Determination of the maximum steady state of lactate (MLSS) in saliva: An alternative to blood lactate determination

Based on previous research which shows parallelism between the saliva and blood lactate response during incremental exercise, we hypothesized that a "maximum salivary lactate steady state" (saliva-MLSS) might exist. Thus, the aim of the present investigation was to establish 1) which lower limit for the increase in salivary lactate concentration during a constant workload (i.e., from the 10th to the 20th min) test could be used to determine the saliva-MLSS and 2) if the exercise intensity corresponding to the saliva-MLSS is identical to that evoking the (blood) MLSS. Twelve male amateur athletes of mean (+/-SD) age 24+/-5 year were selected for the study. Based on the results of a previous maximal cycle ergometer test for lactate threshold (LT) determination, each subject performed consecutive constant workload tests of 20-min duration on separate days for MLSS determination, Blood and saliva (25 mu l) samples were collected at 0, 10, and 20 min during the tests for lactate determination. A Student's t-test for paired data demonstrated that a salivary lactate increase of 0.8 mM corresponded to the saliva-MLSS. At this value, indeed, no significant differences were observed between the mean (V) over dot O-2, and W values corresponding to the MLSS and the saliva-MLSS. In conclusion, the present findings indicate that 0.8 mM is the lower limit for the increase in saliva lactate concentration during a constant load test and thus is that which might be used as a reference to determine saliva-MLSS. Furthermore, saliva-MLSS might be used as an alternative to MLSS determination in blood samples.

Fully smart cities: Analysis of the city to Smart City transformation process

Ever since the birth of the Smart City paradigm, a wide variety of initiatives have sprung up involving this phenomenon: best practices, projects, pilot projects, transformation plans, models, standards, indicators, measuring systems, etc. The question to ask, applicable to any government official, city planner or researcher, is whether this effect is being felt in how cities are transforming, or whether, in contrast, it is not very realistic to speak of cities imbued with this level of intelligence. Many cities are eager to define themselves as smart, but the variety, complexity and scope of the projects needed for this transformation indicate that the change process is longer than it seems. If our goal is to carry out a comparative analysis of this progress among cities by using the number of projects executed and their scope as a reference for the transformation, we could find such a task inconsequential due to the huge differences and characteristics that define a city. We believe that the subject needs simplification (simpler, more practical models) and a new approach. This paper presents a detailed analysis of the smart city transformation process in Spain and provides a support model that helps us understand the changes and the speed at which they are being implemented. To this end we define a set of elements of change called "transformation factors" that group a city's smartness into one of three levels (Low/Medium/Fully) and more homogeneously identify the level of advancement of this process. © 2016 IEEE.