Highlights d Astronauts have shorter telomeres than ground controls before and after spaceflight d Inter-individual differences identified in astronaut telomere length after spaceflight d Chronic oxidative stress during spaceflight correlates with telomere length dynamics d Increased frequencies of chromosomal inversions observed during and after spaceflight

The ability to predict a cancer patient’s response to radiotherapy and risk of developing adverse late health effects would greatly improve personalized treatment regimens and individual outcomes. Telomeres represent a compelling biomarker of individual radiosensitivity and risk, as exposure can result in dysfunctional telomere pathologies that coincidentally overlap with many radiation-induced late effects, ranging from degenerative conditions like fibrosis and cardiovascular disease to proliferative pathologies like cancer. Here, telomere length was longitudinally assessed in a cohort of fifteen prostate cancer patients undergoing Intensity Modulated Radiation Therapy (IMRT) utilizing Telomere Fluorescence in situ Hybridization (Telo-FISH). To evaluate genome instability and enhance predictions for individual patient risk of secondary malignancy, chromosome aberrations were assessed utilizing directional Genomic Hybridization (dGH) for high-resolution inversion detection. We present the first implementation of individual telomere length data in a machine learning model, XGBoost, trained on pre-radiotherapy (baseline) and in vitro exposed (4 Gy γ-rays) telomere length measurements, to predict post radiotherapy telomeric outcomes, which together with chromosomal instability provide insight into individual radiosensitivity and risk for radiation-induced late effects.

The aim of this study was to determine if an abnormal HRV status would have negative effects on simulated individual time trial (ITT) performance in recreational cyclists. Recreational male (n=23, 42.8±8.3 years, 78.0±11.0 kg) and female (n=2, 37.0±6.8 years, 68.0±4.4 kg) cyclists completed simulated indoor 40-minute ITTs (40TT) over ten weeks. Participants were asked to complete simulated 40TTs under two HRV conditions: HRV normal values and HRV abnormal values. Participants recorded daily morning HRV readings to determine HRV status. Each participant performed all 40TTs on their personal indoor bike trainer and bike without external race simulation (e.g., Zwift). All cycling performance data were recorded on personal bike computers and submitted via a Qualtrics survey. A total of 138 ITTs (Normal = 75; Abnormal = 63) were assessed for relationships between HRV status and performance outcomes using a linear mixed-effects model with Cohen’s D for effect sizes (ES). A significant main effect of HRV status was found for peak power (F = 6.61; Normal: 372 ± 121.5 watts; Abnormal: 349 ± 105.9 watts; 380; p = 0.01; ES = 0.20) and peak speed (F = 6.12; Normal = 10.8 ± 1.2 m/s; Abnormal: 10.4 ± 1.2 m/s; p = 0.02; ES = 0.33). No significant main effect or effect sizes exceeding 0.20 were observed for all other performance variables. Daily HRV monitoring provides valuable insight that an individual’s peak power and speed may be compromised during cycling performance despite no changes in physiological or psychological indicators of effort. Coaches and cyclists can use morning HRV to inform race strategy ensuring desired performance outcomes, especially for those who rely on high power outputs

The ability to predict responses of cancer patients to radiotherapy and risks of developing adverse late health effects would greatly improve personalized treatment regimens and individual outcomes. Telomeres represent a compelling biomarker of individual radiosensitivity and risk, as exposure can result in dysfunctional telomere pathologies that coincidentally overlap with many radiation-induced late effects, ranging from degenerative conditions like fibrosis and cardiovascular disease to proliferative pathologies like cancer. Here, telomere length was longitudinally assessed in a cohort of fifteen prostate cancer patients undergoing Intensity Modulated Radiation Therapy (IMRT) utilizing Telomere Fluorescence in situ Hybridization (Telo-FISH). To evaluate genome instability and enhance predictions for individual patient risk of secondary malignancy, chromosome aberrations were also assessed utilizing directional Genomic Hybridization (dGH) for high-resolution inversion detection. We present the first implementation of individual telomere length data in a machine learning model, XGBoost, trained on pre-radiotherapy (baseline) and in vitro exposed (4 Gy γ-rays) telomere length measures, to predict post-radiotherapy telomeric outcomes, which together with chromosomal instability provide insight into individual radiosensitivity and risk for radiation-induced late effects.2 Introduction: