The Effects of Pre-conditioning on Exercise-Induced Muscle Damage: A Systematic Review and Meta-analysis

Background: Several studies have utilised isometric, eccentric and downhill walking pre-conditioning as a strategy for alleviating the signs and symptoms of exercise-induced muscle damage (EIMD) following a bout of damaging physical activity. Objectives: This systematic review and meta-analysis examined the effects of pre-conditioning strategies on indices of muscle damage and physical performance measures following a second bout of strenuous physical activity. Data Sources: PubMed, CINAHL and Scopus. Eligibility Criteria: Studies meeting the PICO (population, intervention/exposure, comparison, and outcome) criteria were included in this review: (1) general population or “untrained” participants with no contraindications affecting physical performance; (2) studies with a parallel design to examine the prevention and severity of muscle-damaging contractions; (3) outcome measures were compared using baseline and post-intervention measures; and (4) outcome measures included any markers of indirect muscle damage and muscular contractility measures. Participants: Individuals with no resistance training experiences in the previous 6 or more months. Interventions: A single bout of pre-conditioning exercises consisting of eccentric or isometric contractions performed a minimum of 24 h prior to a bout of damaging physical activity were compared to control interventions that did not perform pre-conditioning prior to damaging physical activity. Study Appraisal: Kmet appraisal system. Synthesis Methods: Quantitative analysis was conducted using forest plots to examine standardised mean differences (SMD, i.e. effect size), test statistics for statistical significance (i.e. Z-values) and between-study heterogeneity by inspecting I2. Results: Following abstract and full-text screening, 23 articles were included in this paper. Based on the meta-analysis, the pre-conditioning group exhibited lower levels of creatine kinase at 24 h (SMD = − 1.64; Z = 8.39; p = 0.00001), 48 h (SMD = − 2.65; Z = 7.78; p = 0.00001), 72 h (SMD = − 2.39; Z = 5.71; p = 0.00001) and 96 h post-exercise (SMD = − 3.52; Z = 7.39; p = 0.00001) than the control group. Delayed-onset muscle soreness was also lower for the pre-conditioning group at 24 h (SMD = − 1.89; Z = 6.17; p = 0.00001), 48 h (SMD = − 2.50; Z = 7.99; p = 0.00001), 72 h (SMD = − 2.73; Z = 7.86; p = 0.00001) and 96 h post-exercise (SMD = − 3.30; Z = 8.47; p = 0.00001). Maximal voluntary contraction force was maintained and returned to normal sooner in the pre-conditioning group than in the control group, 24 h (SMD = 1.46; Z = 5.49; p = 0.00001), 48 h (SMD = 1.59; Z = 6.04; p = 0.00001), 72 h (SMD = 2.02; Z = 6.09; p = 0.00001) and 96 h post-exercise (SMD = 2.16; Z = 5.69; p = 0.00001). Range of motion was better maintained by the pre-conditioning group compared with the control group at 24 h (SMD = 1.48; Z = 4.30; p = 0.00001), 48 h (SMD = 2.20; Z = 5.64; p = 0.00001), 72 h (SMD = 2.66; Z = 5.42; p = 0.00001) and 96 h post-exercise (SMD = 2.5; Z = 5.46; p = 0.00001). Based on qualitative analyses, pre-conditioning activities were more effective when performed at 2–4 days before the muscle-damaging protocol compared with immediately prior to the muscle-damaging protocol, or 1–3 weeks prior to the muscle-damaging protocol. Furthermore, pre-conditioning activities performed using eccentric contractions over isometric contractions, with higher volumes, greater intensity and more lengthened muscle contractions provided greater protection from EIMD. Limitations: Several outcome measures showed high inter-study heterogeneity. The inability to account for differences in durations between pre-conditioning and the second bout of damaging physical activity was also limiting. Conclusions: Pre-conditioning significantly reduced the severity of creatine kinase release, delayed-onset muscle soreness, loss of maximal voluntary contraction force and the range of motion decrease. Pre-conditioning may prevent severe EIMD and accelerate recovery of muscle force generation capacity

Short-term reliability of inflammatory mediators and response to exercise in the heat

Prospective application of serum cytokines, lipopolysaccharide, and heat shock proteins requires reliable measurement of these biomarkers that can signify exercise-induced heat stress in hot conditions. To accomplish this, both short-term (seven day) reliability (at rest, n=12) and the acute responsiveness of each biomarker to exercise in the heat (pre and post 60 min cycling, 34.5oC and 70% RH, n=20) were evaluated. Serum was analysed for the concentration of C-reactive protein (CRP), interleukin (IL-6), heat shock protein 72 (eHSP72), immunoglobulin M (IgM) and lipopolysaccharide (LPS). Test-retest reliability was determined as the coefficient of variation (CV). Biomarkers with the least short-term within-subject variation were IL-6 (19%, ± 20%; CV, ± 95% confidence limits) and LPS (23%, ± 13%). Greater variability was observed for IgM, eHSP72 and CRP (CV range 28-38%). IL-6 exhibited the largest increase in response to acute exercise (95%, ± 11%, p = <0.001) and although CRP had a modest CV (12%, ± 7%) it increased substantially post-exercise (p = 0.02, ES; 0.78). In contrast, eHSP72 and LPS exhibited trivial changes post-exercise. It appears variation of common inflammatory markers after exercise in the heat is not always discernible from short-term (weekly) variation

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices

The role of ETG modes in JET-ILW pedestals with varying levels of power and fuelling

We present the results of GENE gyrokinetic calculations based on a series of JET-ITER-like-wall (ILW) type I ELMy H-mode discharges operating with similar experimental inputs but at different levels of power and gas fuelling. We show that turbulence due to electron-temperature-gradient (ETGs) modes produces a significant amount of heat flux in four JET-ILW discharges, and, when combined with neoclassical simulations, is able to reproduce the experimental heat flux for the two low gas pulses. The simulations plausibly reproduce the high-gas heat fluxes as well, although power balance analysis is complicated by short ELM cycles. By independently varying the normalised temperature gradients (omega(T)(e)) and normalised density gradients (omega(ne )) around their experimental values, we demonstrate that it is the ratio of these two quantities eta(e) = omega(Te)/omega(ne) that determines the location of the peak in the ETG growth rate and heat flux spectra. The heat flux increases rapidly as eta(e) increases above the experimental point, suggesting that ETGs limit the temperature gradient in these pulses. When quantities are normalised using the minor radius, only increases in omega(Te) produce appreciable increases in the ETG growth rates, as well as the largest increases in turbulent heat flux which follow scalings similar to that of critical balance theory. However, when the heat flux is normalised to the electron gyro-Bohm heat flux using the temperature gradient scale length L-Te, it follows a linear trend in correspondence with previous work by different authors

Spectroscopic camera analysis of the roles of molecularly assisted reaction chains during detachment in JET L-mode plasmas

The roles of the molecularly assisted ionization (MAI), recombination (MAR) and dissociation (MAD) reaction chains with respect to the purely atomic ionization and recombination processes were studied experimentally during detachment in low-confinement mode (L-mode) plasmas in JET with the help of experimentally inferred divertor plasma and neutral conditions, extracted previously from filtered camera observations of deuterium Balmer emission, and the reaction coefficients provided by the ADAS, AMJUEL and H2VIBR atomic and molecular databases. The direct contribution of MAI and MAR in the outer divertor particle balance was found to be inferior to the electron-atom ionization (EAI) and electron-ion recombination (EIR). Near the outer strike point, a strong atom source due to the D+2-driven MAD was, however, observed to correlate with the onset of detachment at outer strike point temperatures of Te,osp = 0.9-2.0 eV via increased plasma-neutral interactions before the increasing dominance of EIR at Te,osp &lt; 0.9 eV, followed by increasing degree of detachment. The analysis was supported by predictions from EDGE2D-EIRENE simulations which were in qualitative agreement with the experimental observations

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.Confining plasma and managing disruptions in tokamak devices is a challenge. Here the authors demonstrate a method predicting and possibly preventing disruptions and macroscopic instabilities in tokamak plasma using data from JET

Overview of JET results for optimising ITER operation

The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (α) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER

Disruption prediction at JET through deep convolutional neural networks using spatiotemporal information from plasma profiles

In view of the future high power nuclear fusion experiments, the early identification of disruptions is a mandatory requirement, and presently the main goal is moving from the disruption mitigation to disruption avoidance and control. In this work, a deep-convolutional neural network (CNN) is proposed to provide early detection of disruptive events at JET. The CNN ability to learn relevant features, avoiding hand-engineered feature extraction, has been exploited to extract the spatiotemporal information from 1D plasma profiles. The model is trained with regularly terminated discharges and automatically selected disruptive phase of disruptions, coming from the recent ITER-like-wall experiments. The prediction performance is evaluated using a set of discharges representative of different operating scenarios, and an in-depth analysis is made to evaluate the performance evolution with respect to the considered experimental conditions. Finally, as real-time triggers and termination schemes are being developed at JET, the proposed model has been tested on a set of recent experiments dedicated to plasma termination for disruption avoidance and mitigation. The CNN model demonstrates very high performance, and the exploitation of 1D plasma profiles as model input allows us to understand the underlying physical phenomena behind the predictor decision