Enhancing gas chromatography-mass spectrometry resolution and pure analyte discovery using intra-chromatogram elution profile matching.

Chemometric decomposition methods like multivariate curve resolution-alternating least squares (MCR-ALS) are often employed in gas chromatography-mass spectrometry (GC-MS) to improve analyte identification and quantitation. However, these methods can perform poorly for analytes with a low chromatographic resolution (Rs) and a high degree of spectral contamination from noise and background interferences. Thus, we propose a novel computational algorithm, termed mzCompare, to improve analyte identification and quantitation when coupled to MCR-ALS. The mzCompare method utilizes an underlying requirement that the retention time and peak shape between mass channels (m/z) of the same analyte should be similar. By discovering the selective m/z for a given analyte in a chromatogram, a pure elution profile can be generated and used as an equality constraint in MCR-ALS. The performance of the mzCompare methodology is demonstrated with both experimental and simulated chromatograms. Experimentally, unresolved analytes with a Rs as low as 0.05 could be confidently identified with mzCompare assisted MCR-ALS. Furthermore, application of the mzCompare algorithm to a complex aerospace fuel resulted in the discovery of 335 analytes, a 44 % increase compared to conventional peak detection methods. GC-MS simulations of target-interferent analyte pairs demonstrated that the performance of MCR-ALS deteriorated below a Rs of ∼0.25. However, mzCompare assisted MCR-ALS showed excellent identification and acceptable quantitative accuracy at a Rs of ∼0.02. These results show that the mzCompare algorithm can help analysts overcome modeling ambiguities resulting from the chemometric multiplex disadvantage.

Enabling cuboid-based fisher ratio analysis using total-transfer comprehensive three-dimensional gas chromatography with time-of-flight mass spectrometry.

Comprehensive three-dimensional (3D) gas chromatography with time-of-flight mass spectrometry (GC3-TOFMS) is a promising instrumental platform for the separation of volatiles and semi-volatiles due to its increased peak capacity and selectivity relative to comprehensive two-dimensional gas chromatography with TOFMS (GC×GC-TOFMS). Given the recent advances in GC3-TOFMS instrumentation, new data analysis methods are now required to analyze its complex data structure efficiently and effectively. This report highlights the development of a cuboid-based Fisher ratio (F-ratio) analysis for supervised, non-targeted studies. This approach builds upon the previously reported tile-based F-ratio software for GC×GC-TOFMS data. Cuboid-based F-ratio analysis is enabled by constructing 3D cuboids within the GC3-TOFMS chromatogram and calculating F-ratios for every cuboid on a per-mass channel basis. This methodology is evaluated using a GC3-TOFMS data set of jet fuel spiked with both non-native and native components. The neat and spiked jet fuels were collected on a total-transfer (100 % duty cycle) GC3-TOFMS instrument, employing thermal modulation between the first (1D) and second dimension (2D) columns and dynamic pressure gradient modulation between the 2D and third dimension (3D) columns. In total, cuboid-based F-ratio analysis discovered 32 spiked analytes in the top 50 hits at concentration ratios as low as 1.1. In contrast, tile-based F-ratio analysis of the corresponding GC×GC-TOFMS data only discovered 28 of the spiked analytes total, with only 25 of them in the top 50 hits. Along with discovering more analytes, cuboid-based F-ratio analysis of GC3-TOFMS data resulted in fewer false positives. The increased discoverability is due to the added peak capacity and selectivity provided by the 3D column with GC3-TOFMS resulting in improved chromatographic resolution.

Tile-Based Random Forest Analysis for Analyte Discovery in Balanced and Unbalanced GC × GC-TOFMS Data Sets.

In this study, we introduce a new nontargeted tile-based supervised analysis method that combines the four-grid tiling scheme previously established for the Fisher ratio (F-ratio) analysis (FRA) with the estimation of tile hit importance using the machine learning (ML) algorithm Random Forest (RF). This approach is termed tile-based RF analysis. As opposed to the standard tile-based F-ratio analysis, the RF approach can be extended to the analysis of unbalanced data sets, i.e., different numbers of samples per class. Tile-based RF computes out-of-bag (oob) tile hit importance estimates for every summed chromatographic signal within each tile on a per-mass channel basis (m/z). These estimates are then used to rank tile hits in a descending order of importance. In the present investigation, the RF approach was applied for a two-class comparison of stool samples collected from omnivore (O) subjects and stored using two different storage conditions: liquid (Liq) and lyophilized (Lyo). Two final hit lists were generated using balanced (8 vs Eight comparison) and unbalanced (8 vs Nine comparison) data sets and compared to the hit list generated by the standard F-ratio analysis. Similar class-distinguishing analytes (p < 0.01) were discovered by both methods. However, while the FRA discovered a more comprehensive hit list (65 hits), the RF approach strictly discovered hits (31 hits for the balanced data set comparison and 29 hits for the unbalanced data set comparison) with concentration ratios, [OLiq]/[OLyo], greater than 2 (or less than 0.5). This difference is attributed to the more stringent feature selection process used by the RF algorithm. Moreover, our findings suggest that the RF approach is a promising method for identifying class-distinguishing analytes in settings characterized by both high between-class variance and high within-class variance, making it an advantageous method in the study of complex biological matrices.

Enhancing partial least squares modeling of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry data by tile-based variance ranking.

Chemometric methods like partial least squares (PLS) regression are valuable for correlating sample-based differences hidden in comprehensive two-dimensional gas chromatography (GC × GC) data to independently measured physicochemical properties. Herein, this work establishes the first implementation of tile-based variance ranking as a selective data reduction methodology to improve PLS modeling performance of 58 diverse aerospace fuels. Tile-based variance ranking discovered a total of 521 analytes with a square of the relative standard deviation (RSD2) in signal between 0.07 to 22.84. The goodness-of-fit for the models were determined by their normalized root-mean-square error of cross-validation (NRMSECV) and normalized root-mean-square error of prediction (NRMSEP). PLS models developed for viscosity, hydrogen content, and heat of combustion using all 521 features discovered by tile-based variance ranking had a respective NRMSECV (NRMSEP) equal to 10.5 % (10.2 %), 8.3 % (7.6 %), and 13.1 % (13.5 %). In contrast, use of a single-grid binning scheme, a common data reduction strategy for PLS analysis, resulted in less accurate models for viscosity (NRMSECV = 14.2 %; NRMSEP = 14.3 %), hydrogen content (NRMSECV = 12.1 %; NRMSEP = 11.0 %), and heat of combustion (NRMSECV = 14.4 %; NRMSEP = 13.6 %). Further, the features discovered by tile-based variance ranking can be optimized for each PLS model with RReliefF analysis, a machine learning algorithm. RReliefF feature optimization selected 48, 125, and 172 analytes out of the original 521 discovered by tile-based variance ranking to model viscosity, hydrogen content, and heat of combustion, respectively. The RReliefF optimized features developed highly accurate property-composition models for viscosity (NRMSECV = 7.9 %; NRMSEP = 5.8 %), hydrogen content (NRMSECV = 7.0 %; NRMSEP = 4.9 %), heat of combustion (NRMSECV = 7.9 %; NRMSEP = 8.4 %). This work also demonstrates that processing the chromatograms with a tile-based approach allows the analyst to directly identify the analytes of importance in a PLS model. Coupling tile-based feature selection with PLS analysis allows for deeper understanding in any property-composition study.

Optimization of Parameters for ROI Data Compression for Nontargeted Analyses Using LC-HRMS.

Nontargeted analyses of low-concentration analytes in the information-rich data collected by liquid chromatography with high-resolution mass spectrometry detection can be challenging to accomplish in an efficient and comprehensive manner. The aim of this study is to demonstrate a workflow involving targeted parameter optimization for entire chromatograms using region of interest (ROI) data compression uncoupled from a subsequent tile-based Fisher ratio (F-ratio) analysis, a supervised discovery-based method, for the discovery of low-concentration analytes. Soil samples spiked with 18 pesticides at nominal concentrations ranging from 0.1 to 50 ppb for a total of six sample classes served as challenging samples to demonstrate the overall workflow. Optimization of two parameters proved to be the most critical for ROI data compression: the signal threshold parameter and the admissible mass deviation parameter. The parameter optimization method workflow we introduce is based upon spiking known analytes into a representative sample and determining the number of detectable spikes and the Δppm for various combinations of the signal threshold and admissible mass deviation, where Δppm is the absolute value of the difference between the theoretical m/z and the ROI m/z. Once optimal parameters are determined providing the lowest average Δppm and the greatest number of detectable analytes, the optimized parameters can be utilized for the intended analysis. Herein, tile-based F-ratio analysis was performed on the ROI compressed data of all spiked soil samples first by applying ROI parameters recommended in the literature, referred to herein as the initial ROI parameters, and finally by the combination of the two optimized parameters. Using the initial ROI parameters, three pesticides were discovered, whereas all 18 spiked pesticides were discovered by optimizing both ROI parameters.

Tile-based variance rank initiated-unsupervised sample indexing for comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry.

Tile-based variance rank initiated-unsupervised sample indexing (VRI-USI) analysis is introduced for comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). VRI-USI analysis addresses the challenge that irrelevant variables can often obscure true chemical variation when using other unsupervised chemometric tools. Implementation of VRI-USI analysis with GC×GC-TOFMS data incorporates the tile-based Fisher ratio (F-ratio) analysis software platform that mitigates the effects of retention shifting in both separation dimensions with an unsupervised variance metric (instead of the F-ratio metric) as the initial step of ranking the hitlist. Next, implementation of k-means clustering, k, per hit using the silhouette metric, Smax, is used to reveal to what extent recurring indexed sample clusters are uncovered. Finally, based upon a probability-based evaluation of how the individual samples cluster throughout the hitlist an unsupervised class membership is revealed. For a JP8 jet fuel dataset spiked with a sulfur-containing analyte mix at 30-ppm, 15-ppm, and neat, clustering by spike level at k = 3 was the most commonly re-occurring set of index assignments, occurring for 11 out of 14 spiked analytes. Upon application of these k-means index assignments to the entire hitlist, all 14 spiked hits had one way ANOVA p-values < 0.05, validating the presumption of classes. Next, application of VRI-USI to a 3-ppm spiked and neat JP8 jet fuel comparison exhibited similar performance to F-ratio analysis for analyte discovery. In the last study, for a dataset of J1800A, JP4, and JP8 jet fuel, each spiked with the sulfur-containing analyte mix at 30-ppm and neat, 453 out of 520 hits in the hitlist exhibited index assignments indicative of fuel type clustering, with the remaining 67 hits having contradictory assignments. Scrutinization of these 67 hits revealed nine hits with "split combinations" in index assignments, whereby the spiked and neat samples for a given fuel were in separate clusters. Eight of these hits were identified as spiked sulfur analytes. Interestingly, these hits also had large Smax indicative of a true sub-cluster. Thus, tile-based VRI-USI analysis appears to be a promising tool for unsupervised multi-class classification studies using GC×GC-TOFMS data.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification.

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

Development of variance rank initiated-unsupervised sample indexing for gas chromatography-mass spectrometry analysis.

Traditional non-targeted chemometric workflows for gas chromatography-mass spectrometry (GC-MS) data rely on using supervised methods, which requires a priori knowledge of sample class membership. Herein, we propose a simple, unsupervised chemometric workflow known as variance rank initiated-unsupervised sample indexing (VRI-USI). VRI-USI discovers analyte peaks exhibiting high relative variance across all samples, followed by k-means clustering on the individual peaks. Based upon how the samples cluster for a given peak, a sample index assignment is provided. Using a probabilistic argument, if the same sample index assignment appears for several discovered peaks, then this outcome strongly suggests that the samples are properly classified by that particular sample index assignment. Thus, relevant chemical differences between the samples have been discovered in an unsupervised fashion. The VRI-USI workflow is demonstrated on three, increasingly difficult datasets: simulations, yeast metabolomics, and human cancer metabolomics. For simulated GC-MS datasets, VRI-USI discovered 85-90% of analytes modeled to vary between sample classes. Nineteen out of 53 peaks in the peak table developed for the yeast metabolome dataset had the same sample index assignments, indicating that those indices are most likely due to class-distinguishing chemical differences. A t-test revealed that 22 out of 53 peaks were statistically significant (p < 0.05) when using those sample index assignments. Likewise, for the human cancer metabolomics study, VRI-USI discovered 25 analytes that were statistically different (p < 0.05) using the sample index assignments determined to highlight meaningful sample-based differences. For all datasets, the sample index assignments that were deduced from VRI-USI were the correct class-based difference when using prior knowledge. VRI-USI holds promise as an exploratory data analysis workflow for studies in which analysts do not readily have a priori class information or want to uncover the underlying nature of their dataset.

Short and long-term outcomes of surgical intervention for empyema in the post-fibrinolytic era.

BACKGROUND: Open window thoracostomy (OWT) is indicated for patients with bronchopleural fistula (BPF) or trapped lung in the setting of empyema refractory to non-surgical interventions. We investigated the role of OWT in the era of minimally invasive surgeries, endobronchial valves and fibrinolytic therapy. METHODS: A retrospective chart review of all patients who underwent OWT at a single institution from 2010 to 2020 was performed. Indications for the procedure as well as operative details and morbidity and mortality were evaluated to determine patient outcomes for OWT. RESULTS: Eighteen patients were identified for the study. The most common indication for OWT was post-resectional BPF (n = 9). Prior to OWT, n = 11 patients failed other surgical or minimally invasive interventions. Patient comorbidities were quantified with the Charlson Comorbidity index (n = 11 score ≥ 5, 10-year survival ≤21%). Three (16.7%) patients died < 30 days post-operatively and 12 (66%) patients were deceased by the study's end (overall survival 24.0 ± 32.2 months). Mean number of ribs resected were 2.5 ± 1.2 (range 1-6) with one patient having 6 ribs removed. Patients were managed with negative pressure wound therapy (n = 9) or Kerlix packing (n = 9). Eleven patients (61.6%) underwent delayed closure (mean time from index surgery to closure 4.8 ± 6.7 months). CONCLUSIONS: Our study illustrates the significant comorbidities of patients undergoing OWT, the poor outcomes therein, and pitfalls associated with this procedure. We show that negative pressure wound therapy can be utilized as potential way to obliterate the pleural space and manage an open chest in the absence of an airleak; however, OWT procedures continue to be extremely morbid.

Baseline correction method for dynamic pressure gradient modulated comprehensive two-dimensional gas chromatography with flame ionization detection.

A baseline correction method is developed for comprehensive two-dimensional (2D) chromatography (GC × GC) with flame-ionization detection (FID) using dynamic pressure gradient modulation (DPGM). The DPGM-GC × GC-FID utilized porous layer open tubular (PLOT) columns in both dimensions to focus on light hydrocarbon separations. Since DPGM is nominally a stop-flow modulation technique, a rhythmic baseline disturbance is observed in the FID signal that cycles with the modulation period (PM). This baseline disturbance needs to be corrected to optimize trace analysis. The baseline correction method has three steps: collection of a background "blank" chromatogram and multiplying it by an optimized normalization factor, subtraction of the normalization-optimized background chromatogram from a sample chromatogram, and application of Savitzky-Golay smoothing. An alkane standard solution, containing pentane, hexane and heptane was used for method development, producing linear calibration curves (r2 > 0.991) over a broad concentration range (7.8 ppm - 4000 ppm). Further, the limit-of-detection (LOD) and limit-of-quantification (LOQ) were determined for pentane (LOD = 2.5 ppm, LOQ = 8.2 ppm), hexane (LOD = 0.9 ppm, LOQ = 3.0 ppm), and heptane (LOD = 1.9 ppm, LOQ = 6.4 ppm). A natural gas sample separation illustrated method applicability, whereby the DPGM produced a signal enhancement (SE) of 30 for isopentane, where SE is defined as the height of the tallest 2D peak in the modulated chromatogram for the analyte divided by the height of the unmodulated 1D peak. The 30-fold SE resulted in about a 10-fold improvement in the signal-to-noise ratio (S/N) for isopentane. Additional versatility of the baseline correction method for more complicated samples was demonstrated for an unleaded gasoline sample, which enabled the detection (and visual appearance) of trace components.

A Fc engineering approach to define functional humoral correlates of immunity against Ebola virus.

Protective Ebola virus (EBOV) antibodies have neutralizing activity and induction of antibody constant domain (Fc)-mediated innate immune effector functions. Efforts to enhance Fc effector functionality often focus on maximizing antibody-dependent cellular cytotoxicity, yet distinct combinations of functions could be critical for antibody-mediated protection. As neutralizing antibodies have been cloned from EBOV disease survivors, we sought to identify survivor Fc effector profiles to help guide Fc optimization strategies. Survivors developed a range of functional antibody responses, and we therefore applied a rapid, high-throughput Fc engineering platform to define the most protective profiles. We generated a library of Fc variants with identical antigen-binding fragments (Fabs) from an EBOV neutralizing antibody. Fc variants with antibody-mediated complement deposition and moderate natural killer (NK) cell activity demonstrated complete protective activity in a stringent in vivo mouse model. Our findings highlight the importance of specific effector functions in antibody-mediated protection, and the experimental platform presents a generalizable resource for identifying correlates of immunity to guide therapeutic antibody design.

Analytical Determination of the Severity of Potato Taste Defect in Roasted East African Arabica Coffee.

The quality of East African coffee beans has been significantly reduced by a flavor defect known as potato taste defect (PTD) due to the presence of 2-isopropyl-3-methoxypyrazine (IPMP) and 2-isobutyl-3-methoxypyrazine (IBMP). Therefore, the aims of this study were to determine the correlation between these methoxypyrazines and the severity of odor attributed to PTD and discover additional analytes that may be correlated with PTD using Fisher ratio analysis, a supervised discovery-based data analysis method. Specialty ground roasted coffees from East Africa were classified as clean (i.e., no off-odor), mild, medium, or strong PTD. For the samples examined, IPMP was found to discriminate between non-defective and defective samples, while IBMP did not do so. Samples affected by PTD exhibited a wide range of IPMP concentration (1.6-529.9 ng/g). Except for one sample, the IPMP concentration in defective samples was greater than the average IPMP concentration in the non-defective samples (2.0 ng/g). Also, an analysis of variance found that IPMP concentrations were significantly different based on the severity of odor attributed to PTD (p < 0.05). Fisher ratio analysis discovered 21 additional analytes whose concentrations were statistically different based on the severity of PTD odor (p < 0.05). Generally, analytes that were positively correlated with odor severity generally had unpleasant sensory descriptions, while analytes typically associated with desirable aromas were found to be negatively correlated with odor severity. These findings not only show that IPMP concentration can differentiate the severity of PTD but also that changes in the volatile analyte profile of coffee beans induced by PTD can contribute to odor severity.

Mediastinal tissue friability-An unreported complication from Mycobacterium chimaera infection post-cardiac surgery.

Mycobacterium chimaera can cause disseminated infection following cardiac surgery with cardiopulmonary bypass and contaminated heater-cooler devices. We discuss a 41-year-old man with a disseminated M. chimaera infection following surgery for a type A aortic dissection. His presentation included cachexia and dorsalgia with a work-up revealing vertebral osteomyelitis with an epidural abscess, bone marrow, and pulmonary infiltration, and fluid collection around his aortic graft. He received 1 month of antibiotics before the explantation of infected foreign material, mediastinal debridement, and aortic reconstruction. Complications included septic shock, respiratory and renal failure, mediastinitis, and four distal aortic anastomotic dehiscences from friable tissue and persistent infection.

Development of an Enhanced Total Ion Current Chromatogram Algorithm to Improve Untargeted Peak Detection.

Accurate analyte peak detection from the background noise is a fundamental step in data analysis. Often, this is initially performed on the total ion current chromatogram (TIC), which is the summed signal from all mass spectral channels. Despite the detection of many of the most abundant peaks within a chromatogram, a large fraction of peaks remains undetected in the standard TIC due to their signal being below the limit of detection. To find peaks obscured by background noise, an untargeted peak detection method termed the "enhanced TIC algorithm" was developed for comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). The reported algorithm utilizes the entire mass spectral dimension to find regions of analytical signal above a threshold while zeroing the background noise. The resulting chromatographic data is summed together to create the enhanced TIC. The utility of the enhanced TIC algorithm is demonstrated using serial dilutions from a 10 parts-per-thousand (ppth) test mixture. For the chromatograms collected at 1 and 10 parts-per-million (ppm), the enhanced TIC algorithm recovered 62% and 93%, respectively, of the original peaks observed in the 10 ppth mixture, while the standard TIC recovered only 0% and 45%, respectively. The improvement in signal enhancement was also shown on a separation of a yeast cell metabolite extract, where the enhanced TIC found 33-64% more peaks than the standard TIC. Chromatographic simulations with increasing levels of background noise were also conducted to compare the enhanced and standard TICs in the context of statistical overlap theory (SOT). Simulated chromatograms with lower signal-to-noise were more accurately modeled by the SOT after enhanced TIC processing compared to those processed by the standard TIC. The enhanced TIC method demonstrates an immense benefit in peak discovery to improve data analysis efforts.

Fabrication and Characterization of a Reversed-Phase/Strong Cation Exchange Stationary Phase Gradient.

Continuous stationary phase gradients for liquid chromatography (LC) have been recently shown to be a promising method of altering selectivity. In this work, we present the first multicomponent continuous stationary phase gradient for separations involving both reversed-phase (RP) and strong cation exchange (SCX) mechanisms. These columns are fabricated using a two-step methodology based on controlled rate infusion (CRI). First, destructive CRI creates a gradient of excess silanol groups along a uniform C18 column. Next, these columns are infused with 3-mercaptopropyltrimethoxysilane (MPTMOS), which bonds to the excess silanol groups. The terminal thiols of the MPTMOS ligands are oxidized with H2O2 to create the sulfonate functionality (SO3-) needed for SCX separations. The success of the modification procedure is characterized by thermogravimetric analysis and X-ray photoelectron spectroscopy. The stability of the C18-SO3- gradients were found to have less than 5 % retention loss and the column-to-column reproducibility had a relative standard deviation under 9 %. The peak asymmetry factors for seven biogenic amines were found to be between 1.03 ± 0.04 to 1.30 ± 0.02, which illustrates minimal peak tailing due to poor packing and residual silanol groups. Characterization of the gradient columns using an isocratic mobile phase showed a unique elution order compared to a uniform C18 and SO3- columns. At lower counterion concentrations, more than 48 % of the overall retention on the gradient stationary phase is due to a SCX mechanism. Meanwhile, the RP mechanism was shown to predominate at higher counterion concentrations on the gradient columns (SCX retention contribution less than 40 %). Coupling the stationary phase gradient to a salt gradient in the mobile phase showed that the gradient phase provides a unique, intermediate selectivity to the uniform C18 and SO3- columns. Under an ACN mobile phase gradient, a significant increase (p < 0.003) in the retention times of three biogenic amines (15 - 16 %) was observed when the multicomponent gradient was oriented to have a high SO3- ligand density near the detector. This work serves as a proof-of-concept design for a multicomponent stationary phase gradient to continue fundamental studies into retention and encourage novel applications.

When Multiple Objective Measures of Medication Adherence Indicate Incongruent Adherence Results: An Example with Pediatric Cancer.

Previous research suggests that children and adolescents with acute lymphoblastic leukemia (ALL) and lymphoblastic lymphoma (LBL) often have difficulty adhering to complex treatment regimens during the maintenance phase of therapy. Measurement of treatment adherence can be done via objective (e.g., electronic monitoring (EM), pharmacological assays) or subjective methods (patient, parent, or physician reports). This paper provides an illustration of recommended strategies for comparing discrepancies between two objective measures of medication adherence (e.g., behavioral adherence using electronic monitoring versus pharmacological adherence using 6-mercaptopurine (6MP) metabolite data) within a relatively large cohort of pediatric patients with ALL or LBL (N = 139) who had longitudinal data for both measures of medication adherence over a 15-month period. Additionally, individual- and family-level factors such as gender, socioeconomic status, household environment, and dose intensity will be examined to identify possible sources of discrepancies between adherence measures. This information will provide practical advice for physicians, healthcare providers, and psychologists in identifying nonadherence and the caveats therein so patients achieve the best possible health outcomes.

Killer Immunoglobulin-Like Receptor-Ligand Interactions Predict Clinical Outcomes following Unrelated Donor Transplantations.

Killer immunoglobulin-like receptor (KIR) and KIR ligand (KIRL) interactions play an important role in natural killer (NK) cell-mediated graft-versus-leukemia effect following hematopoietic cell transplantation (HCT). However, there is considerable heterogeneity in the KIR gene and KIRL content in individuals, making it difficult to estimate the full clinical impact of NK cell reconstitution following HCT. Here we present a novel adaptive mathematical model designed to quantify these interactions to better assess the influence of NK cell-mediated alloreactivity on transplant outcomes. Ninety-eight HLA- matched unrelated donor (URD) HCT recipients were studied retrospectively. The KIR-KIRL interactions were quantified using a system of matrix equations. Unit values were ascribed to each KIR-KIRL interaction, and the directionality of interactions was denoted by either a positive (activating) or negative (inhibition) symbol; these interactions were then summed. The absolute values of both the missing KIRL and inhibitory KIR-KIRL interactions were significantly associated with overall survival and relapse. These score components were initially used to develop a weighted score (w-KIR score) and subsequently a simplified, nonweighted KIR-KIRL interaction score (IM-KIR score). Increased w-KIR score and IM-KIR score were predictive of all-cause mortality and relapse (w-KIR score: hazard ratio [HR], .37 [P = .001] and .44 [P = .044], respectively; IM-KIR score: HR, .5 [P = .049] and .44 [P = .002], respectively). IM-KIR score was also associated with NK cell reconstitution post-HCT. KIR-KIRL interactions as reflected by the w-KIR and IM-KIR scores influence both relapse risk and survival in recipients of HLA-matched URD HCT with hematologic malignancies.

Experimental- and simulation-based investigations of coupling a mobile phase gradient with a continuous stationary phase gradient.

This work seeks to explore and understand the effects of column orientation and degree of modification of continuous stationary phase gradient columns under a mobile phase gradient using both simulations and experiments. Peak parameters such as retention times, peak widths and resolution are obtained for five phenolic compounds on a C18-silica gradient stationary phase. Simulations show that peak widths for the solutes are dependent upon the fractional composition of C18 and orientation of the stationary phase gradient when coupled to a mobile phase gradient. Also, when compared to a simulated uniform mixed-mode column, peak widths reach a minimum on the gradient column with a coverage higher than 50% C18 where the column is oriented to have the C18 dense region at the end. Experimentally, continuous stationary phase gradients were fabricated to have a total C18 composition of 78% of the original uniform column with an exponential profile using a previously described destructive controlled rate infusion method. Under gradient mobile phase conditions, experimental retention times for the gradient column showed a significant increase compared to the original 100% C18 column. Simulations with a similar C18 composition, however, predicted decreased retention times from the original C18 column. A statistical increase in the retention time of protocatechuic acid and decrease in the peak width of tyrosol, caffeic acid, and coumaric acid were noted when the gradient column was oriented to have the C18 dense region located near the detector. Collectively, combining gradients in both the mobile and stationary phases can yield interesting neighboring ligand effects and peak broadening/focusing effects.