Engineering of a miniaturized, robotic clinical laboratory.

The ability to perform laboratory testing near the patient and with smaller blood volumes would benefit patients and physicians alike. We describe our design of a miniaturized clinical laboratory system with three components: a hardware platform (ie, the miniLab) that performs preanalytical and analytical processing steps using miniaturized sample manipulation and detection modules, an assay-configurable cartridge that provides consumable materials and assay reagents, and a server that communicates bidirectionally with the miniLab to manage assay-specific protocols and analyze, store, and report results (i.e., the virtual analyzer). The miniLab can detect analytes in blood using multiple methods, including molecular diagnostics, immunoassays, clinical chemistry, and hematology. Analytical performance results show that our qualitative Zika virus assay has a limit of detection of 55 genomic copies/ml. For our anti-herpes simplex virus type 2 immunoglobulin G, lipid panel, and lymphocyte subset panel assays, the miniLab has low imprecision, and method comparison results agree well with those from the United States Food and Drug Administration-cleared devices. With its small footprint and versatility, the miniLab has the potential to provide testing of a range of analytes in decentralized locations.

Deformable Registration of Biomedical Images Using 2D Hidden Markov Models.

Robust registration of unimodal and multimodal images is a key task in biomedical image analysis, and is often utilized as an initial step on which subsequent analysis techniques critically depend. We propose a novel probabilistic framework, based on a variant of the 2D hidden Markov model, namely, the turbo hidden Markov model, to capture the deformation between pairs of images. The hidden Markov model is tailored to capture spatial transformations across images via state transitions, and modality-specific data costs via emission probabilities. The method is derived for the unimodal setting (where simpler matching metrics may be used) as well as the multimodal setting, where different modalities may provide very different representations for a given class of objects, necessitating the use of advanced similarity measures. We utilize a rich model with hundreds of model parameters to describe the deformation relationships across such modalities. We also introduce a local edge-adaptive constraint to allow for varying degrees of smoothness between object boundaries and homogeneous regions. The parameters of the described method are estimated in a principled manner from training data via maximum likelihood learning, and the deformation is subsequently estimated using an efficient dynamic programming algorithm. Experimental results demonstrate the improved performance of the proposed approach over the state-of-the-art deformable registration techniques, on both unimodal and multimodal biomedical data sets.

Eczema herpeticum in a wrestler.

Eczema herpeticum (EH), first described in 1887, is characterized by a disseminated skin infection consisting of dome-shaped papules and is associated with fever, lymphadenopathy, and malaise. The condition commonly occurs on the trunk, head, and neck and is associated with numerous skin conditions, including atopic dermatitis. It is a result of a superimposed herpes simplex virus infection on otherwise compromised skin. It is a rapidly spreading infection, and early antiviral treatment is essential. Herpes gladiatorum is a common infection in the wrestling population, and atopic dermatitis is a common skin condition in the general population. Together, these 2 conditions can greatly increase the risk of EH infection. It is important for both sports medicine physicians and dermatologists to be aware of the risk of EH infection in this population, the presenting signs and symptoms, and be ready to respond quickly with antiviral treatment.