The use of serum paraoxonase-1 to assess inflammation in horses with colitis.

Background: Paraoxonase-1 (PON-1) has been suggested as a marker of inflammation and oxidative stress in horses and could potentially be used for prognostication in horses with colitis.

Objectives: Assessment of PON-1 in horses with colitis and comparison of two methods.

Methods: Serum PON-1 was measured by two methods (paraoxon and p-nitrophenyl acetate) in 161 horses with colitis and 57 controls.

Approaching the Spin-Statistical Limit in Visible-to-Ultraviolet Photon Upconversion.

Triplet-triplet annihilation photon upconversion (TTA-UC) is a process in which triplet excitons combine to form emissive singlets and holds great promise in biological applications and for improving the spectral match in solar energy conversion. While high TTA-UC quantum yields have been reported for, for example, red-to-green TTA-UC systems, there are only a few examples of visible-to-ultraviolet (UV) transformations in which the quantum yield reaches 10%. In this study, we investigate the performance of six annihilators when paired with the sensitizer 2,3,5,6-tetra(9-carbazol-9-yl)benzonitrile (4CzBN), a purely organic compound that exhibits thermally activated delayed fluorescence.

Growth-enhanced salmon modify stream ecosystem functioning.

Use of fast-growing domesticated and/or genetically modified strains of fish is becoming increasingly common in aquaculture, increasing the likelihood of deliberate or accidental introductions into the wild. To date, their ecological impacts on ecosystems remain to be quantified. Here, using a controlled phenotype manipulation by implanting growth hormone in juvenile Atlantic salmon (Salmo salar), we found that growth-enhanced fish display changes in several phenotypic traits known to be important for ecosystem functioning, such as habitat use, morphology and excretion rate.

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm.

Background: Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some prognostic value. We hypothesised that cumulative information obtained during the first three days of intensive care could produce a reliable model for predicting neurological outcome following out-of-hospital cardiac arrest (OHCA) using artificial neural network (ANN) with and without biomarkers.