Biology subjects

Ting, L.

Publications and source records attributed to Ting, L..

3 recordsLinked to original sources

bioRxiv2018

Closed-loop coupling of a muscle to a robotic device for dynamic assessment of muscle function

The contributions of individual muscles to the performance of functional tasks are difficult to evaluate using traditional isolated muscle protocols. During movements, skeletal muscles work against a variety of environmental loads that influence their energetics and function. In turn, these changes in muscle length and muscle velocity alter the forces that the muscle can generate. Classic single-muscle experiments clamp at least one muscle state (length, velocity, force) such that it is independent of the other states, interrupting the dynamic interactions between the muscle and its environment. The purpose of this study was to design and build a real-time feedback system to virtually couple an isolated muscle to a robotic device. Using this approach, the muscle length is not prescribed, but results from the dynamic interactions between the muscle and a physical environment. Therefore our device facilitates the study of how physical interactions between a muscle, limb, and environments alter the force and motion produced by the muscle during controlled muscle activation. To demonstrate the utility of our system, we replicated some salient features of frog swimming, we coupled a frog plantaris longus muscle to a one-degree of freedom \"limb\" that drove a frog foot through water. We demonstrate that under identical muscle stimulation parameters, changes to muscle moment arm, environmental viscosity, and muscle fatigue can significantly alter the resulting muscle force, length, and work.

physiology↗

bioRxiv2018

Interaction of diabetes and smoking on stroke: A population-based cross-sectional survey in China

ObjectivesDiabetes and smoking are known independent risk factors for stroke; however, their interaction concerning stroke is less clear. We aimed to explore such interaction and its influence on stroke in Chinese adults.\n\nDesignCross-sectional study.\n\nSettingCommunity-based investigation in Xuzhou, China.\n\nParticipantsA total of 39,887 Chinese adults who fulfilled the inclusion criteria were included.\n\nMethodsParticipants were selected using a multi-stage stratified cluster method, and completed self-reported questionnaires on stroke and smoking. Type 2 diabetes mellitus (DM2) was assessed by fasting blood glucose or use of antidiabetic medication. Interaction, relative excess risk owing to interaction (RERI), attributable proportion (AP), and synergy index (S) were evaluated using a logistic regression model.\n\nResultsAfter adjustment for age, sex, marital status, educational level, occupation, physical activity, body mass index, hypertension, family history of stroke, alcohol use, and blood lipids, the relationships between DM2 and stroke, and between smoking and stroke, were still significant: odds ratios were 2.75 (95% confidence interval [CI]: 2.03-3.73) and 1.70 (95% CI: 1.38-2.10), respectively. In subjects with DM2 who smoked, the RERI, AP, and S values (and 95% CIs) were 1.80 (1.24-3.83), 0.52 (0.37-0.73), and 1.50 (1.18-1.84), respectively.\n\nConclusionsThe results suggest there are additive interactions between DM2 and smoking and that these affect stroke in Chinese adults.\n\nArticle Summary: Strengths and limitations of this studyO_LIThe strengths of this study were that a large sample population was randomly selected from the general population of Xuzhou and many confounding risk factors were adjusted for.\nC_LIO_LIOwing to the cross-sectional design, we could not determine a causal combined relationship among diabetes, smoking and stroke.\nC_LIO_LIWe were not able to control for some important and well-known risk factors of diabetes, such as heart rate and cardiovascular causes.\nC_LIO_LIWe did not measure fresh fruit consumption, which is causally related to stroke.\nC_LI

epidemiology↗

bioRxiv2017

A Cross-Sectional Study of Set Shifting Impairments and Falling in Individuals with and without Parkinson’s Disease

INTRODUCTION. Individuals with Parkinsons disease (PD) are at increased risk for falls, and exhibit deficits in executive function, including Set Shifting, which can be measured as the difference between parts B and A of the Trailmaking Test. METHODS. We conducted a cross-sectional study using baseline data of PD patients with and without freezing of gait (FOG) (n=69) and community-dwelling neurologically-normal older adults (NON-PD) (n=84) who had volunteered to participate in clinical rehabilitation research. Multivariate logistic regression analyses were performed to determine associations between Set Shifting, PD, and faller status, as determined by [≥]1 self-reported falls in the previous 6 months, after adjusting for demographic and cognitive factors and clinical disease characteristics. RESULTS. Impaired Set Shifting was associated with previous falls after controlling for age, sex, overall cognitive function, PD, FOG, and PD disease duration (OR=1.29 [1.03-1.60]; P=0.02). In models controlling for age, sex, and overall cognitive function, PD was associated with increased fall prevalence among the study sample (OR=4.15 [95% CI 1.65-10.44], P<0.01) and FOG was associated with increased fall prevalence among the PD sample (OR=3.63 [1.22-10.80], P=0.02). Although the strongest associations between Set Shifting and falling were observed among PD without FOG (OR=2.11) compared to HOA (OR=1.14) and PD with FOG (OR=1.46) in a multivariate model that allowed for interaction between set shifting and PD status, there was insufficient evidence to reject the null hypothesis of no interaction. CONCLUSIONS. Set Shifting is associated with previous falls in non-demented older adults with and without PD.\n\nHighlightsO_LIIndividuals with PD are at increased risk for falls, although causes are unclear.\nC_LIO_LIImpaired Set Shifting was associated with falls in older adults with and without PD.\nC_LIO_LIAssociations were strongest among those with PD but without freezing of gait.\nC_LI

neuroscience↗