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We might well ask “Where is gas chromatography (GC) heading?” For many analysts, the answer may be just “more of the same,” reflecting that GC is mature and that most analysis tasks and sample types have been tried and tested. In this scenario, any changes to the basic method may be marginal—sample introduction, and maybe a new detector? But beneath this status quo is an undercurrent of passion, excitement, and power.

Volatile organic compounds (VOCs) originating from human metabolic activities can be detected in, for example, breath, urine, feces, and blood. Thus, attention has been given to identifying VOCs from the above matrices. Studies identifying and measuring human blood VOCs are limited to those focusing on monitoring specific pollutants, or blood storage and/or decomposition. However, a comprehensive characterization of VOCs in human blood collected for routine diagnostic testing is lacking. In this study, 72 blood-derived plasma samples were obtained from apparently healthy adult participants. VOCs were extracted from plasma using solid-phase microextraction and analyzed using comprehensive two-dimensional gas chromatography tandem time-of-flight mass spectrometry (GC×GC-ToFMS). Chromatographic data were aligned, and putative compound identities were assigned via spectral library comparison. All statistical analysis, including contaminant removal, data normalization and transformation were performed using R. We identified 401 features which we called the pan volatilome of human plasma. Of the 401 features, 34 were present in all the samples with less than 15% variance (core molecules), 210 were present in ≥10% but

Background Transcriptomic profiling of adult tuberculosis patients has become increasingly common, predominantly for diagnostic and risk prediction purposes. However few studies have evaluated signatures in children, particularly in identifying those at risk for developing TB disease. We investigated the relationship between gene expression obtained from umbilical cord blood and both tuberculin skin test conversion as well as incident tuberculosis disease through the first 5 years of life. Methods We conducted a nested case-control study in the Drakenstein Child Health Study, a longitudinal, population-based birth cohort in South Africa. We applied transcriptome-wide screens to umbilical cord blood samples from neonates born to a subset of selected mothers (n=131). Signatures identifying tuberculin conversion and risk of subsequent tuberculosis disease were identified from genome wide analysis of RNA expression. Results Gene expression signatures revealed clear differences predictive of tuberculin conversion (n=26) and tuberculosis disease (n=10); 114 genes were associated with tuberculin conversion and 30 genes were associated with the progression to tuberculosis disease among children with early infection. Co-expression network analysis revealed six modules associated with risk of tuberculosis infection or disease, including a module associated with neutrophil activation in immune response (p Conclusions These findings suggest multiple detectable differences in gene expression at birth which were associated with risk of tuberculosis infection or disease throughout early childhood. Such measures may provide novel insights into tuberculosis pathogenesis and susceptibility.

Tuberculosis (TB) is the archetypical chronic infection, with patients having months of symptoms before diagnosis. In the two years after successful therapy, survivors of TB have a three-fold increased risk of death.Guinea pigs were infected withGuinea pigs with TB demonstrated DNA hypermethylation and showed system-level similarity to humans with TB (In both guinea pigs and humans, TB perturbs epigenetic processes, promoting premature cellular aging and inflammation, a plausible means to explain the long-term detrimental health outcomes after TB.

AIMS: Adjuvant chemotherapy (ACT) for stage III colon cancer is well-established. This study aimed to explore the determinants of ACT use and between-hospital variation within the English National Health Service (NHS). MATERIALS AND METHODS: In total, 11 932 patients (diagnosed 2014-2017) with pathological stage III colon cancer in the English NHS were identified from the National Bowel Cancer Audit. Records were linked to Systemic Anti-Cancer Therapy and Hospital Episode Statistics databases. Multi-level logistic regression analyses were carried out to estimate independent factors for ACT use, including age, sex, deprivation, comorbidities, performance status, American Society of Anaesthesiologists (ASA) grade, surgical urgency, surgical access, TNM staging, readmission and hospital-level factors (university teaching hospital, on-site chemotherapy and high-volume centre). A random intercept was modelled for each English NHS hospital (n = 142). Between-hospital variation was explored using funnel plot methodology. Fully adjusted random-intercept models were fitted separately in young (

Pulmonary infections caused by mycobacteria cause significant mortality and morbidity in the human population. Diagnosing mycobacterial infections is challenging. An infection can lead to active disease or remain indolent with little clinical consequence. In patients with pulmonary

Data fusion has gained much attention in the field of life sciences, and this is because analysis of biological samples may require the use of data coming from multiple complementary sources to express the samples fully. Data fusion lies in the idea that different data platforms detect different biological entities. Therefore, if these different biological compounds are then combined, they can provide comprehensive profiling and understanding of the research question in hand. Data fusion can be performed in three different traditional ways: low-level, mid-level, and high-level data fusion. However, the increasing complexity and amount of generated data require the development of more sophisticated fusion approaches. In that regard, the current study presents an advanced data fusion approach (i.e. proximities stacking) based on random forest proximities coupled with the pseudo-sample principle. Four different data platforms of 130 samples each (faecal microbiome, blood, blood headspace, and exhaled breath samples of patients who have Crohn's disease) were used to demonstrate the classification performance of this new approach. More specifically, 104 samples were used to train and validate the models, whereas the remaining 26 samples were used to validate the models externally. Mid-level, high-level, as well as individual platform classification predictions, were made and compared against the proximities stacking approach. The performance of each approach was assessed by calculating the sensitivity and specificity of each model for the external test set, and visualized by performing principal component analysis on the proximity matrices of the training samples to then, subsequently, project the test samples onto that space. The implementation of pseudo-samples allowed for the identification of the most important variables per platform, finding relations among variables of the different data platforms, and the ex-amination of how variables behave in the samples. The proximities stacking approach outperforms both mid-level and high-level fusion approaches, as well as all individual platform predictions. Concurrently, it tackles significant bottlenecks of the traditional ways of fusion and of another advanced fusion way discussed in the paper, and finally, it contradicts the general belief that the more data, the merrier the result, and therefore, considerations have to be taken into account before any data fusion analysis is conducted. (c) 2021 Published by Elsevier B.V.

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography–quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.

One of the most logical applications of modern breath analysis techniques is to provide information on respiratory infections. Ongoing work in various types of pulmonary infections has begun to denote candidate breath biomarkers of bacterial, viral, and fungal lung infections. Groundbreaking studies have been performed in naturally occurring cases with humans and with animal models of the disease. This has been coupled with cell culture work to understand the nature of the origins of breath biomarkers generated from the pathogen itself as it proliferates. Much work remains to be done, and the published studies described in this chapter are helping to set a foundation for this research area.

Exhaled breath analysis has become an important part of volatilomics, with high potential for health applications, particularly as a noninvasive diagnostic tool. Nevertheless, this approach faces numerous challenges, the first being the need to employ powerful analytical techniques to properly resolve the breath volatilome and to extract the relevant information, and the second consisting of establishing a high-quality and reliable data analysis workflow. This chapter highlights the capabilities of comprehensive two-dimensional gas chromatography (GC×GC) as the ideal analytical technique for the analysis of exhaled breath. The coupling of GC×GC with mass spectrometry (MS) and the enhanced identification power using high-resolution (HR) MS is also discussed. In addition, an emphasis is placed on the importance of the data processing workflow, as well as the need to produce high data quality in order to perform useful statistical analyses. Finally, some successful applications of GC×GC-(HR)MS in volatilomics are presented.

Gas chromatography (GC) coupled with electron ionization (EI) mass spectrometry (MS) is a well-established technique for the analysis of volatile and semi-volatile compounds. The main advantage is the highly repeatable fragmentation of the compounds into the ion source, generating intense and diagnostic fragmentation when the ionization is performed at 70 eV; this is considered the standard ionization condition and has been used for creating many established databases, which are of great support in the analyte identification process. However, such an intense fragmentation often causes the loss of the molecular ion or more diagnostic ions, which can be detrimental for the identification of homologous series or isomers, as for instance fatty acids. To obtain this information chemical or soft ionization can be used, but dedicated ion sources and conditions are required. In this work, we explored different ionization voltages in GC-EI-MS to preserve the intensity of the molecular ion using a conventional quadrupole MS. Twenty, 30, 50, and 70 eV were tested using a mixture of fatty acid methyl esters standards. Intensity and repeatability of the most informative ions were compared. Twenty and 70 eV were then used to analyze the fatty acid composition of six different strains of mycobacteria. Two approaches were used for elaborating the data: (1) a single average spectrum of the entire chromatogram was derived, which can be considered (in terms of concept) as a direct EI-MS analysis; (2) the actual chromatographic separation of the compounds was considered after automatic alignment. The results obtained are discussed herein. Graphical abstract ᅟ.

In this study, the volatile molecule profile of Streptococcus pneumoniae serotypes was evaluated using solid phase microextraction (SPME) and two dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). Here, seven serotypes (6B, 14, 15, 18C, 19F, 9V, and 23F) were analyzed in an isogenic background. We identified 13 core molecules associated with all seven serotypes, and seven molecules that were differentially produced between serotypes. Serotype 14 was found to have the most distinct volatile profile, and could be discriminated from the other six serotypes in aggregate with an area under the curve (AUC) of 89%. This study suggests that molecules from S. pneumoniae culture headspace show potential for rapid serotype identification.

Infections caused by carbapenem-resistant Enterobacteriaceae (CRE) are alarming in the clinical setting, as CRE isolates often exhibit resistance to most clinically-available antibiotics. Klebsiella pneumoniae carbapenemase (KPC) is the most common carbapenemase carried by CRE in North America and Europe, frequently detected in isolates of K. pneumoniae, Escherichia coli, and Enterobacter cloacae. Notably, KPC-expressing strains often arise from clonal lineages, with sequence type 258 (ST258) representing the dominant lineage in K. pneumoniae, ST131 in E. coli, and ST78 and ST171 in E. cloacae. Prior studies have demonstrated that carbapenem-resistant K. pneumoniae differs from carbapenem-susceptible K. pneumoniae at both the transcriptomic and soluble metabolomic levels. In the present study, we sought to determine whether carbapenem-resistant and carbapenem-susceptible isolates of K. pneumoniae, E. coli, and E. cloacae produce distinct volatile metabolic profiles. We were able to identify a volatile metabolic fingerprint that could discriminate between CRE and non-CRE with an area under the receiver operating characteristic curve (AUROC) as high as 0.912. Species-specific AUROCs were as high as 0.988 for K. pneumoniae and 1.000 for E. cloacae. Paradoxically, curing of KPC-expressing plasmids from a subset of K. pneumoniae isolates further accentuated the metabolic differences observed between ST258 and non-ST258.

Respiratory infections caused by Pseudomonas aeruginosa and Staphylococcus aureus are the leading cause of morbidity and mortality in cystic fibrosis (CF) patients. The authors aimed to identify volatile biomarkers from bronchoalveolar lavage (BAL) samples that can guide breath biomarker development for pathogen identification. BAL samples (n = 154) from CF patients were analyzed using two-dimensional gas chromatography time-of-flight mass spectrometry. Random Forest was used to select suites of volatiles for identifying P. aeruginosa-positive and S. aureus-positive samples using multiple infection scenarios and validated using test sets. Using nine volatile molecules, we differentiated P. aeruginosa-positive (n = 7) from P. aeruginosa-negative (n = 53) samples with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.71–1.00) and with positive and negative predictive values of 0.67 (95% CI 0.38–0.75) and 0.92 (95% CI 0.88–1.00), respectively. We were also able to discriminate S. aureus-positive (n = 15) from S. aureus-negative (n = 45) samples with an AUROC of 0.88 (95% CI 0.79-1.00) using eight volatiles and with positive and negative predictive values of 0.86 (95% CI 0.61–0.96) and 0.70 (95% CI 0.61–0.75), respectively. Prospective validation of identified biomarkers as screening tools in patient breath may lead to clinical application.

Cluster resolution feature selection (CR-FS) is a hybrid feature selection algorithm which involves the evaluation of ranked variables via sequential backward elimination (SBE) and sequential forward selection (SFS). The implementation of CR-FS requires two main inputs, namely, start and stop number. The start number is the number of the highly ranked variables for the SBE while the stop number is the point at which the search for additional features during the SFS stage is halted. The setting of these critical parameters has always relied on trial and error which introduced subjectivity in the results obtained. The start and stop numbers are known to vary with each dataset. Drawing inspiration from overlapping coefficients, a method for comparing two probability density functions, empirical equations toward the estimation of start and stop number for a dataset were developed. All of the parameters in the empirical equations are obtained from the comparisons of the two probability density functions except the constant termed d. The equations were optimized using three real-world datasets. The optimum range of d was determined to be 0.48 to 0.57. An implementation of CR-FS using two new datasets demonstrated the validity of this approach. Partial least squares discriminant analysis (PLS-DA) model prediction accuracies increased from 90 and 96 to 100% for both datasets using start and stop numbers calculated with this approach. Additionally, there was a twofold increase in the explained variance captured in the first two principal components. Graphical abstract Here, we describe how to determine the start and stop numbers for an automated feature selection routine, ensuring that you get the best model you can for your data with minimal effort.

Breath is hypothesized to contain clinically relevant information, useful for the diagnosis and monitoring of disease, as well as understanding underlying pathogenesis. Nonhuman primates, such as the cynomolgus macaque, serve as an important model for the study of human disease, including over 70 different human infections. In this feasibility study, exhaled breath was successfully collected in less than 5 min under Biosafety Level 3 conditions from five anesthetized, intubated cynomolgus and rhesus macaques, before and after lung infection with M. tuberculosis. The breath was subsequently analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. A total of 384 macaque breath features were detected, with hydrocarbons being the most abundant. We provide putative identification for 19 breath molecules and report on overlap between the identified macaque breath compounds and those identified in previous human studies.NEW & NOTEWORTHY To the best of our knowledge, this is the first time the volatile molecule content of macaque breath has been comprehensively sampled and analyzed. We do so here in a Biosafety Level 3 setting in the context of M. tuberculosis lung infection. The breath of nonhuman primates represents a novel fluid that could provide insight into disease pathogenesis.

Aims The purpose of this study was to identify the volatile molecules produced by the pathogenic Gram-negative bacterium Klebsiella pneumoniae (ATCC 13883) during in vitro growth using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS). Methods and Results K. pneumoniae ATCC 13883 was incubated in lysogeny broth (LB) to mid-exponential and stationary growth phases. Headspace volatile molecules from culture supernatants were concentrated using solid-phase microextraction (SPME) and analyzed via GC×GC-TOFMS. Ninety-two K. pneumoniae-associated volatile molecules were detected, of which 78 (85%) were detected at both phases of growth and 14 (15%) were detected at either mid-exponential or stationary growth phases. Conclusions This study has increased the total number of reported K. pneumoniae-associated volatile molecules from 77 to 150, demonstrating the sensitivity and resolution achieved by employing GC×GC-TOFMS for the analysis of bacterial headspace volatiles. Significance and Impact of the Study This study represents an early-stage comprehensive volatile metabolomic analysis of an opportunistic bacterial pathogen. Characterizing the volatile molecules produced by K. pneumoniae during in vitro growth could provide us with a better understanding of this organisms’ metabolism; an area that has not been extensively studied to-date. This article is protected by copyright. All rights reserved.

The growth of publicly available repositories, such as the Gene Expression Omnibus, has allowed researchers to conduct meta-analysis of gene expression data across distinct cohorts. In this work, we assess eight imputation methods for their ability to impute gene expression data when values are missing across an entire cohort of Tuberculosis (TB) patients. We investigate how varying proportions of missing data (across 10%, 20%, and 30% of patient samples) influence the imputation results, and test for significantly differentially expressed genes and enriched pathways in patients with active TB. Our results indicate that truncating to common genes observed across cohorts, which is the current method used by researchers, results in the exclusion of important biology and suggest that LASSO and LLS imputation methodologies can reasonably impute genes across cohorts when total missingness rates are below 20%.

Lsr2 is a nucleoid-associated protein conserved throughout the actinobacteria, including the antibiotic-producingStreptomyces.Streptomycesspecies encode paralogous Lsr2 proteins (Lsr2 and Lsr2-like, or LsrL), and we show here that of the two, Lsr2 has greater functional significance. We found that Lsr2 binds AT-rich sequences throughout the chromosome, and broadly represses gene expression. Strikingly, specialized metabolic clusters were over-represented amongst its targets, and the cryptic nature of many of these clusters appears to stem from Lsr2-mediated repression. Manipulating Lsr2 activity in model species and uncharacterized isolates resulted in the production of new metabolites not seen in wild type strains. Our results suggest that the transcriptional silencing of biosynthetic clusters by Lsr2 may protectStreptomycesfrom the inappropriate expression of specialized metabolites, and provide global control overStreptomyces’arsenal of signalling and antagonistic compounds.

Lsr2 is a nucleoid-associated protein conserved throughout the actinobacteria, including the antibiotic-producing Streptomyces. Streptomyces species encode paralogous Lsr2 proteins (Lsr2 and Lsr2-like, or LsrL), and we show here that of the two, Lsr2 has greater functional significance. We found that Lsr2 binds AT-rich sequences throughout the chromosome, and broadly represses gene expression. Strikingly, specialized metabolic clusters were over-represented amongst its targets, and the cryptic nature of many of these clusters appears to stem from Lsr2-mediated repression. Manipulating Lsr2 activity in model species and uncharacterized isolates resulted in the production of new metabolites not seen in wild type strains. Our results suggest that the transcriptional silencing of biosynthetic clusters by Lsr2 may protect Streptomyces from the inappropriate expression of specialized metabolites, and provide global control over Streptomyces’ arsenal of signaling and antagonistic compounds.

We present a QR code paper microfluidic colorimetric assay that can exploit the hardware and software on mobile devices, and circumvent sample preparation by directly targeting volatile biomarkers. Our platform is a printable microarray of well-defined reaction regions, which outputs an instant diagnosis by directing the user to a URL containing their test result, while simultaneously storing epidemiological data for remote access and bioinformatics. To assist in the rapid identification of Escherichia coli in bloodstream infections, we employed an existing colorimetric reagent (p-dimethylaminocinnamaldehyde) and adapted its use to detect volatile indole, a biomarker produced by E. coli. Our assay was able to quantitatively detect indole in the headspace of E. coli culture after 12 h of growth (27.0 ± 3.1 ppm), assisting in species-level identification hours earlier than existing methods. Results were confirmed with headspace solid-phase microextraction (HS-SPME) two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-ToFMS), which estimated indole concentration in E. coli culture to average 32.3 ± 5.2 ppm after 12 h of growth. This QR paper microfluidic platform represents a novel development in both telemedicine and diagnostics using volatile biomarkers. We envision that our QR code platform can be extended to other colorimetric assays for real-time diagnostics in low-resource environments.

The measurement of specific volatile organic compounds in breath has been proposed as a potential diagnostic for a variety of diseases. The most well-studied bacterial lung infection in the breath field is that caused by Pseudomonas aeruginosa. To determine a discriminatory core of molecules in the “breath-print” of mice during a lung infection with four strains of P. aeruginosa (PAO1, PA14, PAK, PA7). Furthermore, we attempted to extrapolate a strain-specific “breath-print” signature to investigate the possibility of recapitulating the genetic phylogenetic groups (Stewart et al. Pathog Dis 71(1), 20–25, 2014. https://doi.org/10.1111/2049-632X.12107 ). Breath was collected into a Tedlar bag and shortly after drawn into a thermal desorption tube. The latter was then analyzed into a comprehensive multidimensional gas chromatography coupled with a time-of-flight mass spectrometer. Random forest algorithm was used for selecting the most discriminatory features and creating a prediction model. Three hundred and one molecules were significantly different between animals infected with P. aeruginosa, and those given a sham infection (PBS) or inoculated with UV-killed P. aeruginosa. Of those, nine metabolites could be used to discriminate between the three groups with an accuracy of 81%. Hierarchical clustering showed that the signature from breath was due to a specific response to live bacteria instead of a generic infection response. Furthermore, we identified ten additional volatile metabolites that could differentiate mice infected with different strains of P. aeruginosa. A phylogram generated from the ten metabolites showed that PAO1 and PA7 were the most distinct group, while PAK and PA14 were interspersed between the former two groups. To the best of our knowledge, this is the first study to report on a ‘core’ murine breath print, as well as, strain level differences between the compounds in breath. We provide identifications (by running commercially available analytical standards) to five breath compounds that are predictive of P. aeruginosa infection.

The analysis of bacterial volatile organic compounds has gained attraction as a non-invasive way to identify disease-causing organisms, given that bacteria have unique metabolisms and volatile metabolic byproducts. In the present research, different adsorbent materials (Carbopack Y, X, B, Carboxen 1000 and Tenax TA), packed singularly or in combination, were compared in terms of sampling performance (sensitivity, repeatability and selectivity) for the extraction of standards and bacterial volatile metabolites in vitro (from Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). After extraction, bacterial volatile organic compounds were desorbed and analyzed in a comprehensive two-dimensional gas chromatography system coupled to a time-of-flight mass spectrometer (GC × GC-ToF MS). The results show that Tenax has the greater ability to extract the standard mix as well as volatile organic compounds with better repeatability (4–26 RSD%), higher sensitivity (on average ∼24 fold) compared to Carbopack Y, X and Carboxen 1000 tube, which followed in terms of performance. In addition, Tenax confirmed the best sensitivity and discriminatory power with no misclassification in the untargeted and unsupervised analysis for the differentiation of the bacterial species.

Tuberculosis (TB) is the deadliest infectious disease, and yet accurate diagnostics for the disease are unavailable for many subpopulations. In this study, we investigate the possibility of using human breath for the diagnosis of active TB among TB suspect patients, considering also several risk factors for TB for smokers and those with human immunodeficiency virus (HIV). The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, non-invasive, and readily available diagnostic service that could positively change TB detection. A total of 50 individuals from a clinic in South Africa were included in this pilot study. Human breath has been investigated in the setting of active TB using the thermal desorption-comprehensive two-dimensional gas chromatography-time of flight mass spectrometry methodology and chemometric techniques. From the entire spectrum of volatile metabolites in breath, three machine learning algorithms (support vector machines, partial least squares discriminant analysis, and random forest) to select discriminatory volatile molecules that could potentially be useful for active TB diagnosis were employed. Random forest showed the best overall performance, with sensitivities of 0.82 and 1.00 and specificities of 0.92 and 0.60 in the training and test data respectively. Unsupervised analysis of the compounds implicated by these algorithms suggests that they provide important information to cluster active TB from other patients. These results suggest that developing a non-invasive diagnostic for active TB using patient breath is a potentially rich avenue of research, including among patients with HIV comorbidities.

Aim There is a requirement of an expansive and up to date review of surgical management of inflammatory bowel disease (IBD) that can dovetail with the medical guidelines produced by the British Society of Gastroenterology. Methods Surgeons who are members of the ACPGBI with a recognised interest in IBD were invited to contribute various sections of the guidelines. They were directed to produce a procedure based document using literature searches that were systematic, comprehensible, transparent and reproducible. Levels of evidence were graded. An editorial board was convened to ensure consistency of style, presentation and quality. Each author was asked to provide a set of recommendations which were evidence based and unambiguous. These recommendations were submitted to the whole guideline group and scored. They were then refined and submitted to a second vote. Only those that achieved >80% consensus at level 5 (strongly agree) or level 4 (agree) after 2 votes were included in the guidelines. Results All aspects of surgical care for IBD have been included along with 157 recommendations for management. Conclusion These guidelines provide an up to date and evidence based summary of the current surgical knowledge in the management of IBD and will serve as a useful practical text for clinicians performing this type of surgery.

As grief can be considered a critical life event, unresolved grief can interfere with quality of life, affecting lifestyle, behavior, emotional strength, and cognitive function. Unresolved grief can even result in suicidal ideation. Counselors can and often do work with grief issues in clients and can promote positive outcomes for grieving clients by addressing personal loss and helping clients process grief related issues. This study was based on an analysis of students within counseling programs accredited by the Council for Accreditation of Counseling and Related Educational Programs based on earlier research conducted by one of the authors. Self-perception of competency in counseling clients who are having grief-related issues from loss was assessed using the Grief Counseling Competency Scale. By analyzing results of the Grief Counseling Competency Scale, participants showed a need for further training in grief counseling skills and theory. Results and implications of the quantitative analysis as well as suggestions for further study are discussed.

In the present research, the potential of breath analysis by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was investigated for the discrimination between healthy and infected mice. A pilot study employing a total of 16 animals was used to develop a method for breath analysis in a murine model for studying Mycobacterium tuberculosis complex (MTBC) using the M. bovis bacillus Calmette-Guérin. Breath was collected in Tedlar bags and concentrated onto thermal desorption tubes for subsequent analysis by GC×GC-MS. Immunological test and bacterial cell count in bronchoalveolar lavage fluid and mice lung homogenate confirmed the presence of bacteria in the infected group. From the GC×GC-MS analysis, 23 molecules were found to mainly drive the separation between control and infected mice and their tentative identification is provided.This study shows that the overall used methodology is able to differentiate breath between healthy and infected animals, and the information herein can be used to further develop the mouse breath model to study MTBC pathogenesis, evaluate pre-clinical drug regimen efficacy, and to further develop the concept of breath-based diagnostics.

The application of manures for fertilization will influence the composition of phosphorus (P) species in the soil profile and the extent of dissolved P leaching to surface waters. Spatial and temporal changes in the P species composition of poultry-manure-amended soil were assessed at two soil column depths (0–5, 10–15 cm) during 10 weeks of leaching. Leachate P was primarily composed of dissolved fractions (soluble reactive P; dissolved unreactive P) and reached a maximum in the fourth week of leaching, concurrent with maxima in the degree of P saturation (DPS; 80%) and water extractable P (WEP, 9 mg kg− 1) in the soil profile. In week 4, orthophosphate (orthoP) represented a greater proportion of total P in the 0–5 cm depth (80% or 11 mg/kg NaOH–EDTA P) than the 10–15 cm depth (72%, 571 mg/kg). In contrast, monoester-(17%, 150 mg kg− 1) and diester-P classes (10%, 60 mg kg− 1) were most abundant in the 10–15 cm depth. In week 4, the 31P NMR spectrum of the 0–5 cm depth soil was most similar to that of the poultry manure, which may have contributed to the mobility of P species, including orthoP, myo-inositol hexakisphosphate (phytate or myo-IHP), and nucleic acids. The appearance of neo- and D -chiro-IHP, as well as phospholipid signals during the leaching period indicate possible short-term (

Previous studies have demonstrated that Pseudomonas aeruginosa PAO1 is chemotactic towards proteinogenic amino acids, however, the chemotaxis response of this strain towards non-proteinogenic amino acids and the specific chemoreceptors involved in this response are essentially unknown. In this study, we analysed the chemotactic response of PAO1 towards two degradation products of elastin, the lysine-rich, non-proteinogenic amino acids, desmosine and isodesmosine. We observed that isodesmosine, a potential biomarker for different diseases, served as a chemoattractant for PAO1. A screen of 251methyl-accepting chemotaxis proteins mutants of PAO1 identified PctA as the chemoreceptor for isodesmosine. We also showed that the positive chemotactic response to isodesmosine is potentially common by demonstrating chemoattraction in 12 of 15 diverse (in terms of source of isolation) clinical isolates, suggesting that the chemotactic response to this non-proteinogenic amino acid might be a conserved feature of acute infection isolates and thus could influence the colonization of potential infection sites.

Lake sediments are known to be a significant source of phosphorus (P) to plankton populations under certain biogeochemical conditions; however, the contribution of sediment organic P (P) to internal P loads remains poorly understood. We investigated P speciation and bioavailability in sediments collected over multiple months from a shallow, eutrophic bay in Lake Champlain (Missisquoi Bay, VT) using solution P nuclear magnetic resonance (NMR) spectroscopy and enzymatic hydrolysis (EH) analysis of sediments collected during years with (2008) and without (2007) algal blooms. Sediments collected during bloom onset (July) and peak bloom (August) months contained the largest proportion of enzyme-labile P, whereas pre- and postbloom sediments were primarily composed of nonlabile P. Monoester P to diester P ratios changed with respect to depth, particularly during bloom periods. Monoester P and DNA accumulation, likely from settling particulate matter, began at the onset of the bloom and continued into October 2008 during the postbloom period. The disappearance of inositol hexakisphosphate stereoisomers and the generation of orthophosphate at lower sediment depths was also evident in August 2008. Principal components analysis of EH and NMR species proportions confirmed differences between sediment cores collected during bloom onset and peak bloom, compared with pre- and postbloom sediments. Large enzyme-labile and P species proportions corresponded to increased sediment P flux and reduced manganese and iron species in porewater. These findings suggest that interseasonal changes in P speciation may influence P mobility in sediments and contribute to important feedback dynamics between biological productivity and sediment water interface geochemistry.

The potential of high-resolution analytical technologies like GC × GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC × GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC × GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC × GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses.