Computational Screening of T-Muurolol for an Alternative Antibacterial Solution against Infections: An In Silico Approach for Phytochemical-Based Drug Discovery.

infections present a significant threat to the global healthcare system. The increasing resistance to existing antibiotics and their limited efficacy underscores the urgent need to identify new antibacterial agents with low toxicity to effectively combat various infections. Hence, in this study, we have screened T-muurolol for possible interactions with several -specific bacterial proteins to establish its potential as an alternative antibacterial agent.

In silico exploration of 4(α-l-rhamnosyloxy)-benzyl isothiocyanate: A promising phytochemical-based drug discovery approach for combating multi-drug resistant Staphylococcus aureus.

Multidrug-resistant (MDR) Staphylococcus aureus infections significantly threaten global health. With rising resistance to current antibiotics and limited solutions, the urgent discovery of new, effective, and affordable antibacterials with low toxicity is imperative to combat diverse MDR S. aureus strains.

Reliability of energy landscape analysis of resting-state functional MRI data.

Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model.

State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis.

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics.

Seed-Induced Living Two-Dimensional (2D) Supramolecular Polymerization in Water: Implications on Protein Adsorption and Enzyme Inhibition.

In biological systems, programmable supramolecular frameworks characterized by coordinated directional non-covalent interactions are widespread. However, only a small number of reports involve pure water-based dynamic supramolecular assembly of artificial π-amphiphiles, primarily due to the formidable challenge of counteracting the strong hydrophobic dominance of the π-surface in water, leading to undesired kinetic traps. This study reveals the pathway complexity in hydrogen-bonding-mediated supramolecular polymerization of an amide-functionalized naphthalene monoimide (NMI) building block with a hydrophilic oligo-oxyethylene (OE) wedge.