Predicting myosin heavy chain isoform from postdissection fiber length in human skeletal muscle fibers.

Experimental techniques in single human skeletal muscle cells require manual dissection. Unlike other mammalian species, human skeletal muscle is characterized by a heterogeneous mixture of myosin heavy chain (MHC) isoforms, typically used to define "fiber type," which profoundly influences cellular function. Therefore, it is beneficial to predict MHC isoform at the time of dissection, facilitating a more balanced fiber-type distribution from a potentially imbalanced sample. Although researchers performing single fiber dissection report predicting fiber-type based on mechanical properties of fibers upon dissection, a rigorous examination of this approach has not been performed. Therefore, we measured normalized fiber length (expressed as a % of the length of the bundle from which the fiber was dissected) in single fibers immediately following dissection. Six hundred sixty-eight individual fibers were dissected from muscle tissue samples from healthy, young adults to assess whether this characteristic could differentiate fibers containing MHC I ("slow" fiber type) or not ("fast" fiber type). Using receiver operator characteristic (ROC) curves, we found that differences in normalized fiber length (114 ± 13%, MHC I; 124 ± 17%, MHC IIA, P < 0.01) could be used to predict fiber type with excellent reliability (area under the curve = 0.72). We extended these analyses to include older adults (2 females, 1 male) to demonstrate the durability of this approach in fibers with likely different morphology and mechanical characteristics. We report that MHC isoform expression in human skeletal muscle fibers can be predicted at the time of dissection, regardless of origin.NEW & NOTEWORTHY A priori estimation of myosin heavy chain (MHC) isoform in individual muscle fibers may bias the relative abundance of fiber types in subsequent assessment. Until now, no standardized assessment approach has been proposed to characterize fibers at the time of dissection. We demonstrate an approach based on normalized fiber length that may dramatically bias a sample toward slow twitch (MHC I) or fast twitch (not MHC I) fiber populations.

Actual vs. perceived exertion during active virtual reality game exercise.

Background: Virtual exercise has become more common as emerging and converging technologies make active virtual reality games (AVRGs) a viable mode of exercise for health and fitness. Our lab has previously shown that AVRGs can elicit moderate to vigorous exercise intensities that meet recommended health benefit guidelines. Dissociative attentional focuses during AVRG gameplay have the potential to widen the gap between participants' perception of exertion and actual exertion. Objective: The aim of this study was to determine actual exertion (AEx) vs. perceived exertion (PEx) levels during AVRGs by measuring heart rate (HR) and ratings of perceived exertion (RPE) in two different settings. Materials and methods: HR and RPE were collected on participants (N = 32; age 22.6 ± 2.6) during 10 min of gameplay in LabS and GymS using the HTC VIVE with the following games played: Fruit Ninja VR (FNVR), Beat Saber (BS), and Holopoint (HP). Results: Participants exhibited significantly higher levels of AEx compared to reported PEx for all three AVRGs (Intensity): FNVR [AEx = 11.6 ± 1.8 (Light), PEx = 9.0 ± 2.0 (Very Light)], BS [AEx = 11.3 ± 1.7 (Light), PEx = 10.3 ± 2.1 (Very Light)], HP [AEx = 13.1 ± 2.3 (Somewhat Hard), PEx = 12.3 ± 2.4 (Light-Somewhat Hard)]. Additionally, participants playing in the GymS experienced significantly higher levels of AEx [12.4 ± 2.3 (Light-Somewhat Hard)] and PEx [10.8 ± 2.5 (Very Light-Light)] compared to the LabS [AEx = 11.6 ± 1.8 (Light), PEx = 10.3 ± 2.6 (Very Light-Light)]. Conclusion: Perceptions of exertion may be lower than actual exertion during AVRG gameplay, and exertion levels can be influenced by the setting in which AVRGs are played. This may inform VR developers and health clinicians who aim to incorporate exercise/fitness regimens into upcoming 'virtual worlds' currently being developed at large scales (i.e., the "metaverse").

A Handheld Metabolic Device (Lumen) to Measure Fuel Utilization in Healthy Young Adults: Device Validation Study.

BACKGROUND: Metabolic carts measure the carbon dioxide (CO2) produced and oxygen consumed by an individual when breathing to assess metabolic fuel usage (carbohydrates versus fats). However, these systems are expensive, time-consuming, and only available in health care laboratory settings. A small handheld device capable of determining metabolic fuel usage via CO2 from exhaled air has been developed. OBJECTIVE: The aim of this study is to evaluate the validity of a novel handheld device (Lumen) for measuring metabolic fuel utilization in healthy young adults. METHODS: Metabolic fuel usage was assessed in healthy participants (n=33; mean age 23.1 years, SD 3.9 years) via respiratory exchange ratio (RER) values obtained from a metabolic cart as well as % CO2 from the Lumen device. Measurements were performed at rest in two conditions: fasting, and after consuming 150 grams of glucose, in order to determine changes in metabolic fuel usage. Reduced major axis regression and simple linear regression were performed to test for agreement between RER and Lumen % CO2. RESULTS: Both RER and Lumen % CO2 significantly increased after glucose intake (P<.001 for both) compared with fasting conditions, by 0.089 and 0.28, respectively. Regression analyses revealed an agreement between the two measurements (F1,63=18.54; P<.001). CONCLUSIONS: This study shows the validity of Lumen for detecting changes in metabolic fuel utilization in a comparable manner with a laboratory standard metabolic cart, providing the ability for real-time metabolic information for users under any circumstances.