Efficacy of a new technique - INtubate-RECruit-SURfactant-Extubate - "IN-REC-SUR-E" - in preterm neonates with respiratory distress syndrome: Study protocol for a randomized controlled trial

Background: Although beneficial in clinical practice, the INtubate-SURfactant-Extubate (IN-SUR-E) method is not successful in all preterm neonates with respiratory distress syndrome, with a reported failure rate ranging from 19 to 69 %. One of the possible mechanisms responsible for the unsuccessful IN-SUR-E method, requiring subsequent re-intubation and mechanical ventilation, is the inability of the preterm lung to achieve and maintain an "optimal" functional residual capacity. The importance of lung recruitment before surfactant administration has been demonstrated in animal studies showing that recruitment leads to a more homogeneous surfactant distribution within the lungs. Therefore, the aim of this study is to compare the application of a recruitment maneuver using the high-frequency oscillatory ventilation (HFOV) modality just before the surfactant administration followed by rapid extubation (INtubate-RECruit-SURfactant-Extubate: IN-REC-SUR-E) with IN-SUR-E alone in spontaneously breathing preterm infants requiring nasal continuous positive airway pressure (nCPAP) as initial respiratory support and reaching pre-defined CPAP failure criteria. Methods/design: In this study, 206 spontaneously breathing infants born at 24+0-27+6 weeks' gestation and failing nCPAP during the first 24 h of life, will be randomized to receive an HFOV recruitment maneuver (IN-REC-SUR-E) or no recruitment maneuver (IN-SUR-E) just prior to surfactant administration followed by prompt extubation. The primary outcome is the need for mechanical ventilation within the first 3 days of life. Infants in both groups will be considered to have reached the primary outcome when they are not extubated within 30 min after surfactant administration or when they meet the nCPAP failure criteria after extubation. Discussion: From all available data no definitive evidence exists about a positive effect of recruitment before surfactant instillation, but a rationale exists for testing the following hypothesis: a lung recruitment maneuver performed with a step-by-step Continuous Distending Pressure increase during High-Frequency Oscillatory Ventilation (and not with a sustained inflation) could have a positive effects in terms of improved surfactant distribution and consequent its major efficacy in preterm newborns with respiratory distress syndrome. This represents our challenge. Trial registration: ClinicalTrials.gov identifier: NCT02482766. Registered on 1 June 2015

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Relationship between the neonatal white blood cell count and histologic chorioamnionitis in preterm newborns.

Objective: The aim was to examine the relationship between neonatal white blood cell (WBC) count and the diagnosis of histologic chorioamnionitis (HCA). Design: We measured WBC, a widely used marker of inflammation, to evaluate whether the values at birth were associated with HCA. Setting: NICU, Department of Pediatrics of Padua University, Padua, Italy. Subjects: WBC count was evaluated in 71 preterm neonates (< 32 weeks of gestation) with HCA and in a control group without HCA on day 1, 3, and 6 after delivery. Logistic regression analysis and diagnostic accuracy analysis were used to assess the association between WBC counts and HCA. Main results: WBC levels were significantly higher in infants with HCA than in those without HCA (Median IQR, WBC (x10(9)/l): day 1, 13.2 (6.2-21.8) vs 8.1 (6-11.4), p < 0.001; day 3, 17.4 (11.4-26.9) vs 6.3 (5.2-8.3), p < 0.001; day 6, 18.4 (11.1-31) vs 6.5 (4.4-9), p < 0.0001). The neonatal WBC count on the third day of life was the most sensitive parameter associated with HCA (sensitivity: 0.80; specificity: 0.88). The cut-off value based on the ROC curve was 10 (x10(9)/l). Conclusions: WBC count in the third day of life is strongly associated with HCA

Relationship between the neonatal white blood cell count and histologic chorioamnionitis in preterm newborns.

OBJECTIVE: The aim was to examine the relationship between neonatal white blood cell (WBC) count and the diagnosis of histologic chorioamnionitis (HCA). DESIGN: We measured WBC, a widely used marker of inflammation, to evaluate whether the values at birth were associated with HCA. Setting: NICU, Department of Pediatrics of Padua University, Padua, Italy. SUBJECTS: WBC count was evaluated in 71 preterm neonates (<32 weeks of gestation) with HCA and in a control group without HCA on day 1, 3, and 6 after delivery. Logistic regression analysis and diagnostic accuracy analysis were used to assess the association between WBC counts and HCA. MAIN RESULTS: WBC levels were significantly higher in infants with HCA than in those without HCA (Median IQR, WBC (x10(9)/l): day 1, 13.2 (6.2-21.8) vs 8.1 (6-11.4), p < 0.001; day 3, 17.4 (11.4-26.9) vs 6.3 (5.2-8.3), p < 0.001; day 6, 18.4 (11.1-31) vs 6.5 (4.4-9), p < 0.0001). The neonatal WBC count on the third day of life was the most sensitive parameter associated with HCA (sensitivity: 0.80; specificity: 0.88). The cut-off value based on the ROC curve was 10 (x10(9)/l). CONCLUSIONS: WBC count in the third day of life is strongly associated with HCA