Bioluminescent sensor proteins for point-of-care therapeutic drug monitoring.

For many drugs, finding the balance between efficacy and toxicity requires monitoring their concentrations in the patient's blood. Quantifying drug levels at the bedside or at home would have advantages in terms of therapeutic outcome and convenience, but current techniques require the setting of a diagnostic laboratory. We have developed semisynthetic bioluminescent sensors that permit precise measurements of drug concentrations in patient samples by spotting minimal volumes on paper and recording the signal using a simple point-and-shoot camera. Our sensors have a modular design consisting of a protein-based and a synthetic part and can be engineered to selectively recognize a wide range of drugs, including immunosuppressants, antiepileptics, anticancer agents and antiarrhythmics. This low-cost point-of-care method could make therapies safer, increase the convenience of doctors and patients and make therapeutic drug monitoring available in regions with poor infrastructure.

Handheld-assisted field data collection for occupational risk assessment.

A computerized handheld procedure is presented in this paper. It is intended as a database complementary tool, to enhance prospective risk analysis in the field of occupational health. The Pendragon forms software (version 3.2) has been used to implement acquisition procedures on Personal Digital Assistants (PDAs) and to transfer data to a computer in an MS-Access format. The data acquisition strategy proposed relies on the risk assessment method practiced at the Institute of Occupational Health Sciences (IST). It involves the use of a systematic hazard list and semi-quantitative risk assessment scales. A set of 7 modular forms has been developed to cover the basic need of field audits. Despite the minor drawbacks observed, the results obtained so far show that handhelds are adequate to support field risk assessment and follow-up activities. Further improvements must still be made in order to increase the tool effectiveness and field adequacy.

Development of the First Disease Activity Index for Eosinophilic Esophagitis: Update on the EEsAI in 2011

Background a nd Aims: T he international E EsAI study g roupis currently developing the first activity index (EEsAI) specificfor Eosinophilic Esophagitis (EoE). Goal: To develop, evaluateand validate the EEsAI.Methods: T he d evelopment comprises three phases: 1.Selection of candidate items; 2. Evaluation of the activity indexin a f irst patient cohort; and 3. V alidation in a s econd EoEpatient cohort. Focus group interviews with patients were usedin p hase 1 to generate p atient r eported outcomes ( PRO)according to guidelines o f regulatory authorities ( FDA andEMA), whereas the section of biologic items was developed byDelphi r ounds of i nternational E oE experts from E urope andNorth America.Results: The EEsAI has a modular composition to assess thefollowing components o f EoE activity: p atient reportedoutcomes, endoscopic activity, histologic activity, laboratoryactivity, a nd quality of life. D efinitions f or all aspects o fendoscopic and histologic appearance were established byconsensus rounds among EoE experts. Symptom assessmenttools were created that take into account d ifferent foodconsistencies as w ell as f ood avoidance and specificprocessing strategies. T he EEsAI is evaluated in a c ohort ofadult EoE patients since March 2011.Conclusions: After successful validation, the EEsAI will allowto standardize outcome assessment in E oE t rials which w illlikely lead to its wide applicability.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.