Dataset Information

Assessing the efficacy of tablet-based simulations for learning pseudo-surgical instrumentation.

ABSTRACT: Nurses and surgeons must identify and handle specialized instruments with high temporal and spatial precision. It is crucial that they are trained effectively. Traditional training methods include supervised practices and text-based study, which may expose patients to undue risk during practice procedures and lack motor/haptic training respectively. Tablet-based simulations have been proposed to mediate some of these limitations. We implemented a learning task that simulates surgical instrumentation nomenclature encountered by novice perioperative nurses. Learning was assessed following training in three distinct conditions: tablet-based simulations, text-based study, and real-world practice. Immediately following a 30-minute training period, instrument identification was performed with comparable accuracy and response times following tablet-based versus text-based training, with both being inferior to real-world practice. Following a week without practice, response times were equivalent between real-world and tablet-based practice. While tablet-based training does not achieve equivalent results in instrument identification accuracy as real-world practice, more practice repetitions in simulated environments may help reduce performance decline. This project has established a technological framework to assess how we can implement simulated educational environments in a maximally beneficial manner.

SUBMITTER: Kryklywy JH

PROVIDER: S-EPMC7808648 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Assessing the efficacy of tablet-based simulations for learning pseudo-surgical instrumentation.

Kryklywy James H JH Roach Victoria A VA Todd Rebecca M RM

PloS one 20210114 1

Nurses and surgeons must identify and handle specialized instruments with high temporal and spatial precision. It is crucial that they are trained effectively. Traditional training methods include supervised practices and text-based study, which may expose patients to undue risk during practice procedures and lack motor/haptic training respectively. Tablet-based simulations have been proposed to mediate some of these limitations. We implemented a learning task that simulates surgical instrumenta ...[more]

PMID: 33444407

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Project description:Drug-induced cardiotoxicity is a potentially lethal and yet one of the most common side effects with the drugs in clinical use. Most of the drug-induced cardiotoxicity is associated with an off-target pharmacological blockade of K+ currents carried out by the cardiac Human-Ether-a-go-go-Related (hERG1) potassium channel. There is a compulsory preclinical stage safety assessment for the hERG1 blockade for all classes of drugs, which adds substantially to the cost of drug development. The availability of a high-resolution cryogenic electron microscopy (cryo-EM) structure for the channel in its open/depolarized state solved in 2017 enabled the application of molecular modeling for rapid assessment of drug blockade by molecular docking and simulation techniques. More importantly, if successful, in silico methods may allow a path to lead-compound salvaging by mapping out key block determinants. Here, we report the blind application of the site identification by the ligand competitive saturation (SILCS) protocol to map out druggable/regulatory hotspots in the hERG1 channel available for blockers and activators. The SILCS simulations use small solutes representative of common functional groups to sample the chemical space for the entire protein and its environment using all-atom simulations. The resulting chemical maps, FragMaps, explicitly account for receptor flexibility, protein-fragment interactions, and fragment desolvation penalty allowing for rapid ranking of potential ligands as blockers or nonblockers of hERG1. To illustrate the power of the approach, SILCS was applied to a test set of 55 blockers with diverse chemical scaffolds and pIC50 values measured under uniform conditions. The original SILCS model was based on the all-atom modeling of the hERG1 channel in an explicit lipid bilayer and was further augmented with a Bayesian-optimization/machine-learning (BML) stage employing an independent literature-derived training set of 163 molecules. BML approach was used to determine weighting factors for the FragMaps contributions to the scoring function. pIC50 predictions from the combined SILCS/BML approach to the 55 blockers showed a Pearson correlation (PC) coefficient of >0.535 relative to the experimental data. SILCS/BML model was shown to yield substantially improved performance as compared to commonly used rigid and flexible molecular docking methods for a well-established cohort of hERG1 blockers, where no correlation with experimental data was recorded. SILCS/BML results also suggest that a proper weighting of protonation states of common blockers present at physiological pH is essential for accurate predictions of blocker potency. The precalculated and optimized SILCS FragMaps can now be used for the rapid screening of small molecules for their cardiotoxic potential as well as for exploring alternative binding pockets in the hERG1 channel with applications to the rational design of activators.

Dataset Information

Assessing the efficacy of tablet-based simulations for learning pseudo-surgical instrumentation.

Publications

Assessing the efficacy of tablet-based simulations for learning pseudo-surgical instrumentation.

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