The concept of encapsulated-cell therapy is very appealing, but in practice a great deal of technology and know-how is needed for the production of long-term functional transplants. Alginate is one of the most promising biomaterials for immunoisolation of allogeneic and xenogeneic cells and tissues (such as Langerhans islets). Although great advances in alginate-based cell encapsulation have been reported, several improvements need to be made before routine clinical applications can be considered. Among these is the production of purified alginates with consistently high transplantation-grade quality. This depends to a great extent on the purity of the input algal source as well as on the development of alginate extraction and purification processes that can be validated. A key engineering challenge in designing immunoisolating alginate-based microcapsules is that of maintaining unimpeded exchange of nutrients, oxygen and therapeutic factors (released by the encapsulated cells), while simultaneously avoiding swelling and subsequent rupture of the microcapsules. This requires the development of efficient, validated and well-documented technology for cross-linking alginates with divalent cations. Clinical applications also require validated technology for long-term cryopreservation of encapsulated cells to maintaining a product inventory in order to meet end-user demands. As shown here these demands could be met by the development of novel, validated technologies for production of transplantation-grade alginate and microcapsule engineering and storage. The advances in alginate-based therapy are demonstrated by transplantation of encapsulated rat and human islet grafts that functioned properly for about 1 year in diabetic mice.

We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spiderfearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia.

The design of safe stimulation protocols for functional electrostimulation requires knowledge of the “maximum reversible charge injection capacity” of the implantable microelectrodes. One of the main difficulties encountered in characterizing such microelectrodes is the calculation of the access voltage Va. This paper proposes a method to calculate Va that does not require prior knowledge of the overpotential terms and of the electrolyte (or excitable tissue) resistance, which is an advantage for in vivo electrochemical characterization of microelectrodes. To validate this method, we compare the calculated results with those obtained from conventional methods for characterizing three flexible platinum microelectrodes by cyclic voltammetry and voltage transient measurements. This paper presents the experimental setup, the required instrumentation, and the signal processing.

Animal testing plays a vital role in biomedical research. Stress reduction is important for improving research results and increasing the welfare and the quality of life of laboratory animals. To estimate stress we believe it is of great importance to develop non-invasive techniques for monitoring physiological signals during the transport of laboratory animals, thereby allowing the gathering of information on the transport conditions, and, eventually, the improvement of these conditions. Here, we study the suitability of commercially available electric potential integrated circuit (EPIC) sensors, using both contact and contactless techniques, for monitoring the heart rate and breathing rate of non-restrained, non-sedated laboratory mice. The design has been tested under different scenarios with the aim of checking the plausibility of performing contactless capture of mouse heart activity (ideally with an electrocardiogram). First experimental results are shown.

The only widely used and accepted method for long-term cell preservation is storage below -130 degrees C. The biosciences will make increasing use of preservation and place new demands on it. Currently, cells are frozen in volumes greater than 1 ml but the new cell and implantation therapies (particularly those using stem cells) will require accurately defined freezing and storage conditions for each single cell. Broadly-based, routine freezing of biological samples allows the advantage of retrospective analysis and the possibility of saving genetic rights. For such applications, one billion is a modest estimation for the number of samples. Current cryotechniques cannot handle so many samples in an efficient and economic way, and the need for new cryotechnology is evident. The interdisciplinary approach presented here should lead to a new sample storage and operating strategy that fulfils the needs mentioned above. Fundamental principles of this new kind of smart sample storage are: (i) miniaturisation; (ii) modularisation; (iii) informationsample integration, i.e. freezing memory chips with samples; and (iv) physical and logical access to samples and information without thawing the samples. In contrast to current sample systems, the prototyped family of intelligent cryosubstrates allows the recovery of single wells (parts) of the substrate without thawing the rest of the sample. The development of intelligent cryosubstrates is linked to developments in high throughput freezing, high packing density storage and minimisation of cytotoxic protective agents.