Outcome after operative treatment for chronic versus acute Achilles tendon rupture — A comparative analysis

Background

This study compared outcomes after treatment of acute Achilles tendon (AT) rupture via percutaneous suturing, with those after chronic AT rupture treated via open reconstruction.

In vitro studies on hirudin elimination by haemofiltration: comparison of three high-flux membranes.

BACKGROUND: Recombinant hirudin (r-hirudin) is a highly selective thrombin inhibitor used for anticoagulation in heparin-induced thrombocytopenia type II. R-hirudin is increasingly applied to patients with renal failure and on renal replacement therapy. Since kidney function impairment strongly prolongs r-hirudin elimination half-life, severe accumulation and bleeding complications may occur. Data on the r-hirudin permeability and elimination capacity of different haemofilters are limited. METHODS: Three haemofilter types were investigated: high-flux polysulphone (Fresenius), AN69 (Hospal), and polyamide (Gambro). We used two in vitro haemofiltration models: (i) an open post-dilution haemofiltration model with ultrafiltration and fluid substitution (model 1) simulating hirudin intoxication, and (ii) a closed model with ultrafiltrate reinfusion (model 2) to determine steady-state sieving coefficients (SC). Fresh human heparinized blood (2 IU unfractionated heparin/ml blood) was used. In model 2, SC obtained with human whole blood were compared with isotonic saline. RESULTS: In model 1, r-hirudin levels decreased significantly faster with polysulphone than with AN69 or polyamide (P<0.05). In accordance with this, in model 2 the observed SC in whole blood were 1.11+/-0.28 (polysulphone), 0.61+/-0.15 (AN69) and 0.33+/-0.13 (polyamide), and clearances were 28+/-7 (polysulphone), 15+/-4 (AN69) and 8+/-3 ml/min (polyamide) (P<0.001 for all comparisons). The SC in saline were slightly but significantly lower for polysulphone (0.88+/-0.12), similar for AN69 (0.59+/-0.1), and significantly improved for polyamide (0.83+/-0.1). CONCLUSIONS: Elimination of r-hirudin by haemofiltration strongly depended on the membrane material. Using human blood, we observed large differences between the three high-flux membranes. The saline experiments suggest a membrane-dependent impact of plasma proteins and pH on hirudin sieving. Our findings have implications for r-hirudin dosage in haemofiltration, for treatment of overdosage, and for future in vitro haemofiltration studies.     

Color Doppler Visualization of Intramyocardial Coronary Arteries Using a New Echo System: Effect of Contrast Enhancement and Vasodilation

Utilizing a new echocardiographic system, intramyocardial coronary arteries could be demonstrated by color Doppler in a small number of open chest animals. Both intravenous and intracoronary injections of a contrast agent (Levovist) resulted in significant enhancement of color flow signals with an increase in the length, width, and the number of these vessels. Similar results were obtained with intravenous infusion and direct intracoronary injections of a vasodilator (adenosine).     

Deep neural models for color classification and color constancy

Color constancy is our ability to perceive constant colors across varying illuminations. Here, we trained deep neural networks to be color constant and evaluated their performance with varying cues. Inputs to the networks consisted of two-dimensional images of simulated cone excitations derived from three-dimensional (3D) rendered scenes of 2,115 different 3D shapes, with spectral reflectances of 1,600 different Munsell chips, illuminated under 278 different natural illuminations. The models were trained to classify the reflectance of the objects. Testing was done with four new illuminations with equally spaced CIEL*a*b* chromaticities, two along the daylight locus and two orthogonal to it. High levels of color constancy were achieved with different deep neural networks, and constancy was higher along the daylight locus. When gradually removing cues from the scene, constancy decreased. Both ResNets and classical ConvNets of varying degrees of complexity performed well. However, DeepCC, our simplest sequential convolutional network, represented colors along the three color dimensions of human color vision, while ResNets showed a more complex representation.