Mouse urinary biomarkers provide signatures of maturation, diet, stress level, and diurnal rhythm.

Body fluids such as urine potentially contain a wealth of information pertaining to age, sex, social and reproductive status, physiologic state, and genotype of the donor. To explore whether urine could encode information regarding environment, physiology, and development, we compared the volatile compositions of mouse urine using solid-phase microextraction and gas chromatography-mass spectrometry (SPME-GC/MS). Specifically, we identified volatile organic compounds (VOCs) in individual urine samples taken from inbred C57BL/6J-H-2(b) mice under several experimental conditions-maturation state, diet, stress, and diurnal rhythms, designed to mimic natural variations. Approximately 1000 peaks (i.e., variables) were identified per comparison and of these many were identified as potential differential biomarkers. Consistent with previous findings, we found groups of compounds that vary significantly and consistently rather than a single unique compound to provide a robust signature. We identified over 49 new predictive compounds, in addition to identifying several published compounds, for maturation state, diet, stress, and time-of-day. We found a considerable degree of overlap in the chemicals identified as (potential) biomarkers for each comparison. Chemometric methods indicate that the strong group-related patterns in VOCs provide sufficient information to identify several parameters of natural variations in this strain of mice including their maturation state, stress level, and diet.

Development of a microfluidics-based intracochlear drug delivery device.

BACKGROUND: Direct delivery of drugs and other agents into the inner ear will be important for many emerging therapies, including the treatment of degenerative disorders and guiding regeneration. METHODS: We have taken a microfluidics/MEMS (MicroElectroMechanical Systems) technology approach to develop a fully implantable reciprocating inner-ear drug-delivery system capable of timed and sequenced delivery of agents directly into perilymph of the cochlea. Iterations of the device were tested in guinea pigs to determine the flow characteristics required for safe and effective delivery. For these tests, we used the glutamate receptor blocker DNQX, which alters auditory nerve responses but not cochlear distortion product otoacoustic emissions. RESULTS: We have demonstrated safe and effective delivery of agents into the scala tympani. Equilibration of the drug in the basal turn occurs rapidly (within tens of minutes) and is dependent on reciprocating flow parameters. CONCLUSION: We have described a prototype system for the direct delivery of drugs to the inner ear that has the potential to be a fully implantable means for safe and effective treatment of hearing loss and other diseases.

Variable selection using iterative reformulation of training set models for discrimination of samples: application to gas chromatography/mass spectrometry of mouse urinary metabolites.

The paper discusses variable selection as used in large metabolomic studies, exemplified by mouse urinary gas chromatography of 441 mice in three experiments to detect the influence of age, diet, and stress on their chemosignal. Partial least squares discriminant analysis (PLS-DA) was applied to obtain class models, using a procedure of 20,000 iterations including the bootstrap for model optimization and random splits into test and training sets for validation. Variables are selected using PLS regression coefficients on the training set using an optimized number of components obtained from the bootstrap. The variables are ranked in order of significance, and the overall optimal variables are selected as those that appear as highly significant over 100 different test and training set splits. Cost/benefit analysis of performing the model on a reduced number of variables is also illustrated. This paper provides a strategy for properly validated methods for determining which variables are most significant for discriminating between two groups in large metabolomic data sets avoiding the common pitfall of overfitting if variables are selected on a combined training and test set and also taking into account that different variables may be selected each time the samples are split into training and test sets using iterative procedures.

Fabrication Methods and Performance of Low-Permeability Microfluidic Components for a Miniaturized Wearable Drug Delivery System.

In this paper, we describe low-permeability components of a microfluidic drug delivery system fabricated with versatile micromilling and lamination techniques. The fabrication process uses laminate sheets which are machined using XY milling tables commonly used in the printed-circuit industry. This adaptable platform for polymer microfluidics readily accommodates integration with silicon-based sensors, printed-circuit, and surface-mount technologies. We have used these methods to build components used in a wearable liquid-drug delivery system for in vivo studies. The design, fabrication, and performance of membrane-based fluidic capacitors and manual screw valves provide detailed examples of the capability and limitations of the fabrication method. We demonstrate fluidic capacitances ranging from 0.015 to 0.15 µL/kPa, screw valves with on/off flow ratios greater than 38 000, and a 45× reduction in the aqueous fluid loss rate to the ambient due to permeation through a silicone diaphragm layer.

Autoregressive modeling of analytical sensor data can yield classifiers in the predictor coefficient parameter space.

SUMMARY: The analysis of chromatographic data resulting from complex chemical mixtures is challenging. Components may co-elute, causing their signals to overlap. An algorithm that will increase the signal-to-noise ratio so compounds present in low abundance can be better distinguished from noise is useful in this type of analysis. The autoregressive (AR) filter offers the advantage of smoothing chromatograms to increase this ratio, while also offering data compression and increased resolution. Furthermore, this filter can be useful for classification, as the roots of the predictor coefficient vectors represent features present in the data and can therefore be used for pattern recognition. In this paper, we present a novel method for applying AR filtering to chromatogram data. We show that the AR filter outperforms the Savitzky-Golay filter for smoothing noise while retaining important information within chromatograms, and also that AR correlation coefficients have the potential to be used to classify chromatogram data into groups. CONTACT: cdavis@draper.com.