Innovative Use of an Injectable, Self-Healing Drug-Loaded Pectin-Based Hydrogel for Micro- and Supermicro-Vascular Anastomoses.

Microvascular surgery plays a crucial role in reconnecting micrometer-scale vessel ends. Suturing remains the gold standard technique for small vessels; however, suturing the collapsed lumen of microvessels is challenging and time-consuming, with the risk of misplaced sutures leading to failure. Although multiple solutions have been reported, the emphasis has predominantly been on resolving challenges related to arteries rather than veins, and none has proven superior. In this study, we introduce an innovative solution to address these challenges through the development of an injectable lidocaine-loaded pectin hydrogel by using computational and experimental methods. To understand the extent of interactions between the drug and the pectin chain, molecular dynamics (MD) simulations and quantum mechanics (QM) calculations were conducted in the first step of the research. Then, a series of experimental studies were designed to prepare lidocaine-loaded injectable pectin-based hydrogels, and their characterization was performed by using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and rheological analysis. After all the results were evaluated, the drug-loaded pectin-based hydrogel exhibiting self-healing properties was selected as a potential candidate for in vivo studies to determine its performance during operation. In this context, the hydrogel was injected into the divided vessel ends and perivascular area, allowing for direct suturing through the gel matrix. While our hydrogel effectively prevented vasospasm and facilitated micro- and supermicro-vascular anastomoses, it was noted that it did not cause significant changes in late-stage imaging and histopathological analysis up to 6 months. We strongly believe that pectin-based hydrogel potentially enhanced microlevel arterial, lymphatic, and particularly venous anastomoses.

A computational workflow to determine drug candidates alternative to aminoglycosides targeting the decoding center of E. coli ribosome.

The global antibiotic resistance problem necessitates fast and effective approaches to finding novel inhibitors to treat bacterial infections. In this study, we propose a computational workflow to identify plausible high-affinity compounds from FDA-approved, investigational, and experimental libraries for the decoding center on the small subunit 30S of the E. coli ribosome. The workflow basically consists of two molecular docking calculations on the intact 30S, followed by molecular dynamics (MD) simulations coupled with MM-GBSA calculations on a truncated ribosome structure. The parameters used in the molecular docking suits, Glide and AutoDock Vina, as well as in the MD simulations with Desmond were carefully adjusted to obtain expected interactions for the ligand-rRNA complexes. A filtering procedure was followed, considering a fingerprint based on aminoglycoside's binding site on the 30S to obtain seven hit compounds either with different clinical usages or aminoglycoside derivatives under investigation, suggested for in vitro studies. The detailed workflow developed in this study promises an effective and fast approach for the estimation of binding free energies of large protein-RNA and ligand complexes.