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Objectives: To map and summarise the sports coaches' and support staff's perspectives on athlete monitoring to explore the breadth of literature, identify knowledge gaps and inform future research. Design: Scoping review based on the Joanna Briggs Institute (JBI) methodology. Methods: SPORTDiscus, MEDLINE, APA PsycInfo, and Embase databases were searched in English until 6 September 2022. The inclusion criteria were (1) coach(es) and/or support staff were explicitly questioned about their knowledge, perceptions, understanding, opinions, and/or applied practice of athlete monitoring; (2) results could be directly attributed to coach(es) and/or support staff; (3) primary research projects that are available as full-text. Exclusion criteria were applied for grey literature. The data were extracted into a custom-made data charting spreadsheet. Results: From the 4381 identified records, 42 met the eligibility criteria. Almost all the studies were conducted within the Anglosphere and at the national or international level. The main reasons for coaches and support staff to implement athlete monitoring were to reduce injury and illness, inform the training program, and improve or maintain performance. While training load monitoring is generally seen as valuable the coaches and support staff acknowledged that there was no perfect scientific approach to monitoring athletes and believed it should be part of the bigger picture, emphasising communication. Conclusions: There has been a recent surge in research demonstrating that athlete monitoring extends beyond quantitative information and encompasses non-quantified subjective information. This further substantiates that coaches and support staff will remain central to athlete monitoring, even amidst the anticipated technological progress. [ABSTRACT FROM AUTHOR]

Metabolomics is the data-driven science of small molecules. Untargeted metabolomics, using liquid chromatography-high resolution mass spectrometry (LC-HRMS), differs greatly from targeted metabolite assays, using nominal mass LC-MS instruments, as it generates thousands of metabolite features which are not quantified and are identified "post hoc." Thus, a substantial amount of time is dedicated to the data processing workflow. Despite the prevalence of untargeted metabolomics and LC-HRMS in contemporary research, undergraduate education in this area is almost nonexistent. To expose upper-division undergraduate analytical chemistry students to untargeted metabolomics, a realistic laboratory experiment, typical of biomedical research, was developed. In the clinic, hemolysis can result from poor sample handling. In this experiment, students artificially induced hemolysis in horse blood and assessed the resulting metabolomic differences. Using XCMS Online, an open-source online platform, and a guided worksheet, students navigated their processed data, learning how untargeted metabolomics differs from the targeted assays they previously performed. This experiment guided their understanding of key concepts such as the number of metabolite features detected, quality assessment, metabolite identification, and data visualization.

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Microsamples are collections usually less than 50 µL, although all devices that we have captured as part of this review do not fit within this definition (as some can perform collections of up to 600 µL); however, they are considered microsamples that can be self-administered. These microsamples have been introduced in pre-clinical, clinical, and research settings to overcome obstacles in sampling via traditional venepuncture. However, venepuncture remains the sampling gold standard for the metabolic phenotyping of blood. This presents several challenges in metabolic phenotyping workflows: accessibility for individuals in rural and remote areas (due to the need for trained personnel), the unamenable nature to frequent sampling protocols in longitudinal research (for its invasive nature), and sample collection difficulty in the young and elderly. Furthermore, venous sample stability may be compromised when the temperate conditions necessary for cold-chain transport are beyond control. Alternatively, research utilising microsamples extends phenotyping possibilities to inborn errors of metabolism, therapeutic drug monitoring, nutrition, as well as sport and anti-doping. Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into the metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the 'haematocrit effect' in dried blood spot microsampling can be overcome. In this review, we provide an overview of the common uses and workflows for microsampling in metabolic phenotyping research. We discuss the advancements in technologies, highlighting key considerations and remaining knowledge gaps for the employment of microsamples in metabolic phenotyping research. This review supports the translation of research from the 'bench to the community'. [ABSTRACT FROM AUTHOR]

Purpose: To examine the influence of manipulating aerobic contribution after whole-blood removal on pacing patterns, performance, and energy contribution during self-paced middle-distance cycling. Methods: Seven male cyclists (33 ± 8 y) completed an incremental cycling test followed 20 min later by a 4-min self-paced cycling time trial (4MMP) on 6 separate occasions over 42 d. The initial 2 sessions acted as familiarization and baseline testing, after which 470 mL of blood was removed, with the remaining sessions performed 24 h, 7 d, 21 d, and 42 d after blood removal. During all 4MMP trials, power output, oxygen uptake, and aerobic and anaerobic contribution to power were determined. Results: 4MMP average power output significantly decreased by 7% ± 6%, 6% ± 8%, and 4% ± 6% at 24 h, 7 d, and 21 d after blood removal, respectively. Compared with baseline, aerobic contribution during the 4MMP was significantly reduced by 5% ± 4%, 4% ± 5%, and 4% ± 10% at 24 h, 7 d, and 21 d, respectively. The rate of decline in power output on commencement of the 4MMP was significantly attenuated and was 76% ± 20%, 72% ± 24%, and 75% ± 35% lower than baseline at 24 h, 21 d, and 42 d, respectively. Conclusion: Removal of 470 mL of blood reduces aerobic energy contribution, alters pacing patterns, and decreases performance during self-paced cycling. These findings indicate the importance of aerobic energy distribution during self-paced middle-distance events. [ABSTRACT FROM AUTHOR]

New Findings: What is the central question of this study?Does 14 days of live‐high, train‐low simulated altitude alter an individual's metabolomic/metabolic profile?What is the main finding and its importance?This study demonstrated that ∼200 h of moderate simulated altitude exposure resulted in greater variance in measured metabolites between subject than within subject, which indicates individual variability during the adaptive phase to altitude exposure. In addition, metabolomics results indicate that altitude alters multiple metabolic pathways, and the time course of these pathways is different over 14 days of altitude exposure. These findings support previous literature and provide new information on the acute adaptation response to altitude. The purpose of this study was to determine the influence of 14 days of normobaric hypoxic simulated altitude exposure at 3000 m on the human plasma metabolomic profile. For 14 days, 10 well‐trained endurance runners (six men and four women; 29 ± 7 years of age) lived at 3000 m simulated altitude, accumulating 196.4 ± 25.6 h of hypoxic exposure, and trained at ∼600 m. Resting plasma samples were collected at baseline and on days 3 and 14 of altitude exposure and stored at −80°C. Plasma samples were analysed using liquid chromatography–high‐resolution mass spectrometry to construct a metabolite profile of altitude exposure. Mass spectrometry of plasma identified 36 metabolites, of which eight were statistically significant (false discovery rate probability 0.1) from baseline to either day 3 or day 14. Specifically, changes in plasma metabolites relating to amino acid metabolism (tyrosine and proline), glycolysis (adenosine) and purine metabolism (adenosine) were observed during altitude exposure. Principal component canonical variate analysis showed significant discrimination between group means (P < 0.05), with canonical variate 1 describing a non‐linear recovery trajectory from baseline to day 3 and then back to baseline by day 14. Conversely, canonical variate 2 described a weaker non‐recovery trajectory and increase from baseline to day 3, with a further increase from day 3 to 14. The present study demonstrates that metabolomics can be a useful tool to monitor metabolic changes associated with altitude exposure. Furthermore, it is apparent that altitude exposure alters multiple metabolic pathways, and the time course of these changes is different over 14 days of altitude exposure. [ABSTRACT FROM AUTHOR]

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography–mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal–Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management. [ABSTRACT FROM AUTHOR]