Foot Mechanics Define Directional Changes in Curved-Path Walking: New Methods to Assess the Motor Skill of Walking

Although it is essential to navigating the world, curved path walking is a challenge to mediolateral balance control. The focus of previous curved-path walking research was in spatiotemporal characteristics. We quantified the foot-ground interaction, center of pressure (COP) characteristics during non-linear (eg curved-path) walking important to understand the functional mechanics of directional changes for curved paths. We hypothesized the foot mechanics differ between older adults with better versus poorer curved-path walking (Figure of 8 Walk Test, F8W). Twenty-five older adults (mean age 71.8 ± 8.9 years) completed the F8W on an instrumented walkway (Protokinetics, LLC.) The derived metrics of the foot mechanics included medial/lateral movement of the COP for inside and outside steps, maximum medial and lateral COP excursions, and total medial/lateral COP range. Pearson correlations were used to examine relations F8W (time and steps) and COP metrics; ANOVAs were used to examine differences in COP metrics between older adults grouped by median-split of F8W time. Longer F8W time and more steps were related to lesser total COP range and outside foot lateral maximum excursion (r range -0.415 to -0.706, p<0.04). Older adults with stronger F8W performance compared to poorer F8W performance had larger outside foot total COP ranges (3.61cm vs 4.39cm, p=0.016) and greater lateral excursion (1.60cm vs 2.12cm, p=0,003). Foot-ground interactions offer new insights into control of curved path walking and methods for evaluating efficacy of interventions focused on improving walking skill in older adults.

Evaluation of drug efficacy based on the spatial position comparison of drug–target interaction centers

The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drug’s pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs’ efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug–target pairs. Second, we set 5.5 Å as the maximum distance threshold for the drug–amino acid residue atom interaction and construct 3-dimensional interaction surface models. Third, by calculating the spatial position of the 3-dimensional interaction surface center, we develop a comparison strategy for estimating the efficacy of different drug–target pairs. For the 1199 drug–target interactions of the 649 drugs and 355 targets, the drugs that have similar interaction center positions tend to have similar efficacies in disease treatment, especially in the analysis of the 37 targeted relationships between the 15 known anti-cancer drugs and 10 target molecules. Furthermore, the analysis of the unpaired anti-cancer drug and target molecules suggests that there is a potential application for discovering new drug actions using the sampling molecular docking and analyzing method. The comparison of the drug–target interaction center spatial position method better reflect the drug–target interaction situations and could support the discovery of new efficacies among the known anti-cancer drugs.