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Mitochondrial respiration using the oxygraph-2k respirometer (Oroboros) is widely used to estimate mitochondrial capacity in human skeletal muscle. Here, we measured mitochondrial respiration variability, in a relatively large sample, and for the first time, using statistical simulations, we provide the sample size required to detect meaningful respiration changes following lifestyle intervention. Muscle biopsies were taken from healthy, young men from the Gene SMART cohort, at multiple time points. We utilized samples for each measurement with two technical repeats using two respirometer chambers (n = 160 pairs of same muscle after removal of low-quality samples). We measured the Technical Error of measurement (TE M ) and the coefficient of variation (CV) for each mitochondrial complex. There was a high correlation between measurements from the two chambers (R > 0.7 P < .001) for all complexes, but the TE M was large (7.9-27 pmol s -1  mg -1 ; complex dependent), and the CV was >15% for all complexes. We performed statistical simulations of a range of effect sizes at 80% power and found that 75 participants (with duplicate measurements) are required to detect a 6% change in mitochondrial respiration after an intervention, while for interventions with 11% effect size, ~24 participants are sufficient. The high variability in respiration suggests that the typical sample sizes in exercise studies may not be sufficient to capture exercise-induced changes., (© 2020 The Authors. The FASEB Journal published by Wiley Periodicals, Inc. on behalf of Federation of American Societies for Experimental Biology.)

Introduction: Osteocalcin (OC) is used as a surrogate marker for bone turnover in clinical settings. As bone mineral density (BMD) is largely heritable, we tested the hypothesis that a) bone-associated genetic variants previously identified in Genome-Wide Association Studies (GWAS) and combined into a genetic risk score (GRS) are associated with a) circulating levels of OC and b) the changes in OC following acute exercise., Methods: Total OC (tOC), undercarboxylated OC (ucOC), and carboxylated OC (cOC) were measured in serum of 73 healthy Caucasian males at baseline and after a single bout of high-intensity interval exercise. In addition, genotyping was conducted targeting GWAS variants previously reported to be associated with BMD and then combined into a GRS. Potential associations between the GRS and tOC, ucOC and cOC were tested with linear regressions adjusted for age., Results: At baseline none of the individual SNPs associated with tOC, ucOC and cOC. However, when combined, a higher GRS was associated with higher tOC (β = 0.193 ng/mL; p = 0.037; 95% CI = 0.012, 0.361) and cOC (β = 0.188 ng/mL; p = 0.04; 95% CI = 0.004, 0.433). Following exercise, GRS was associated with ucOC levels, (β = 3.864 ng/mL; p-value = 0.008; 95% CI = 1.063, 6.664) but not with tOC or cOC., Conclusion: Screening for genetic variations may assist in identifying people at risk for abnormal circulating levels of OC at baseline/rest. Genetic variations in BMD predicted the ucOC response to acute exercise indicating that physiological functional response to exercise may be influenced by bone-related gene variants., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Angiotensin-converting enzyme (ACE) is expressed in human skeletal muscle. The ACE I/D polymorphism has been associated with athletic performance in some studies. Studies have suggested that the ACE I/D gene variant is associated with ACE enzyme content in serum, and there is an interaction between ACE and uncoupling proteins 2 and 3 (UCP2 and UCP3). However, no studies have explored the effect of ACE I/D on ACE, UCP2, and UCP3 protein content in human skeletal muscle. Utilizing the Gene SMART cohort ( n = 81), we investigated whether the ACE I/D gene variant is associated with ACE enzyme content in blood and ACE, UCP2, and UCP3 protein content in skeletal muscle at baseline and following a session of high-intensity interval exercise (HIIE). Using a stringent and robust statistical analyses, we found that the ACE I/D gene variant was associated with ACE enzyme content in blood ( P < 0.005) at baseline but not the ACE, UCP2, and UCP3 protein content in muscle at baseline. A single session of HIIE tended (0.005 < P < 0.05) to increase blood ACE content immediately postexercise, whereas muscle ACE protein content was lower 3 h after a single session of HIIE ( P < 0.005). Muscle UCP3 protein content decreased immediately after a single session of HIIE ( P < 0.005) and remained low 3 h postexercise. However, those changes in the muscle were not genotype dependent. In conclusion, The ACE I/D gene variant predicts ACE enzyme content in blood but not the ACE, UCP2, and UCP3 protein content of human skeletal muscle. NEW & NOTEWORTHY This paper describes the association between ACE I/D gene variant and ACE protein content in blood and ACE, UCP2, and UCP3 protein content in skeletal muscle at baseline and after exercise in a large cohort of healthy males. Our data suggest that ACE I/D is a strong predictor of blood ACE content but not muscle ACE content.

The gene SMART (genes and the Skeletal Muscle Adaptive Response to Training) Study aims to identify genetic variants that predict the response to both a single session of High-Intensity Interval Exercise (HIIE) and to four weeks of High-Intensity Interval Training (HIIT). While the training and testing centre is located at Victoria University, Melbourne, three other centres have been launched at Bond University, Queensland University of Technology, Australia, and the University of Brighton, UK. Currently 39 participants have already completed the study and the overall aim is to recruit 200 moderately-trained, healthy Caucasians participants (all males 18-45 y, BMI < 30). Participants will undergo exercise testing and exercise training by an identical exercise program. Dietary habits will be assessed by questionnaire and dietitian consultation. Activity history is assessed by questionnaire and current activity level is assessed by an activity monitor. Skeletal muscle biopsies and blood samples will be collected before, immediately after and 3 h post HIIE, with the fourth resting biopsy and blood sample taken after four weeks of supervised HIIT (3 training sessions per week). Each session consists of eight to fourteen 2-min intervals performed at the pre-training lactate threshold (LT) power plus 40 to 70% of the difference between pre-training lactate threshold (LT) and peak aerobic power (W peak ). A number of muscle and blood analyses will be performed, including (but not limited to) genotyping, mitochondrial respiration, transcriptomics, protein expression analyses, and enzyme activity. The participants serve as their own controls. Even though the gene SMART study is tightly controlled, our preliminary findings still indicate considerable individual variability in both performance (in-vivo) and muscle (in-situ) adaptations to similar training. More participants are required to allow us to better investigate potential underlying genetic and molecular mechanisms responsible for this individual variability.

There is a large individual variation in the bone remodelling markers (BRMs) osteocalcin (OC), procollagen 1 N-terminal propeptide (P1NP) and β-isomerized C-terminal telopeptide (β-CTx), as well as undercarboxylated osteocalcin (ucOC), at rest and in response to exercise. α-actinin-3 (ACTN3), a sarcomeric protein, is expressed in skeletal muscle and osteoblasts and may influence BRM levels and the cross-talk between muscle and bone. We tested the levels of serum BRMs in α-actinin-3 deficient humans (ACTN3 XX) at baseline, and following a single bout of exercise. Forty-three healthy Caucasian individuals were divided into three groups (ACTN3 XX, n=13; ACTN3 RX, n=16; ACTN3 RR, n=14). Participants completed a single session of High Intensity Interval Exercise (HIIE) on a cycle ergometer (8×2-min intervals at 85% of maximal power). Blood samples were taken before, immediately after, and three hours post exercise to identify the peak changes in serum BRMs. There was a stepwise increase in resting serum BRMs across the ACTN3 genotypes (XX>RX>RR) with significantly higher levels of tOC ~26%, P1NP ~34%, and β-CTX (~33%) in those with ACTN3 XX compared to ACTN3 RR. Following exercise BRMs and ucOC were higher in all three ACTN3 genotypes, with no significant differences between groups. Serum levels of tOC, P1NP and β-CTX are higher in men with ACTN3 XX genotype (α-actinin-3 deficiency) compared to RR and RX. It appears that the response of BRMs and ucOC to exercise is not explained by the ACTN3 genotype., (Copyright © 2017. Published by Elsevier Inc.)

This article examines changes in the influence of several key actors (state and university officials, board members, and hospital and medical school administrators) in management and policy decisions for university hospitals (UHs). We propose that the decreasing influence of external actors in UH decision making and the increasing influence of UH and medical school actors as well as UH-medical school goal consensus will be related to higher levels of UH performance. Data are drawn from a national sample of 52 UHs that participated in a study of UH decision making in 1981 and 1985. Results indicate that state and university actors lost influence in UH policy decisions between 1981 and 1985, while actors internal to academic health centers (AHCs) gained influence in such decisions. The data indicate a similar trend, although not as strong, regarding influence in UH management decisions. Results from regression analyses indicate that decreasing levels of external influence on UH decision making are related to UH effectiveness, but increasing levels of AHC influence and goal consensus have weak or inconsistent relationships with UH effectiveness. Implications for improving the performance of UHs are discussed.

One of the most significant problem areas for publicly owned hospitals is the administration and control of support services. The location of the control of these services affects their responsiveness and efficiency.

A new way of defining nursing assignment patterns, consistent with current work in studies of organizations, is presented. An instrument has been devised which allows direct measurement of the assignment pattern in use on a patient unit. Although current instrumentation requires continued refinement, the instrument has been used successfully on a dozen patients units to characterize the assignment pattern and appears to be both practical and superior to more conventional methods of characterizing assignment patterns which are now in use.

In response to opportunities and threats in their environments, academic health centers (AHCs) are making important changes in their structure. Several AHCs have legally separated their university hospital from the university. In contrast, other AHCs are linking the university hospital more closely to the medical school by concentrating authority for key decisions in the office of an AHC executive. This article draws from a national study of AHCs and examines the advantages and disadvantages of such changes in AHC structure. An important reason for these changes is maximizing revenues from patient care; an important consequence is the increased salience of patient care among the multiple purposes of AHCs.