Lower Extremity Injury Rates on Artificial Turf Versus Natural Grass Surfaces in the National Football League During the 2021 and 2022 Seasons.

BACKGROUND: It has been argued that the use of artificial turf football fields in the National Football League (NFL) increases player injury risk compared with natural grass surfaces. PURPOSE/HYPOTHESIS: The purpose of this study was to quantify the rate of lower extremity injuries occurring in NFL players on artificial turf compared with natural grass surfaces and characterize the time missed due to injury and proportion of injuries requiring surgery. It was hypothesized that lower extremity injuries requiring surgical intervention would occur at a higher rate on artificial turf than on natural grass. STUDY DESIGN: Descriptive epidemiology study. METHODS: Lower extremity injury data for the 2021 and 2022 NFL seasons were obtained using publicly available records. Data collected included injury type, player position, player age, playing surface, weeks missed due to injury, and whether the patient underwent season-ending or minor surgery. Multivariable logistic regression was performed to determine the risk of season-ending surgery according to playing surface. RESULTS: When combining injuries for the 2021 and 2022 seasons (N = 718 injuries), the incidence rate of lower extremity injury was 1.22 injuries/game for natural grass and 1.42 injuries/game for artificial turf. The odds of a season-ending surgery were found to be significantly higher on artificial turf compared with natural grass (odds ratio = 1.60; 95% CI, 1.28-1.99; P < .05), while additional variables, including weather, age, position, week of injury occurrence, and history of prior injury, did not influence the odds of season-ending surgery. CONCLUSION: The 2021 and 2022 NFL seasons of our analysis demonstrated a higher incidence rate of injuries on artificial turf surfaces compared with natural grass surfaces. In addition, the odds of injury requiring season-ending surgery were found to be significantly higher on artificial turf compared with natural grass

Fragility of overactive bladder medication clinical trials: A systematic review

PurposeOveractive bladder (OAB) syndrome significantly impairs quality of life, often necessitating pharmacological interventions with associated risks. The fragility of OAB trial outcomes, as measured by the fragility index (FI: smallest number of event changes to reverse statistical significance) and quotient (FQ: FI divided by total sample size expressed as a percentage), is critical yet unstudied.Materials and methodsWe conducted a systematic search for randomized controlled trials on OAB medications published between January 2000 and August 2023. Inclusion criteria were trials with two parallel arms reporting binary outcomes related to OAB medications. We extracted trial details, outcomes, and statistical tests employed. We calculated FI and FQ, analyzing associations with trial characteristics through linear regression.ResultsWe included 57 trials with a median sample size of 211 participants and a 12% median lost to follow-up. Most studies investigated anticholinergics (37/57, 65%). The median FI/FQ was 5/3.5%. Larger trials were less fragile (median FI 8; FQ 1.0%) compared to medium (FI: 4; FQ 2.5%) and small trials (FI: 4; FQ 8.3%). Double-blinded studies exhibited higher FQs (median 2.9%) than unblinded trials (6.7%). Primary and secondary outcomes had higher FIs (median 5 and 6, respectively) than adverse events (FI: 4). Each increase in 10 participants was associated with a +0.19 increase in FI (p < 0.001).ConclusionsA change in outcome for a median of five participants, or 3.5% of the total sample size, could reverse the direction of statistical significance in OAB trials. Studies with larger sample sizes and efficacy outcomes from blinded trials were less fragile

Lower Extremity Injury Rates on Artificial Turf Versus Natural Grass Surfaces in the National Football League During the 2021 and 2022 Seasons

BACKGROUND: It has been argued that the use of artificial turf football fields in the National Football League (NFL) increases player injury risk compared with natural grass surfaces. PURPOSE/HYPOTHESIS: The purpose of this study was to quantify the rate of lower extremity injuries occurring in NFL players on artificial turf compared with natural grass surfaces and characterize the time missed due to injury and proportion of injuries requiring surgery. It was hypothesized that lower extremity injuries requiring surgical intervention would occur at a higher rate on artificial turf than on natural grass. STUDY DESIGN: Descriptive epidemiology study. METHODS: Lower extremity injury data for the 2021 and 2022 NFL seasons were obtained using publicly available records. Data collected included injury type, player position, player age, playing surface, weeks missed due to injury, and whether the patient underwent season-ending or minor surgery. Multivariable logistic regression was performed to determine the risk of season-ending surgery according to playing surface. RESULTS: When combining injuries for the 2021 and 2022 seasons (N = 718 injuries), the incidence rate of lower extremity injury was 1.22 injuries/game for natural grass and 1.42 injuries/game for artificial turf. The odds of a season-ending surgery were found to be significantly higher on artificial turf compared with natural grass (odds ratio = 1.60; 95% CI, 1.28-1.99; CONCLUSION: The 2021 and 2022 NFL seasons of our analysis demonstrated a higher incidence rate of injuries on artificial turf surfaces compared with natural grass surfaces. In addition, the odds of injury requiring season-ending surgery were found to be significantly higher on artificial turf compared with natural grass

Efficacy of Cipargamin (KAE609) in a Randomized, Phase II Dose-Escalation Study in Adults in Sub-Saharan Africa With Uncomplicated Plasmodium falciparum Malaria.

BACKGROUND: Cipargamin (KAE609) is a potent antimalarial in a phase II trial. Here we report efficacy, pharmacokinetics, and resistance marker analysis across a range of cipargamin doses. These were secondary endpoints from a study primarily conducted to assess the hepatic safety of cipargamin (hepatic safety data are reported elsewhere). METHODS: This phase II, multicenter, randomized, open-label, dose-escalation trial was conducted in sub-Saharan Africa in adults with uncomplicated Plasmodium falciparum malaria. Cipargamin monotherapy was given as single doses up to 150 mg or up to 50 mg once daily for 3 days, with artemether-lumefantrine as control. Key efficacy endpoints were parasite clearance time (PCT), and polymerase chain reaction (PCR)-corrected and uncorrected adequate clinical and parasitological response (ACPR) at 14 and 28 days. Pharmacokinetics and molecular markers of drug resistance were also assessed. RESULTS: All single or multiple cipargamin doses ≥50 mg were associated with rapid parasite clearance, with median PCT of 8 hours versus 24 hours for artemether-lumefantrine. PCR-corrected ACPR at 14 and 28 days was >75% and 65%, respectively, for each cipargamin dose. A treatment-emerging mutation in the Pfatp4 gene, G358S, was detected in 65% of treatment failures. Pharmacokinetic parameters were consistent with previous data, and approximately dose proportional. CONCLUSIONS: Cipargamin, at single doses of 50 to 150 mg, was associated with very rapid parasite clearance, PCR-corrected ACPR at 28 days of >65% in adults with uncomplicated P. falciparum malaria, and recrudescent parasites frequently harbored a treatment-emerging mutation. Cipargamin will be further developed with a suitable combination partner. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov (NCT03334747)

Evaluation of conservation practices effect on water quality using the SWAT model

The deterioration of water quality due to human-driven alternations has an adverse effect on the environment. More than 50% of surveyed surface water bodies in the United States (US) are classified as impaired waters as per the Clean Water Act. The pollutants affecting the water quality in the US are classified as point and non-point sources. Pollutant mitigation strategies such as the selective implementation of best management practices (BMPs) based on the severity of the pollution could improve water quality by reducing the amounts of pollutants. Quantifying the efficiency of a specific management practice can be difficult for large watersheds. Complex hydrologic models are used to assess water quality and quantity at watershed scales. This study used a Soil and Water Assessment Tool (SWAT) that can simulate a longer time series for hydrologic and water quality assessments in the Yazoo River Watershed (YRW). This research aims to estimate streamflow, sediment, and nutrient load reductions by implementing various BMPs in the watershed. BMPs such as vegetative filter strips (VFS), riparian buffers, and cover crops were applied in this study. Results from these scenarios indicated that the combination of VFS and riparian buffers at the watershed scale had the highest reduction in sediment and nutrient loads. Correspondingly, a comparative analysis of BMP implementation at the field and watershed scale showed the variability in the reduction of streamflow, sediment, and nutrient loads. The results indicated that combining VFS and CC at the field scale watershed had a greater nutrient reduction than at the watershed scale. Likewise, this study investigated the soil-specific sediment load assessments for predominant soils in the YRW, which resulted in soil types of Alligator, Sharkey, and Memphis soils being highly erodible from the agricultural-dominant region. This study also included the effect of historical land use and land-cover (LULC) change on water quality. The analysis revealed that there was a significant decrease in pastureland and a simultaneous increase in forest and wetlands, which showed a decreasing trend in hydrologic and water quality outputs. Results from this study could be beneficial in decision-making for prescribing appropriate conservation practice

Machine Learning Approach for Forecasting the Sales of Truck Components

Context: The context of this research is to forecast the sales of truck componentsusing machine learning algorithms which can help the organization in activity oftrade and business and it also plays a major role for firms in decision-making operationsin the areas corresponding to sales, production, purchasing, finance, and accounting. Objectives: This study first investigates to find the suitable machine learning algorithmsthat can be used to forecast the sales of truck components and then theexperiment is performed with the chosen algorithms to forecast the sales and to evaluatethe performances of the chosen machine learning algorithms. Methods: Firstly, a Literature review is used to find suitable machine learningalgorithms and then based on the results obtained, an experiment is performed toevaluate the performances of machine learning algorithms. Results: Results from the literature review shown that regression algorithms namely Supports Vector Machine Regression, Ridge Regression, Gradient Boosting Regression, and Random Forest Regression are suitable algorithms and results from theexperiment showed that Ridge Regression has performed well than the other machine learning algorithms for the chosen dataset. Conclusion: After the experimentation and the analysis, the Ridge regression algorithmhas been performed well when compared with the performances of the otheralgorithms and therefore, Ridge Regression is chosen as the optimal algorithm forperforming the sales forecasting of truck components for the chosen data

Machine Learning Approach for Forecasting the Sales of Truck Components

Context: The context of this research is to forecast the sales of truck componentsusing machine learning algorithms which can help the organization in activity oftrade and business and it also plays a major role for firms in decision-making operationsin the areas corresponding to sales, production, purchasing, finance, and accounting. Objectives: This study first investigates to find the suitable machine learning algorithmsthat can be used to forecast the sales of truck components and then theexperiment is performed with the chosen algorithms to forecast the sales and to evaluatethe performances of the chosen machine learning algorithms. Methods: Firstly, a Literature review is used to find suitable machine learningalgorithms and then based on the results obtained, an experiment is performed toevaluate the performances of machine learning algorithms. Results: Results from the literature review shown that regression algorithms namely Supports Vector Machine Regression, Ridge Regression, Gradient Boosting Regression, and Random Forest Regression are suitable algorithms and results from theexperiment showed that Ridge Regression has performed well than the other machine learning algorithms for the chosen dataset. Conclusion: After the experimentation and the analysis, the Ridge regression algorithmhas been performed well when compared with the performances of the otheralgorithms and therefore, Ridge Regression is chosen as the optimal algorithm forperforming the sales forecasting of truck components for the chosen data