Framework for multivariate selectivity analysis, part I: theoretical and practical merits

PGC-1α regulates the interplay between oxidative stress, senescence and autophagy in the ageing retina important in age-related macular degeneration

We previously showed that mice with knockout in the peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A) gene encoding the PGC-1α protein, and nuclear factor erythroid 2 like 2 (NFE2L2) gene, exhibited some features of the age-related macular degeneration (AMD) phenotype. To further explore the mechanism behind the involvement of PGC-1α in AMD pathogenesis we used young (3-month) and old (12-month) mice with knockout in the PPARGC1A gene and age-matched wild-type (WT) animals. An immunohistochemical analysis showed age-dependent different expression of markers of oxidative stress defence, senescence and autophagy in the retinal pigment epithelium of KO animals as compared with their WT counterparts. Multivariate inference testing showed that senescence and autophagy proteins had the greatest impact on the discrimination between KO and WT 3-month animals, but proteins of antioxidant defence also contributed to that discrimination. A bioinformatic analysis showed that PGC-1α might coordinate the interplay between genes encoding proteins involved in antioxidant defence, senescence and autophagy in the ageing retina. These data support importance of PGC-1α in AMD pathogenesis and confirm the utility of mice with PGC-1α knockout as an animal model to study AMD pathogenesis.

Review of Ethanol Intoxication Sensing Technologies and Techniques

The field of alcohol intoxication sensing is over 100 years old, spanning the fields of medicine, chemistry, and computer science, aiming to produce the most effective and accurate methods of quantifying intoxication levels. This review presents the development and the current state of alcohol intoxication quantifying devices and techniques, separated into six major categories: estimates, breath alcohol devices, bodily fluid testing, transdermal sensors, mathematical algorithms, and optical techniques. Each of these categories was researched by analyzing their respective performances and drawbacks. We found that the major developments in monitoring ethanol intoxication levels aim at noninvasive transdermal/optical methods for personal monitoring. Many of the "categories" of ethanol intoxication systems overlap with each other with to a varying extent, hence the division of categories is based only on the principal operation of the techniques described in this review. In summary, the gold-standard method for measuring blood ethanol levels is through gas chromatography. Early estimation methods based on mathematical equations are largely popular in forensic fields. Breath alcohol devices are the most common type of alcohol sensors on the market and are generally implemented in law enforcement. Transdermal sensors vary largely in their sensing methodologies, but they mostly follow the principle of electrical sensing or enzymatic reaction rate. Optical devices and methodologies perform well, with some cases outperforming breath alcohol devices in terms of the precision of measurement. Other estimation algorithms consider multimodal approaches and should not be considered alcohol sensing devices, but rather as prospective measurement of the intoxication influence. This review found 38 unique technologies and techniques for measuring alcohol intoxication, which is testament to the acute interest in the innovation of noninvasive technologies for assessing intoxication.

Orthogonality constrained inverse regression to improve model selectivity and analyte predictions from vibrational spectroscopic measurements

In analytical chemistry spectroscopy is attractive for high-throughput quantification, which often relies on inverse regression, like partial least squares regression. Due to a multivariate nature of spectroscopic measurements an analyte can be quantified in presence of interferences. However, if the model is not fully selective against interferences, analyte predictions may be biased. The degree of model selectivity against an interferent is defined by the inner relation between the regression vector and the pure interfering signal. If the regression vector is orthogonal to the signal, this inner relation equals zero and the model is fully selective. The degree of model selectivity largely depends on calibration data quality. Strong correlations may deteriorate calibration data resulting in poorly selective models. We show this using a fructose-maltose model system. Furthermore, we modify the NIPALS algorithm to improve model selectivity when calibration data are deteriorated. This modification is done by incorporating a projection matrix into the algorithm, which constrains regression vector estimation to the null-space of known interfering signals. This way known interfering signals are handled, while unknown signals are accounted for by latent variables. We test the modified algorithm and compare it to the conventional NIPALS algorithm using both simulated and industrial process data. The industrial process data consist of mid-infrared measurements obtained on mixtures of beta-lactoglobulin (analyte of interest), and alpha-lactalbumin and caseinoglycomacropeptide (interfering species). The root mean squared error of beta-lactoglobulin (% w/w) predictions of a test set was 0.92 and 0.33 when applying the conventional and the modified NIPALS algorithm, respectively. Our modification of the algorithm returns simpler models with improved selectivity and analyte predictions. This paper shows how known interfering signals may be utilized in a direct fashion, while benefitting from a latent variable approach. The modified algorithm can be viewed as a fusion between ordinary least squares regression and partial least squares regression and may be very useful when knowledge of some (but not all) interfering species is available.

Comparison of spectroscopically measured tissue alcohol concentration to blood and breath alcohol measurements

Alcohol testing is an expanding area of interest due to the impacts of alcohol abuse that extend well beyond drunk driving. However, existing approaches such as blood and urine assays are hampered in some testing environments by biohazard risks. A noninvasive, in vivo spectroscopic technique offers a promising alternative, as no body fluids are required. The purpose of this work is to report the results of a 36-subject clinical study designed to characterize tissue alcohol measured using near-infrared spectroscopy relative to venous blood, capillary blood, and breath alcohol. Comparison of blood and breath alcohol concentrations demonstrated significant differences in alcohol concentration [root mean square of 9.0 to 13.5 mg/dL] that were attributable to both assay accuracy and precision as well as alcohol pharmacokinetics. A first-order kinetic model was used to estimate the contribution of alcohol pharmacokinetics to the differences in concentration observed between the blood, breath, and tissue assays. All pair-wise combinations of alcohol assays were investigated, and the fraction of the alcohol concentration variance explained by pharmacokinetics ranged from 41.0% to 83.5%. Accounting for pharmacokinetic concentration differences, the accuracy and precision of the spectroscopic tissue assay were found to be comparable to those of the blood and breath assays.

Framework for multivariate selectivity analysis, part II: experimental applications

In Part I of this paper, a framework for multivariate selectivity was introduced that is both calculable from first principles and experimentally tractable. In this part, we employ the proposed selectivity framework for analyzing both in vitro and in vivo near-infrared experimental data. Two in vitro data sets are used to compare different methods for estimating selectivity and to demonstrate the benefits obtained from validation data with expanded interferant concentration ranges. The in vitro data also demonstrate that the experimentally estimated selectivities provide insights into the properties of the calibration models that are difficult or impossible to infer by other means. The merits of the proposed selectivity function are further demonstrated using a complex in vivo application: the noninvasive measurement of ethanol in humans. Results indicate that in vivo calibration model sensitivity, selectivity, and concentration correlations can be systematically interrogated using the proposed selectivity framework and judicious use of experimental measurements. These analyses not only provide selectivity and sensitivity information, but also the variance components of the total MSEP, which is invaluable information for both method development and analytical method characterization.