forms of metformin hydrochloride with the help of experiments, quantum chemical calculations and atomistic molecular dynamics simulations. We validate our force field by comparing simulation results to that of the experimental findings. Nevertheless, we discover that the non-planar tautomeric form is the most stable. Metformin forms strong hydrogen bonds with surrounding water molecules and its solvation dynamics show unique features. Because of an extended positive charge distribution, metformin possesses features of being a permanent cationic partner toward several targets. We study its interaction and binding ability with DNA using UV spectroscopy, circular dichroism, fluorimetry and metadynamics simulation. We find a non-intercalating mode of interaction. Metformin feasibly forms a minor/major groove-bound state within a few tens of nanoseconds, preferably with AT rich domains. A significant decrease in the free-energy of binding is observed when it binds to a minor groove of DNA.Comment: 60 pages, 24 figure

Shape Effect on Electronic and Photovoltaic Properties of CdS Nanocrystals

Changes in electronic and photovoltaic properties of semiconductor nanocrystals predominantly due to changes in shape are discussed here. Cadmium sulfide (CdS) semiconductor nanocrystals of various shapes (tetrapod, tetrahedron, sphere and rod) obtained using an optimized solvothermal process exhibited a mixed cubic (zinc blende) and hexagonal (wurtzite) crystal structure. The simultaneous presence of the two crystal phases in varying amounts is observed to play a pivotal role in determining both the electronic and photovoltaic properties of the CdS nanocrystals. Light to electrical energy conversion efficiencies (measured in two-electrode configuration laboratory solar cells) remarkably decreased by one order in magnitude from tetrapod -> tetrahedron -> sphere -> rod. The tetrapod-CdS nanocrystals, which displayed the highest light to electrical energy conversion efficiency, showed a favorable shift in position of the conduction band edge leading to highest rate of electron injection (from CdS nanocrystal to the wide band gap semiconductor viz, titanium dioxide, TiO2) and lowest rate of electron-hole recombination (higher free electron lifetimes)

One-pot synthesis of a TiO2-CdS nano-heterostructure assembly with enhanced photocatalytic activity

An unprecedented morphology of a titanium dioxide (TiO2) and cadmium sulfide (CdS) self-assembly obtained using a `truly' one-pot and highly cost effective method with a multi-gram scale yield is reported here. The TiO2-CdS assembly, comprising of TiO2 and CdS nanoparticles residing next to each other homogeneously self-assembling into `woollen knitting ball' like microspheres, exhibited remarkable potential as a visible light photocatalyst with high recyclability

Dependence of electron recombination time and light to electricity conversion efficiency on shape of the nanocrystal light sensitizer

Tunability of electron recombination time and light to electricity conversion efficiency to superior values in semiconductor sensitized solar cells via optimized design of nanocrystal light sensitizer shape is discussed here

Optimizing Photovoltaic Response by Tuning Light-Harvesting Nanocrystal Shape Synthesized Using a Quick Liquid-Gas Phase Reaction

The electron recombination lifetime in a sensitized semiconductor assembly is greatly influenced by the crystal structure and geometric form of the light-harvesting semiconductor nanocrystal. When such light harvesters with varying structural characteristics are configured in a photoanode, its interface with the electrolyte becomes equally important and directly influences the photovoltaic efficiency. We have systematically probed here the influence of nanocrystal crystallographic structure and shape on the electron recombination lifetime and its eventual influence on the light to electricity conversion efficiency of a liquid junction semiconductor sensitized solar cell. The light-harvesting cadmium sulfide (CdS) nanocrystals of distinctly different and controlled shapes are obtained using a novel and simple liquid gas phase synthesis method performed at different temperatures involving very short reaction times. High resolution synchrotron X-ray diffraction and spectroscopic studies respectively exhibit different crystallographic phase content and optical properties. When assembled on a mesoscopic TiO2 film by a linker molecule, they exhibit remarkable variation in electron recombination lifetime by 1 order of magnitude, as determined by ac-impedance spectroscopy. This also drastically affects the photovoltaic efficiency of the differently shaped nanocrystal sensitized solar cells

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19

Testing Self-Reducible Samplers

Samplers are the back bone of the implementations of any randomised algorithm. Unfortunately, obtaining an efficient algorithm to test the correctness of samplers is very hard to find. Recently, in a series of works, testers like Barbarik, Teq, Flash for testing of some particular kinds of samplers, like CNF-samplers and Horn-samplers, were obtained. But their techniques have a significant limitation because one cannot expect to use their methods to test for other samplers, such as perfect matching samplers or samplers for sampling linear extensions in posets. In this paper, we present a new testing algorithm that works for such samplers and can estimate the distance of a new sampler from a known sampler (say, uniform sampler). Testing the identity of distributions is the heart of testing the correctness of samplers. This paper’s main technical contribution is developing a new distance estimation algorithm for distributions over high-dimensional cubes using the recently proposed sub-cube conditioning sampling model. Given sub-cube conditioning access to an unknown distribution P, and a known distribution Q defined over {0,1}^n, our algorithm CubeProbeEst estimates the variation distance between P and Q within additive error ζ using O(n^2/ζ^4) sub-cube conditional samples from P. Following the testing-via-learning paradigm, we also get a tester which distinguishes between the cases when P and Q are ε-close or η-far in variation distance with probability at least 0.99 using O(n^2/(η−ε)^4) sub-cube conditional samples. The estimation algorithm in the sub-cube conditioning sampling model helps us to design the first tester for self-reducible samplers. The correctness of the testers is formally proved. On the other hand, we implement our algorithm to create CubeProbeEst and use it to test the quality of three samplers for sampling linear extensions in posets.<br/