Submaximal oxygen uptake efficiency slope as a predictor of VO2max in men with cardiovascular disease

Purpose: Although V̇O2 max is considered the gold standard measure of cardiorespiratory fitness (CRF), it can be difficult to attain in patients with cardiovascular disease (CVD). The submaximal oxygen uptake efficiency slope (OUES) integrates cardiovascular, musculoskeletal and respiratory function during incremental exercise into a single index and has been proposed as an alternative and effort independent measure of cardiopulmonary reserve (Baba et al., 1996). The purpose of this study was to examine the relation between V̇O2 max and both submaximal absolute OUES and relative OUES (OUES.kg-1). Methods: A total of 55 men ((mean ± SD) age, 59.08 ± 9.03 yr; VO2 max, 1.94 ± 0.53 L.min-1and 22.73 ± 5.95 mL.kg-1.min-1) were recruited during induction to a community based exercise referral program following completion of phase 2 cardiac rehabilitation. Participants performed a graded exercise test on a cycle ergometer with breath-by-breath open circuit spirometry and a 12 lead ECG. Absolute OUES and OUES.kg-1 were calculated by plotting VO2 in mL.min-1 on the x-axis, and the log transformed VE on the y-axis (VO2 = a log 10 VE + b). Exercise data up to the ventilatory anaerobic threshold (VAT) was included in the analysis. Results: The %V̇O2 max corresponding to the VAT was 55.72 ± 11.81. Absolute OUES and OUES.kg-1 were 2164.42 ± 540.96 and 25.28 ± 5.99, respectively. There was a significant positive correlation between V̇O2 max (L.min-1) and OUES (r= 0.775; p<0.001) and between V̇O2 max (mL.kg-1.min-1) and OUES.kg-1 (r= 0.78; p<0.001). Conclusion: Determination of V̇O2 max is not often feasible in individuals with CVD where maximal exercise testing is contraindicated or when performance may be impaired by pain, dyspnea or angina. The findings from the present study indicate that the OUES and OUES.kg-1 are significantly related to absolute and relative V̇O2 max, respectively and may be used as a valid sub maximal effort independent measure of CRF

Relation between physical activity and oxygen uptake efficiency in men with CVD

Purpose: The oxygen uptake efficiency slope (OUES) represents the rate of increase in V̇O2 in response to a given V̇E during incremental exercise, indicating how effectively oxygen is taken in by the lungs, transported and used in the periphery. OUES, calculated using only submaximal exercise data is identical to the OUES calculated over the entire duration of a cardiopulmonary exercise test (CEPT) , and both maximal and submaximal OUE are significantly related to cardiorespiratory fitness (CRF) measured as V̇O2peak. Currently, little research has been published on how physical activity (PA) assessed by accelerometers is related to submaximal and maximal OUES. The purpose of this study was to determine the relation light (LIPA), moderate (MIPA) and vigorous (VIPA) intensity physical activity and maximal and submaximal OUES in men with cardiovascular disease (CVD). Methods: A total of 56 men (mean ( SD): age of 59.3 ± 9.2 yr., V̇O2 peak (L/min) 2.0 0.50, V̇O2 peak (mL/kg/min) 23.6 5.8, were recruited during an induction to a community-based exercise referral program following completion of phase 2 cardiac rehabilitation program. Participants underwent a graded exercise test on a cycle ergometer with breath by breath open circuit spirometry after which they wore a wrist worn accelerometer (Actigraph) for 7 d. Absolute and relative submaximal and maximal OUES were calculated by plotting V̇O2 in mL/min on the x axis, and the log transformed VE on the y axis (V̇O2 = a log 10 VE + b). Exercise data up to the ventilatory anaerobic threshold and maximal exercise were used to calculate submaximal and maximal OUE, respectively. Results: Participants performed 584.49 73.87 min of daily LIPA, 145.45 60.85 min of MIPA and no daily min of VIPA. There was a significant relation between absolute submaximal OUES (r=0.386; p<0.01), submaximal OUES/Kg (r=0.296; p<0.05) and LIPA. There was a significant relation between maximal OUES (r=0.286; p<0.05), maximal OUES/Kg (r=0.279; p<0.05) and MIPA. Conclusion: Submaximal and maximal OUE are related to levels of LIPA and MIPA, respectively. Submaximal OUES can potentially be used as an objective, effort independent test to estimate LIPA levels among men with CVD

Physical activity patterns and cardiorespiratory fitness in men with cardiovascular disease

Purpose: Cardiorespiratory fitness (CRF) is generally regarded as an objective and reproducible measure of recent habitual physical activity (PA). Considering that the majority of daily PA is performed at light intensity, it is likely that CRF benefits will be detected at submaximal rather than maximal exercise. The purpose of this study was to evaluate daily minutes of light (LIPA), moderate (MIPA) and vigorous (VIPA) intensity physical activity among men with cardiovascular disease (CVD), and to determine the relation between PA and submaximal (oxygen uptake efficiency slope (OUES)) and maximal (V̇O2 peak) indices of CRF. Methods: A total 32 male participants (mean ( SD): age of 60.0 ± 8.7 yr, V̇O2 peak (L/min) 2.0 0.45, V̇O2 peak (mL/kg/min) 23.3 5.7, were recruited during an induction to a community based exercise referral program following completion of phase 2 cardiac rehabilitation. Participants underwent a graded exercise test on a cycle ergometer with breath by breath open circuit spirometry after which they wore a wrist worn accelerometer (Actigraph) for 7 d. Absolute and relative submaximal OUES were calculated by plotting V̇O2 in mL/min on the x axis, and the log transformed VE on the y axis (V̇O2 = a log 10 VE + b). Exercise data up to the ventilatory anaerobic threshold was included in the analysis. Results: Participants performed 589.05 69.41 min of daily LIPA, 161.38 66.16 min of MIPA and no daily min of VIPA. There was no significant relation between peak V̇O2 and either LIPA or MIPA. There was a significant correlation between submaximal OUES (r=0.44; p<0.01) and LIPA. The relation between submaximal OUES/kg and LIPA min almost reached statistical significance (r=0.33; p<0.07). There was no significant relation between MIPA and OUES or OUES/kg. Conclusion: Men with CVD spend the majority (78%) of their day performing LIPA. OUES, a submaximal measure of CRF was related LIPA whereas no relation was found between V̇O2 peak and LIPA

Decoherence in Josephson Qubits from Dielectric Loss

Dielectric loss from two-level states is shown to be a dominant decoherence source in superconducting quantum bits. Depending on the qubit design, dielectric loss from insulating materials or the tunnel junction can lead to short coherence times. We show that a variety of microwave and qubit measurements are well modeled by loss from resonant absorption of two-level defects. Our results demonstrate that this loss can be significantly reduced by using better dielectrics and fabricating junctions of small area $\lesssim 10 \mu \textrm{m}^2$. With a redesigned phase qubit employing low-loss dielectrics, the energy relaxation rate has been improved by a factor of 20, opening up the possibility of multi-qubit gates and algorithms.Comment: shortened version submitted to PR

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows