Novel Dicyano-Phenylenevinylene Fluorophores for Low-Doped Layers: A Highly Emissive Material for Red OLEDs

Two efficient deep red (DR)-emitting organic dicyano-phenylenevinylene derivatives with terminal withdrawing or donor groups were synthesized. The spectroscopic properties of the neat solids and the low-doped layers in polystyrene or polyvinylcarbazole host matrixes were analyzed, and the luminescence performance was explained using density functional theory (DFT) analysis. A noteworthy 89% fluorescence quantum yield was observed for the brightest red-emissive polyvinylcarbazole (PVK) blend. This result pushed us to successfully produce an emissive red organic light-emitting device (OLED) as a preliminary feasibility test

Theoretical investigation of hydroxylated analogues of valinomycin as potassium transporter

: Valinomycin is a potent ionophore known for its ability to transport potassium ions across biological membranes. The study focuses on the hydroxylated analogues of valinomycin (HyVLMs) and compares their energy profiles and capabilities for transporting potassium ions across phospholipid membranes. Using metadynamics, we investigated the energy profiles of wildtype valinomycin (VLM_1) and its three hydroxylated analogues (VLM_2, VLM_3, and VLM_4). We observed that all analogues exhibited energy maxima in the centre of the membrane and preferred positions below the phospholipid heads. Furthermore, the entry barriers for membrane penetration were similar among the analogues, suggesting that the hydroxyl group did not significantly affect their passage through the membrane. Transition state calculations provided insights into the ability of valinomycin analogues to capture potassium ions, with VLM_4 showing the lowest activation energy and VLM_2 displaying the highest. Our findings contribute to understanding the mechanisms of potassium transport by valinomycin analogues and highlight their potential as ionophores. The presence of the hydroxyl group is of particular importance because it paves the way for subsequent chemical modifications and the synthesis of new antiviral agents with reduced intrinsic toxicity

Evaluating Epidemiological Risk by Using Open Contact Tracing Data: Correlational Study

BackgroundDuring the 2020s, there has been extensive debate about the possibility of using contact tracing (CT) to contain the SARS-CoV-2 pandemic, and concerns have been raised about data security and privacy. Little has been said about the effectiveness of CT. In this paper, we present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users. ObjectiveWe aimed to discuss the technical and health aspects of using a centralized approach. We also aimed to show the correlation between the acquired contact data and the number of SARS-CoV-2–positive cases. Finally, we aimed to analyze CT data to define population behaviors and show the potential applications of real CT data. MethodsWe collected, analyzed, and evaluated CT data on the duration, persistence, and frequency of contacts over several months of observation. A statistical test was conducted to determine whether there was a correlation between indices of behavior that were calculated from the data and the number of new SARS-CoV-2 infections in the population (new SARS-CoV-2–positive cases). ResultsWe found evidence of a correlation between a weighted measure of contacts and the number of new SARS-CoV-2–positive cases (Pearson coefficient=0.86), thereby paving the road to better and more accurate data analyses and spread predictions. ConclusionsOur data have been used to determine the most relevant epidemiological parameters and can be used to develop an agent-based system for simulating the effects of restrictions and vaccinations. Further, we demonstrated our system's ability to identify the physical locations where the probability of infection is the highest. All the data we collected are available to the scientific community for further analysis

Advancements and novel approaches in modified AutoDock Vina algorithms for enhanced molecular docking

Molecular docking plays a crucial role in modern drug discovery by facilitating the prediction of interactions between small molecules and biomolecular targets. AutoDock Vina (Vina) has earned its reputation as a leading software thanks to its effective energy-based scoring system and user-friendly interface. However, the growing demands of computational biology have prompted investigations into improvements for Vina, leading to a range of algorithmic enhancements. This systematic review explores the recent developments achieved by Vina for molecular docking. These modifications include hybrid parallelization methods utilizing high-performance computing and innovative scoring functions integrated with machine learning. The review examines the difficulties and possibilities associated with these adapted algorithms, shedding light on their diverse origins and potential collaboration across computational chemistry, machine learning, structural biology, and emerging technologies

YAMACS: a graphical interface for GROMACS

A graphical user interface for the GROMACS program has been developed as plugins for YASARA molecular graphics suite. The most significant GROMACS methods can be run entirely via a windowed menu system, and the results are shown on screen in real time

The Effect of Bulky Substituents on Two π-Conjugated Mesogenic Fluorophores. Their Organic Polymers and Zinc-Bridged Luminescent Networks

From a dicyano-phenylenevinylene (PV) and an azobenzene (AB) skeleton, two new symmetrical salen dyes were obtained. Terminal bulky substituents able to reduce intermolecular interactions and flexible tails to guarantee solubility were added to the fluorogenic cores. Photochemical performances were investigated on the small molecules in solution, as neat crystals and as dopants in polymeric matrixes. High fluorescence quantum yield in the orange-red region was observed for the brightest emissive films (88% yield). The spectra of absorption and fluorescence were predicted by Density Functional Theory (DFT) calculations. The predicted energy levels of the frontier orbitals are in good agreement with voltammetry and molecular spectroscopy measures. Employing the two dyes as dopants of a nematic polymer led to remarkable orange or yellow luminescence, which dramatically decreases in on-off switch mode after liquid crystal (LC) order was lost. The fluorogenic cores were also embedded in organic polymers and self-assembly zinc coordination networks to transfer the emission properties to a macro-system. The final polymers emit from red to yellow both in solution and in the solid state and their photoluminescence (PL) performance are, in some cases, enhanced when compared to the fluorogenic cores

Combined computational and cellular screening identifies synergistic inhibition of SARS-CoV-2 by lenvatinib and remdesivir

Rapid repurposing of existing drugs as new therapeutics for COVID-19 has been an important strategy in the management of disease severity during the ongoing SARS-CoV-2 pandemic. Here, we used high-throughput docking to screen 6000 compounds within the DrugBank library for their potential to bind and inhibit the SARS-CoV-2 3 CL main protease, a chymotrypsin-like enzyme that is essential for viral replication. For 19 candidate hits, parallel in vitro fluorescence-based protease-inhibition assays and Vero-CCL81 cell-based SARS-CoV-2 replication-inhibition assays were performed. One hit, diclazuril (an investigational anti-protozoal compound), was validated as a SARS-CoV-2 3 CL main protease inhibitor in vitro (IC$_{50}$ value of 29 µM) and modestly inhibited SARS-CoV-2 replication in Vero-CCL81 cells. Another hit, lenvatinib (approved for use in humans as an anti-cancer treatment), could not be validated as a SARS-CoV-2 3 CL main protease inhibitor in vitro, but serendipitously exhibited a striking functional synergy with the approved nucleoside analogue remdesivir to inhibit SARS-CoV-2 replication, albeit this was specific to Vero-CCL81 cells. Lenvatinib is a broadly-acting host receptor tyrosine kinase (RTK) inhibitor, but the synergistic effect with remdesivir was not observed with other approved RTK inhibitors (such as pazopanib or sunitinib), suggesting that the mechanism-of-action is independent of host RTKs. Furthermore, time-of-addition studies revealed that lenvatinib/remdesivir synergy probably targets SARS-CoV-2 replication subsequent to host-cell entry. Our work shows that combining computational and cellular screening is a means to identify existing drugs with repurposing potential as antiviral compounds. Future studies could be aimed at understanding and optimizing the lenvatinib/remdesivir synergistic mechanism as a therapeutic option