Ergogenic effect of ischemic preconditioning is not directly conferred to isolated skeletal muscle via blood.

PURPOSE: Ischemic preconditioning (IPC) in humans has been demonstrated to confer ergogenic benefit to aerobic exercise performance, with an improvement in the response rate when the IPC stimulus is combined with concurrent exercise. Despite potential performance improvements, the nature of the neuronal and humoral mechanisms of conferral and their respective contributions to ergogenic benefit remain unclear. We sought to examine the effects of the humoral component of ischemic preconditioning on skeletal muscle tissue using preconditioned human serum and isolated mouse soleus. METHODS: Isolated mouse soleus was electrically stimulated to contract while in human serum preconditioned with either traditional (IPC) or augmented (AUG) ischemic preconditioning compared to control (CON) and exercise (ERG) preconditioning. Force frequency (FF) curves, twitch responses, and a fatigue-recovery protocol were performed on muscles before and after the addition of serum. After preconditioning, human participants performed a 4 km cycling time trial in order to identify responders and non-responders to IPC. RESULTS: No differences in indices of contractile function, fatiguability, nor recovery were observed between conditions in mouse soleus muscles. Further, no human participants improved performance in a 4-km cycling time trial in response to traditional nor augmented ischemic preconditioning compared to control or exercise conditions (CON 407.7 ± 41.1 s, IPC 411.6 ± 41.9 s, ERG 408.8 ± 41.4 s, AUG 414.1 ± 41.9 s). CONCLUSIONS: Our findings do not support the conferral of ergogenic benefit via a humoral component of IPC at the intracellular level. Ischemic preconditioning may not manifest prominently at submaximal exercise intensities, and augmented ischemic preconditioning may have a hormetic relationship with performance improvements.

Cardiac autonomic recovery following traditional and augmented remote ischemic preconditioning.

PURPOSE: While the possible ergogenic benefits of remote ischemic preconditioning (RIPC) make it an attractive training modality, the mechanisms of action remain unclear. Alterations in neural tone have been demonstrated in conjunction with circulatory occlusion, yet investigation of the autonomic nervous system following RIPC treatment has received little attention. We sought to characterize alterations in autonomic balance to both RIPC and augmented RIPC (RIPCaug) performed while cycling, using acute and sustained autonomic indices. METHODS: Thirteen participants (8M:5F) recorded baseline waking heart rate variability (HRV) for 5 days prior to treatment. Participants then completed control exercise (CON), RIPC, and RIPCaug interventions in a randomized cross-over design. Cardiovascular measurements were recorded immediately before and after each intervention at rest, and during an orthostatic challenge. Waking HRV was repeated the morning after each intervention. RESULTS: RIPC resulted in acutely reduced resting heart rates (HR) (∆ - 4 ± 6 bpm, P = 0.02) and suppressed HR 30 s following the orthostatic challenge compared to CON (64 ± 10 vs 74 ± 9 bpm, P = 0.003). RIPCaug yielded elevated HRs compared to CON and RIPC prior to (P = 0.003) and during the orthostatic challenge (P = 0.002). RIPCaug reduced LnSDNN (Baseline 4.39 ± 0.27; CON 4.44 ± 0.39; RIPC 4.41 ± 0.34; RIPCaug 4.22 ± 0.29, P = 0.02) and LnHfa power (Baseline 7.82 ± 0.54; CON 7.73 ± 1.11; RIPC 7.89 ± 0.78; RIPCaug 7.23 ± 0.87, P = 0.04) the morning after treatment compared to all other conditions. CONCLUSIONS: Our data suggest that RIPC may influence HR acutely, possibly through a reduction in cardiac sympathetic activity, and that RIPCaug reduces HRV through cardiac vagal withdrawal or increased cardiac sympathetic modulation, with alterations persisting until the following morning. These findings imply a dose-response relationship with potential for optimization of performance.

Simulated weight reduction using an anti-gravity treadmill - A pilot study of the impact of weight loss on foot and ankle arthritis.

BACKGROUND: Despite a paucity of evidence, obesity is frequently cited as an exacerbator of symptoms in foot and ankle arthritis. The aims of the current study were to determine whether simulated weight loss would improve symptoms in obese patients with foot and ankle arthritis. METHODS: Patients walked on an "anti-gravity" treadmill allowing simulated weight reduction. Pain was recorded at baseline weight and then compared with pain at simulated normal BMI. RESULTS: Simulated reduction to BMI 25 caused a significant reduction in pain. Mean pain scores improved from baseline to BMI 25 by 32% (15.9 points, p=0.04). Paired analysis showed a significant improvement in pain scores (p=0.016) from BMI of 30 to 25. CONCLUSION: Simulated weight loss from high to normal BMI improved arthritic symptoms. This could be used to power future studies to further investigate the effects of weight loss in foot and ankle patients. Level of evidence Level II - repeated measures cohort study.

The z-spectrum from human blood at 7T.

Chemical Exchange Saturation Transfer (CEST) has been used to assess healthy and pathological tissue in both animals and humans. However, the CEST signal from blood has not been fully assessed. This paper presents the CEST and nuclear Overhauser enhancement (NOE) signals detected in human blood measured via z-spectrum analysis. We assessed the effects of blood oxygenation levels, haematocrit, cell structure and pH upon the z-spectrum in ex vivo human blood for different saturation powers at 7T. The data were analysed using Lorentzian difference (LD) model fitting and AREX (to compensate for changes in T1), which have been successfully used to study CEST effects in vivo. Full Bloch-McConnell fitting was also performed to provide an initial estimate of exchange rates and transverse relaxation rates of the various pools. CEST and NOE signals were observed at 3.5 ppm, -1.7 ppm and -3.5 ppm and were found to originate primarily from the red blood cells (RBCs), although the amide proton transfer (APT) CEST effect, and NOEs showed no dependence upon oxygenation levels. Upon lysing, the APT and NOE signals fell significantly. Different pH levels in blood resulted in changes in both the APT and NOE (at -3.5 ppm), which suggests that this NOE signal is in part an exchange relayed process. These results will be important for assessing in vivo z-spectra.

Dual-domain accelerated MRI reconstruction using transformers with learning-based undersampling.

Acceleration in MRI has garnered much attention from the deep-learning community in recent years, particularly for imaging large anatomical volumes such as the abdomen or moving targets such as the heart. A variety of deep learning approaches have been investigated, with most existing works using convolutional neural network (CNN)-based architectures as the reconstruction backbone, paired with fixed, rather than learned, k-space undersampling patterns. In both image domain and k-space, CNN-based architectures may not be optimal for reconstruction due to its limited ability to capture long-range dependencies. Furthermore, fixed undersampling patterns, despite ease of implementation, may not lead to optimal reconstruction. Lastly, few deep learning models to date have leveraged temporal correlation across dynamic MRI data to improve reconstruction. To address these gaps, we present a dual-domain (image and k-space), transformer-based reconstruction network, paired with learning-based undersampling that accepts temporally correlated sequences of MRI images for dynamic reconstruction. We call our model DuDReTLU-net. We train the network end-to-end against fully sampled ground truth dataset. Human cardiac CINE images undersampled at different factors (5-100) were tested. Reconstructed images were assessed both visually and quantitatively via the structural similarity index, mean squared error, and peak signal-to-noise. Experimental results show superior performance of DuDReTLU-net over state-of-the-art methods (LOUPE, k-t SLR, BM3D-MRI) in accelerated MRI reconstruction; ablation studies show that transformer-based reconstruction outperformed CNN-based reconstruction in both image domain and k-space; dual-domain reconstruction architectures outperformed single-domain reconstruction architectures regardless of reconstruction backbone (CNN or transformer); and dynamic sequence input leads to more accurate reconstructions than single frame input. We expect our results to encourage further research in the use of dual-domain architectures, transformer-based architectures, and learning-based undersampling, in the setting of accelerated MRI reconstruction. The code for this project is made freely available at https://github.com/william2343/dual-domain-mri-recon-nets (Hong et al., 2022).