Comparative characteristics of some methods for estimating energy expenditure in critically ill mechanically ventilated patients

Aim: To compare the energy expenditure (EE) assessed by ventilator-derived carbon dioxide production (EE–VCO2-ventilator) and the energy expenditure calculated from six predictive equations with the gold standard energy expenditure measured with indirect calorimetry (IC) in mechanically ventilated patients. Materials and methods: This is a prospective, non-randomized, one-month study which included six mechanically ventilated patients with FiO2 <60% and PEEP <10 mbar. Thirty-minute measurements were taken using a Cosmed Q-NRG+ metabolic monitor. The average ventilator-derived VCO2 from the Drager Evita Infinity V500 respirator (VʹCO2, ml/min) was calculated for the same period. The IC-measured EE (MEE-IC) was compared with EE–VCO2-ventilator by a formula proposed in ESPEN (8.19×VCO2) and with six predictive equations. Results: Mean MEE-IC was 1650±365 kcal. Mean measured EE–VCO2-ventilator was 1669±340 kcal. A statistically nonsignificant difference was found between the two measurements (p=0.84, correlation coefficient 0.98). Of the predictive equations we compared, the best correlation to the reference method was the Penn State 3 with mean EE of 1679±356 (p=0.81, correlation coefficient of 0.78). Conclusions: In critically ill mechanically ventilated patients, the assessment of EE based on a ventilator-derived VCO2 is an alternative to IC and is more accurate than most predictive equations

Are There Any Land Use Dynamics in the Upper Bistrița Basin, Eastern Carpathians, Romania, in the Period 1990–2021?

This paper aims to assess land use and land use change (LULC). For this purpose, supervised mapping on satellite imagery, using the European Space Agency (ESA) SNAP programme from LANDSAT databases, publicly accessible through the European Copernicus portal, was used. At the same time, an analysis of the degree of landscape fragmentation in the study area was carried out, which revealed that, because of the particular fragmentation of small polygons, the best results were obtained via analysis/supervised mapping on satellite images. This method, once validated in the field, reflects the most accurate land use pattern in the analysed area, with wide applications in studies of agriculture, biodiversity, geography, etc. Between 2000 and 2010, significant changes were registered. Artificial surfaces decreased by approximately 400 ha, showing a negative trend in the last period of the interval. Coniferous forests reached their maximum threshold in 2000 (with 114,400 ha) in conjunction with the “Grassland” class, which exceeded 16,700 ha. In 2010, a drastic decrease in “Grassland” was recorded, reaching half of the values of 1990 and 2000, now having only 15,684 ha. Land cover changes were significant when comparing the period before 1989 with 2021. This fact was due to socio-economic changes in society, in large part caused by changes in professions and the way of life of the population