MOESM1 of Associations between bolus infusion of hydrocortisone, glycemic variability and insulin infusion rate variability in critically Ill patients under moderate glycemic control

Additional file 1. Flow chart for blood glucose control and Tables S1–S4 containing the results for the post hoc analysis

Respiratory parameters in an animal model of lung injury during treatment with heliox ventilation (N=8 per group).

<div><p>Data are MEAN ± SEM. VILI is marked by open triangles; LP ventilation is marked by filled circles. Heliox ventilation is marked by a disconnected line and oxygen/air by a continuous line. Comparisons are between heliox and oxygen within the VILI or the LP group. *: P < 0.05; **: P < 0.01; ***: P < 0.001.</p> <p>(A) Minute volume ventilation (mL min^-1); (B) respiratory rate (breaths per min); (C) inspiratory pressure (cm H₂O); (D ) mean airway pressure (cm H₂O); (E ) tidal volume (mL kg^-1); (F) A-a gradient; (G) dead space (mmHg) and (H) compliance (mL cm H₂O ^-1). </p></div

Inflammatory parameters in an animal model of ventilator–induced lung injury (N=8 per group), treated with heliox ventilation.

<div><p>Heliox is marked by white bars and oxygen/air by grey bars. Data are MEAN ± SEM. *: P < 0.05; **: P < 0.01. </p> <p>(A) Protein levels; (B) IL- 6 levels; (C) CINC–3 levels and (D) cell count in bronchoalveolar lavage fluid. </p></div

Analysis of the reduced wake effect for available wind power calculation during curtailment

With the increase of installed wind power capacity, the contribution of wind power curtailment to power balancing becomes more relevant. Determining the available power during curtailment at the wind farm level is not trivial, as curtailment changes the wake effects in a wind farm. Current best practice to estimate the available power is to sum the available power calculated by every wind turbine. However, during curtailment the changed local wind conditions at the wind turbines lead to inaccurate results at the wind farm level. This paper presents an algorithm to determine the available power of a wind farm during curtailment. Moreover, results of curtailment experiments are discussed that were performed on nearshore wind farm Westermeerwind to validate the algorithm. For the case where a single turbine is being curtailed, it is shown that the algorithm reduces the estimation error for the first downstream turbine significantly. Further development of the algorithm is required for accurate estimation of the second turbine. All further downstream turbines did not experience a change in wake conditions.Wind Energ

Early Prediction of Intensive Care Unit–Acquired Weakness Using Easily Available Parameters: A Prospective Observational Study

<div><p>Introduction</p><p>An early diagnosis of Intensive Care Unit–acquired weakness (ICU–AW) using muscle strength assessment is not possible in most critically ill patients. We hypothesized that development of ICU–AW can be predicted reliably two days after ICU admission, using patient characteristics, early available clinical parameters, laboratory results and use of medication as parameters.</p><p>Methods</p><p>Newly admitted ICU patients mechanically ventilated ≥2 days were included in this prospective observational cohort study. Manual muscle strength was measured according to the Medical Research Council (MRC) scale, when patients were awake and attentive. ICU–AW was defined as an average MRC score <4. A prediction model was developed by selecting predictors from an a–priori defined set of candidate predictors, based on known risk factors. Discriminative performance of the prediction model was evaluated, validated internally and compared to the APACHE IV and SOFA score.</p><p>Results</p><p>Of 212 included patients, 103 developed ICU–AW. Highest lactate levels, treatment with any aminoglycoside in the first two days after admission and age were selected as predictors. The area under the receiver operating characteristic curve of the prediction model was 0.71 after internal validation. The new prediction model improved discrimination compared to the APACHE IV and the SOFA score.</p><p>Conclusion</p><p>The new early prediction model for ICU–AW using a set of 3 easily available parameters has fair discriminative performance. This model needs external validation.</p></div

MOESM1 of Iron metabolism in critically ill patients developing anemia of inflammation: a case control study

Additional file 1: Table S1. Definition of patient groups including sampling days

MOESM1 of Diagnostic accuracy of quantitative neuromuscular ultrasound for the diagnosis of intensive care unit-acquired weakness: a cross-sectional observational study

Additional file 1: Figure E1. Muscle measurements predefined measurement sites. Table E1. Muscle measurements predefined measurement sites. Figure E2. Ultrasound image of the rectus femoris. Table E2. Regression model formulas for normal values of muscle thickness and muscle echo intensity. Table E3. Characteristics of new healthy control cohort. Figure E3. Muscle thickness and echo intensity

Study flowchart.

<p>ICU-AW: Intensive Care Unit – acquired weakness; OHCA: out-of hospital cardiac arrest; mRankin: modified Rankin score; NMD: neuromuscular disorder; MRC: muscle strength as assessed with Medical Research Council scale.</p

Construction of prediction model.

<p>Construction of prediction model.</p

Patient and admission characteristics.

<p>Patient and admission characteristics.</p