Non-small cell lung cancer patients treated with Anti-PD1 immunotherapy show distinct microbial signatures and metabolic pathways according to progression-free survival and PD-L1 status.

Due to the high variance in response rates concerning anti-PD1 immunotherapy (IT), there is an unmet need to discover innovative biomarkers to predict immune checkpoint inhibitor (ICI)-efficacy. Our study included 62 Caucasian advanced-stage non-small cell lung cancer (NSCLC) patients treated with anti-PD1 ICI. Gut bacterial signatures were evaluated by metagenomic sequencing and correlated with progression-free survival (PFS), PD-L1 expression and other clinicopathological parameters. We confirmed the predictive role of PFS-related key bacteria with multivariate statistical models (Lasso- and Cox-regression) and validated on an additional patient cohort (n = 60). We find that alpha-diversity showed no significant difference in any comparison. However, there was a significant difference in beta-diversity between patients with long- (>6 months) vs. short (≤6 months) PFS and between chemotherapy (CHT)-treated vs. CHT-naive cases. Short PFS was associated with increased abundance of Firmicutes (F) and Actinobacteria phyla, whereas elevated abundance of Euryarchaeota was specific for low PD-L1 expression. F/Bacteroides (F/B) ratio was significantly increased in patients with short PFS. Multivariate analysis revealed an association between Alistipes shahii, Alistipes finegoldii, Barnesiella visceriola, and long PFS. In contrast, Streptococcus salivarius, Streptococcus vestibularis, and Bifidobacterium breve were associated with short PFS. Using Random Forest machine learning approach, we find that taxonomic profiles performed superiorly in predicting PFS (AUC = 0.74), while metabolic pathways including Amino Acid Synthesis and Fermentation were better predictors of PD-L1 expression (AUC = 0.87). We conclude that specific metagenomic features of the gut microbiome, including bacterial taxonomy and metabolic pathways might be suggestive of ICI efficacy and PD-L1 expression in NSCLC patients.

Football movement profile analysis and creatine kinase relationships in youth national team players.

Objective: Creatine kinase (CK) is widely used as a monitoring tool to make inferences on fatigue and readiness in elite soccer. Previous studies have examined the relationship between CK and GPS parameters, however these metrics may not accurately describe the players' load during soccer-specific movements. Football Movement Profile (FMP) monitoring is a viable option for such purposes, providing solely inertial sensor-based data and categorizing movements according to intensity (very low, low, medium, high) and movement type (running-linear locomotive, dynamic - change of direction or speed). Methods: We investigated the relationship between the FMP distribution of youth (U16-U21) national team soccer players and the absolute day-to-day change in CK. We applied Spearman's correlations, principal component analysis and K-means clustering to classify players' CK responses according to their specific FMP. Results: Moderate to large negative associations were found between very low intensity FMP parameters and CK change (r = -0.43 ± 0.12) while large positive associations were identified between CK change and other FMP metrics (r = 0.62 ± 0.12). Best fitting clustering methods were used to group players depending on their CK sensitivity to FMP values. Principal component analysis explained 83.0% of the variation with a Silhouette score of 0.61 for the 4 clusters. Conclusions: Our results suggest that soccer players can be clustered based on the relationship between FMP measures and the CK change. These findings can help to plan soccer training or recovery sessions according to the desired load on skeletal muscle, as FMP monitoring might bridge the limitations of GPS telemetry.

Positioning in 5G and 6G Networks-A Survey.

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning-indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios.

Quantification of Training Load Relative to Match Load of Youth National Team Soccer Players.

BACKGROUND: Previous studies have examined the training load relative to match load in club settings. The aims of this study were to (1) quantify the external training load relative to match load in days before a subsequent international game and (2) examine the cumulative training load in relation to match load of U-17 national team field soccer players. HYPOTHESIS: Volume and intensity load parameters will vary between trainings; the farthermost trainings have the highest load gradually decreasing toward the match. STUDY DESIGN: Prospective cohort study. LEVEL OF EVIDENCE: Level 4. METHODS: External training load data were collected from 84 youth national team players using global positioning technology between 2016 and 2020. In the national team setting, training load data were obtained from 3 days before the actual match day (MD-3, MD-2, MD-1 days) and analyzed with regard to the number of days up to the game. Volume and intensity parameters were calculated as a percentage of the subsequent match load. RESULTS: Significant differences were found between MD-1 and MD-2, as well as between MD-1 and MD-3 for most volume parameters (P < 0.01; effect sizes [ESs] 0.68-0.99) and high-intensity distance (P < 0.002; ES 0.67 and 0.73) and maximum velocity (P < 0.002; ES 0.82) as intensity parameters. Most cumulative values were significantly different from total duration (P < 0.001, common language ES 0.80-0.96). CONCLUSION: The training volume gradually decreased as match day approached, with the highest volume occurring on MD-3. Intensity variables, such as maximum velocity, high-intensity accelerations, and meterage per minute were larger in MD-1 training relative to match load. Training volume was lowest in MD-1 trainings and highest in MD-3 trainings; intensity however varies between training days. CLINICAL RELEVANCE: The findings of this study may help to understand the special preparational demands of international matches, highlighting the role of decreased training volume and increased intensity.

Factors Influencing Creatine Kinase Response in Youth National Team Soccer Players.

BACKGROUND: Previous studies have examined the relationship between external training load and creatine kinase (CK) response after soccer matches in adults. This study aimed to build training- and match-specific CK prediction models for elite youth national team soccer players. HYPOTHESIS: Training and match load will have different effects on the CK response of elite youth soccer players, and there will be position-specific differences in the most influential external and internal load parameters on the CK response. STUDY DESIGN: Prospective cohort study. LEVEL OF EVIDENCE: Level 4. METHODS: Forty-one U16-U17 youth national team soccer players were measured over an 18-month period. Training and match load were monitored with global positioning system devices. Individual CK values were measured from whole blood every morning in training camps. The dataset consisted of 1563 data points. Clustered prediction models were used to examine the relationship between external/internal load and consecutive CK changes. Clusters were built based on the playing position and activity type. The performance of the linear regression models was described by the R2 and the root-mean-square error (RMSE, U/L for CK values). RESULTS: The prediction models fitted similarly during games and training sessions (R2 = 0.38-0.88 vs 0.6-0.77), but there were large differences based on playing positions. In contrast, the accuracy of the models was better during training sessions (RMSE = 81-135 vs 79-209 U/L). Position-specific differences were also found in the external and internal load parameters, which best explained the CK changes. CONCLUSION: The relationship between external/internal load parameters and CK changes are position specific and might depend on the type of session (training or match). Morning CK values also contributed to the next day's CK values. CLINICAL RELEVANCE: The relationship between position-specific external/internal load and CK changes can be used to individualize postmatch recovery strategies and weekly training periodization with a view to optimize match performance.