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Query: UMLS:C0016632 (Fox)
 1,461 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Developing medical devices that resist bacterial attachment and subsequent biofilm formation is highly desirable. In this paper, we report the optimization of the molecular structure and thus material properties of a range of (meth)acrylate copolymers which contain monomers reported to deliver bacterial resistance to surfaces. This optimization allows such monomers to be employed within novel coatings to reduce bacterial attachment to silicone urinary catheters. We show that the flexibility of copolymers can be tuned to match that of the silicone catheter substrate, by copolymerizing these polymers with a lower Tg monomer such that it passes the flexing fatigue tests as coatings upon catheters, that the homopolymers failed. Furthermore, the Tg values of the copolymers are shown to be readily estimated by the Fox equation. The bacterial resistance performance of these copolymers were typically found to be better than the neat silicone or a commercial silver containing hydrogel surface, when the monomer feed contained only 25 v% of the "hit" monomer. The method of initiation (either photo or thermal) was shown not to affect the bacterial resistance of the copolymers. Optimized synthesis conditions to ensure that the correct copolymer composition and to prevent the onset of gelation are detailed.
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PMID:Application of Targeted Molecular and Material Property Optimization to Bacterial Attachment-Resistant (Meth)acrylate Polymers. 2746 41

Fox, JL, Scanlan, AT, and Stanton, R. A review of player monitoring approaches in basketball: current trends and future directions. J Strength Cond Res 31(7): 2021-2029, 2017-Effective monitoring of players in team sports such as basketball requires an understanding of the external demands and internal responses, as they relate to training phases and competition. Monitoring of external demands and internal responses allows coaching staff to determine the dose-response associated with the imposed training load (TL), and subsequently, if players are adequately prepared for competition. This review discusses measures reported in the literature for monitoring the external demands and internal responses of basketball players during training and competition. The external demands of training and competition were primarily monitored using time-motion analysis, with limited use of microtechnology being reported. Internal responses during training were typically measured using hematological markers, heart rate, various TL models, and perceptual responses such as rating of perceived exertion (RPE). Heart rate was the most commonly reported indicator of internal responses during competition with limited reporting of hematological markers or RPE. These findings show a large discrepancy between the reporting of external and internal measures and training and competition demands. Microsensors, however, may be a practical and convenient method of player monitoring in basketball to overcome the limitations associated with current approaches while allowing for external demands and internal responses to be recorded simultaneously. The triaxial accelerometers of microsensors seem well suited for basketball and warrant validation to definitively determine their place in the monitoring of basketball players. Coaching staff should make use of this technology by tracking individual player responses across the annual plan and using real-time monitoring to minimize factors such as fatigue and injury risk.
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PMID:A Review of Player Monitoring Approaches in Basketball: Current Trends and Future Directions. 2844 27

Age-predicted maximal heart rate (APMHR) is an essential measure for healthcare professionals in determining cardiovascular response to exercise testing, exertion, and prescription. Although multiple APMHR prediction equations have been validated for specific populations, the accuracy of each within a general population requires testing. We aimed to determine which APMHR equation (Fox, Gellish, Gulati, Tanaka, Arena, Astrand, Nes, Fairbarn) most accurately predicts max heart rate (HRmax) in a general population. HRmax from 99 graded treadmill exercise tests (GXT) were measured. GXTs ended upon volitional fatigue and were only included for analysis if RER > 1.10. Individual paired t-test were performed to determine if significant differences existed between measured and predicted HRmax, along with root mean square errors for each equation. Bland-Altman plots were constructed to determine agreement between equations and measured HRmax. Significant differences between measured and predicted HRmax were found for the Gulati, Astrand, Nes, and Fairbarn (male) equations (p < 0.05). Bland-Altman plots revealed wide limits of agreement for all nine APMHR equations, suggesting poor agreement between measured and predicted HRmax. Proportional bias indicates that prediction equations under and overestimated HRmax in individuals with lower and higher measured HRmax, respectively, with the exception of the Fox equation. All equations used in this study show poor agreement between measured HRmax and APMHR. The Fox equation may represent the best option for a general population as it is less likely to under or overestimate based on individual HRmax. Individuals should use data from GXTs to determine HRmax when applicable to ensure accuracy.
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PMID:Accuracy of Commonly Used Age-Predicted Maximal Heart Rate Equations. 3304 84