No abstract

Calcitonin gene-related peptide (CGRP) is a pivotal messenger in the inflammatory process in migraine. Limited evidence indicates that diet impacts circulating levels of CGRP, suggesting that certain elements in the diet may influence migraine outcomes. Interruption of calcium signaling, a mechanism which can trigger CGRP release, has been suggested as one potential route by which exogenous food substances may impact CGRP secretion. The objective of this study was to investigate the effects of foods and a dietary supplement on two migraine-related mechanisms in vitro: CGRP secretion from neuroendocrine CA77 cells, and calcium uptake by differentiated PC12 cells. Ginger and grape pomace extracts were selected for their anecdotal connections to reducing or promoting migraine. S-petasin was selected as a suspected active constituent of butterbur extract, the migraine prophylactic dietary supplement. Results showed a statistically significant decrease in stimulated CGRP secretion from CA77 cells following treatment with ginger (0.2 mg dry ginger equivalent/mL) and two doses of grape pomace (0.25 and 1.0 mg dry pomace equivalent/mL) extracts. Relative to vehicle control, CGRP secretion decreased by 22%, 43%, and 87%, respectively. S-petasin at 1.0 μM also decreased CGRP secretion by 24%. Meanwhile, S-petasin and ginger extract showed inhibition of calcium influx, whereas grape pomace had no effect on calcium. These results suggest that grape pomace and ginger extracts, and S-petasin may have anti-inflammatory propensity by preventing CGRP release in migraine, although potentially by different mechanisms, which future studies may elucidate further.

There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings.

The analysis of functional upper extremity (UE) movement kinematics has implications across domains such as rehabilitation and evaluating job-related skills. Using movement kinematics to quantify movement quality and skill is a promising area of research but is currently not being used widely due to issues associated with cost and the need for further methodological validation. Recent developments by computationally-oriented research communities have resulted in potentially useful methods for evaluating UE function that may make kinematic analyses easier to perform, generally more accessible, and provide more objective information about movement quality, the importance of which has been highlighted during the COVID-19 pandemic. This narrative review provides an interdisciplinary perspective on the current state of computer-assisted methods for analyzing UE kinematics with a specific focus on how to make kinematic analyses more accessible to domain experts. We find that a variety of methods exist to more easily measure and segment functional UE movement, with a subset of those methods being validated for specific applications. Future directions include developing more robust methods for measurement and segmentation, validating these methods in conjunction with proposed kinematic outcome measures, and studying how to integrate kinematic analyses into domain expert workflows in a way that improves outcomes.