The maximal oxygen uptake (VO2max) and maximal power output (Pmax) are commonly used parameters to evaluate the endurance fitness status. A connection between exercise and the kynurenine pathway (KP), which describes the metabolism of unused tryptophan, has already been reported. However, a potential association of the KP with endurance fitness levels remains unknown. In this study, adolescent competitive athletes performed an exhaustive incremental exercise test. Blood samples were taken before, directly after, and 30 minutes after the end of exercise. Tryptophan (Trp), kynurenine (Kyn) and kynurenic acid (KA) serum levels were determined by high-performance liquid chromatography (HPLC). Forty-four male and 27 female athletes (median age: 16 years) were recruited. During exhaustive exercise tests, Trp initially declined and then increased 30 minutes after discontinuing exercise. Similar findings were observed for Kyn, whereas KA levels behaved inversely. After incremental exhaustive exercise the relative increase of Trp concentrations, termed the tryptophan-recovery-index (TRI), showed a highly significant positive correlation with VO2max and Pmax (r=0.468 and 0.491, p-values <0.001). There was a significant gender-difference with higher levels of all metabolites at all measured time points in male participants. In the present study, a highly significant correlation was found between the TRI and the maximal oxygen uptake in well-trained athletes. The implementation of TRI can therefore be suggested as a biomarker for physical fitness.

BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AIs role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AIs successful clinical implementation. FUNDING: No specific funding was provided for this study.