Critical steps in the isolation and expansion of adipose-derived stem cells for translational therapy.

Since the discovery of adipose-derived stem cells (ASCs), there have been high expectations of their putative clinical use. Recent advances support these expectations, and it is expected that the transition from pre-clinical and clinical studies to implementation as a standard treatment modality is imminent. However ASCs must be isolated and expanded according to good manufacturing practice guidelines and a basic assurance of quality, safety, and medical effectiveness is needed for authorisation by regulatory agencies, such as European Medicines Agency and US Food and Drug Administration. In this review, a collection of studies investigating the influence of different steps of the isolation and expansion protocol on the yield and functionality of ASCs has been presented in an attempt to come up with best recommendations that ensure potential beneficial clinical outcome of using ASCs in any therapeutic setting. If the findings confirm the initial observations of beneficial effects of ASCs, the path is paved for implementing these ASC-based therapies as standard treatment options.

Blood flow restricted and traditional resistance training performed to fatigue produce equal muscle hypertrophy.

This study investigated the hypertrophic potential of load-matched blood-flow restricted resistance training (BFR) vs free-flow traditional resistance training (low-load TRT) performed to fatigue. Ten healthy young subjects performed unilateral BFR and contralateral low-load TRT elbow flexor dumbbell curl with 40% of one repetition maximum until volitional concentric failure 3 days per week for 6 weeks. Prior to and at 3 (post-3) and 10 (post-10) days post-training, magnetic resonance imaging (MRI) was used to estimate elbow flexor muscle volume and muscle water content accumulation through training. Acute changes in muscle thickness following an early vs a late exercise bout were measured with ultrasound to determine muscle swelling during the immediate 0-48 h post-exercise. Total work was threefold lower for BFR compared with low-load TRT (P < 0.001). Both BRF and low-load TRT increased muscle volume by approximately 12% at post-3 and post-10 (P < 0.01) with no changes in MRI-determined water content. Training increased muscle thickness during the immediate 48 h post-exercise (P < 0.001) and to greater extent with BRF (P < 0.05) in the early training phase. In conclusion, BFR and low-load TRT, when performed to fatigue, produce equal muscle hypertrophy, which may partly rely on transient exercise-induced increases in muscle water content.

Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments.

The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures alpha-helix, beta-strand, and coil. The networks are designed using a priori knowledge of amino acid properties with respect to the secondary structure and the characteristic periodicity in alpha-helices. Since these single-structure networks all have less than 600 adjustable weights, overfitting is avoided. To obtain a three-state prediction of alpha-helix, beta-strand, or coil, ensembles of single-structure networks are combined with another neural network. This method gives an overall prediction accuracy of 66.3% when using 7-fold cross-validation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins increases the prediction accuracy significantly to 71.3% with corresponding Matthew's correlation coefficients C alpha = 0.59, C beta = 0.52, and Cc = 0.50. More than 72% of the residues in the database are predicted with an accuracy of 80%. It is shown that the network outputs can be interpreted as estimated probabilities of correct prediction, and, therefore, these numbers indicate which residues are predicted with high confidence.

[Use of a mask as an alternative to endotracheal intubation in acute respiratory failure]. / Maskebehandling som alternativ til endotrakeal intubasjon ved akutt respirasjonssvikt.

Acute respiratory failure that is unresponsive to medical treatment requires ventilatory support. Non-invasive treatment with a nose or face mask is an alternative to endotracheal intubation in awake and cooperative patients. In patients with acute cardiogenic pulmonary oedema the application of continuous positive airway pressure (CPAP) increases cardiac output. CPAP-treatment is also effective in acute hypercapnic respiratory failure and oxygenation difficulties, but in these conditions pressure or volume-cycled positive pressure ventilation with a mask as an interface often has more to offer. The method reduces the need for endotracheal intubation and hence the time spent by the patient in the intensive care unit, but the need for personnel resources is equal. Possible clinical problems are skin erosions on the bridge of the nose due to pressure from the mask, and systemic hypotension.

[Yellow nail syndrome. Lymphatic vessel hypoplasia as a cause of pleural effusion]. / Gul negl-syndromet. Lymfekarhypoplasi som årsak til pleuravaeske.

Yellow nail syndrome is a clinical entity characterized by lymphedema, nail dystrophy and exudative pleural effusion. The cause of the syndrome is thought to be hypoplasia of the lymphatic vessels, with local infection or inflammation as a common precipitating event. Treatment is symptomatic: chemical or surgical induced pleurodesis is the best way of controlling symptomatic pleural effusion.

Hidden neural networks.

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear performance gains compared to standard HMMs tested on the same task.