RESUMO
"Children are not tiny adults" is an adage commonly used in pediatrics to emphasize the fact that children often have different physiological responses to sickness and trauma compared to adults. However, despite widespread acceptance of this concept, diagnostic blood testing is an excellent example of clinical care that is not yet customized to the needs of children, especially newborns. Cumulative blood loss resulting from clinical testing does not typically impact critically ill adult patients, but can quickly escalate in children, leading to iatrogenic anemia and related comorbidities. Moreover, the tests prioritized for rapid, near-patient testing in adults are not always the most clinically relevant tests for children or newborns. This report describes the development of a digital microfluidic testing platform and associated clinical assays purposely curated to address current shortcomings in pediatric laboratory testing by using microliter volumes (<50 µL) of samples. The automated platform consists of a small instrument and single-use cartridges, which contain all reagents necessary to prepare the sample and perform the assay. Electrowetting technology is used to precisely manipulate nanoliter-sized droplets of samples and reagents inside the cartridge. To date, we have automated three disparate types of assays (biochemical assays, immunoassays, and molecular assays) on the platform and have developed over two dozen unique tests, each with important clinical application to newborns and pediatric patients. Cell lysis, plasma preparation, magnetic bead washing, thermocycling, incubation, and many other essential functions were all performed on the cartridge without any user intervention. The resulting assays demonstrate performance comparable to standard clinical laboratory assays and are economical due to the reduced hands-on effort required for each assay and lower overall reagent consumption. These capabilities allow a wide range of assays to be run simultaneously on the same cartridge using significantly reduced sample volumes with results in minutes.
RESUMO
PURPOSE: To use an artificial neural network (ANN) to model the effect of 15 weeks of resistance training on changes in countermovement jump (CMJ) performance in male track-and-field athletes. METHODS: Resistance training volume load (VL) of 21 male division I track-and-field athletes was monitored over the course of 15 weeks, which covered their indoor and outdoor competitive season. Weekly CMJ height was also measured and used to calculate the overall 15-week change in CMJ performance. A feed-forward ANN with 5 hidden layers was used to model how the VL from each of the 15 weeks was associated with the overall change in CMJ height. RESULTS: Testing the performance of the developed ANN on 4 separate athletes showed that 15 weeks of VL data could predict individual changes in CMJ height with an average error between 0.21 and 1.47 cm, which suggested that the ANN adequately modeled the relationship between weekly VL and its effects on CMJ performance. In addition, analysis of the relative importance of each week in predicting changes in CMJ height indicated that the VLs during deload or taper weeks were the best predictors (10%-17%) of changes in CMJ performance. CONCLUSIONS: ANN can be used to effectively model the effects of weekly VL on changes in CMJ performance. In addition, ANN can be used to assess the relative importance of each week in predicting changes in CMJ height.