Search

Inflammatory bowel disease represents a wide range of pathologies and disease states including Crohn's Disease (CD) and Ulcerative colitis (UC). Despite extensive research, the exact immunopathogenesis of Crohn's disease remains unclear, but a variety of studies over the years have pointed to yeast as a potential source antigen of uncertain significance. The aim of this review is to summarize the current literature investigating the association between Crohn's disease patients and their responses to yeast. To do this, we performed a literature review by looking at clinical and translational research regarding the immunopathogenesis of Crohn's disease, yeast and its associated biomarkers, and overall patient response to dietary yeast published between 1 January 1990 and 1 October 2024 that were indexed on PubMed and Google Scholar with the majority written in English. It was found that antibodies against Saccharomyces cerevisiae (ASCA) have proven to be highly specific for CD during the workup of IBD and may have diagnostic value for the purpose of excluding ulcerative colitis. For CD patients, there appears to be a dysregulated immune response to antigens like yeast that results in abnormal mucosal permeability and thus increases antigen presentation to the immune system. In addition, ASCA and its immunoglobulin modifications have been shown to have significant potential in the prediction of CD onset and disease course. Interestingly, although other abnormally structured antibodies can be found in CD patients' serum for years preceding diagnosis, there appears to be no relation between ASCA and dietary yeast sensitivity by CD patients. In conclusion, significant research efforts have been made in recent years to explore the role of diet in the disease course and management of patients with Crohn's disease. The immunological role of antigens including yeast in CD is complex and may represent an important pathogenetic factor in addition to influencing the specific phenotype of the disease. Unfortunately, no single specific diet is superior for the management of IBD, and individualized patient treatment by experts in the field is best for adjunctive therapy. New studies characterizing the microbiome of CD patients and also using immune markers/gene modifications to predict disease outcomes have shown to be quite promising. However, further research is required to investigate the CD yeast response and its role in the pathogenesis, diagnosis and treatment of CD. [ABSTRACT FROM AUTHOR]

Introduction. Serological diagnosis of inflammatory bowel diseases (IBD) is an additional tool not only for differential diagnosis, but also for individual prediction of the clinical course and long-term outcomes of Crohn’s disease (CD) and ulcerative colitis (UC).The objective was to assess the occurrence and capabilities of determining antibodies to Saccharomyces cerevisiae (ASCA) and antineutrophil cytoplasmic antibodies (ANCA) in predicting the clinical outcomes of IBD.Methods and materials. The study included 71 patients with CD, 26 with UC, and 21 with and 21 with IBD unclassified (IBDU). The comparison group consisted of 35 patients with other gastrointestinal diseases (irritable bowel syndrome with diarrhea (IBS-D), celiac disease, autoimmune gastritis (AIG)); the control group consisted of 24 apparently healthy individuals. The level of antibodies to ASCA IgA and IgG was measured by the ELISA method (ORGENTEC Diagnostika GmbH, Germany), ANCA IgG was determined by the IIF method of the Granulocyte Mosaic test system (EUROIMMUN AG, Germany).Results. The occurrence of ASCA IgA and IgG in patients with CD was 25 % and 38 %, which is significantly higher compared to patients with UC (0 % and 3.8 %), IBDU (5 % and 5 %), AIG (0 % and 5.3 %) respectively (p

ABSTRACTCandida albicans is an immunogen for anti-Saccharomyces cerevisiae antibodies (ASCA), a serological marker of Crohn’s disease. ASCA has also been reported in other autoimmune diseases, including coeliac disease (CeD). A strong antibody response against Hwp1, a protein associated with invasive hyphal form of C. albicans which presents peptide sequence homologies with gliadin, has also been described in CeD. This observation supports the hypothesis that C. albicans hyphal transition in C. albicans may trigger CeD onset through a mechanism of molecular/antigenic mimicry. In this study, we assessed whether the anti-C. albicans oligomannose and anti-Hwp1 protein responses may be linked despite their different pathophysiological significance. The measurement of ASCA levels in a cohort of patients involved in our previous Hwp1 study showed a significant correlation between the two biomarkers. This new observation further reinforces the link between C. albicans and CeD.

Severe obesity is linked to a low-grade inflammatory process due to enlarged adipose tissue, resulting in elevated pro-inflammatory cytokines. Bariatric surgery induces anatomical changes, causing intestinal inflammation marked by anti-Saccharomyces cerevisiae (ASCA) antibodies. This study aimed to assess ASCA IgG/IgA levels preoperatively and 12 months post-surgery, correlating them with systemic inflammation markers (IL-6, CRP, MCP-1). Participants (BMI > 35 kg/m2) were recruited in South Brazil. Severe obesity individuals showed elevated IL-6 (p = 0.002), CRP (p

This study focuses on exploring and understanding measurement errors in analytical procedures involving miniaturized near-infrared instruments. Despite recent spreading in different application fields, there remains a lack of emphasis on the accuracy and reliability of these devices, which is a critical concern for accurate scientific outcomes. The study investigates multivariate measurement errors, revealing their complex nature and the influence that preprocessing techniques can have. The research introduces a possible workflow for practical error analysis in experiments involving diverse samples and instruments. Notably, it investigates how sample characteristics impact errors in the case of solid pills and tablets, typical pharmaceutical samples. ASCA was used for understanding critical instrumental factors and the potential and limitations of the method in the current application were discussed. The joint interpretation of multivariate error matrices and their resume through image histograms and K index are discussed in order to evaluate the impact of common preprocessing methods and to assess their influence on signals. [ABSTRACT FROM AUTHOR]

The role of biomarkers in the diagnosis of inflammatory bowel disease is not fully characterized. C-reactive protein has a short half-life and elevates quickly after the onset of an inflammatory process; the performance is better in Crohn's disease than in ulcerative colitis. Erythrocyte sedimentation rate is easy to determine, widely available, and cheap, but the long half-life, the influence of age, anemia, smoking, and drugs limit its usefulness. Fecal markers have good specificity, but suboptimal accuracy. Microbial antibodies and novel immunological markers show promise but need further evidence before entering clinical practice. Proteomic methods could represent the dawn of a new era of stool protein/peptide biomarker panels able to select patients at risk of inflammatory bowel disease. [ABSTRACT FROM AUTHOR]

Wine is a temperature, light, and oxygen-sensitive product, so its physicochemical characteristics can be modified by variations in temperature and time when samples are either sampled, transported, and/or analyzed. These changes can alter its metabolomic fingerprinting, impacting further classification tasks and quality/quantitative analyses. For these reasons, the aim of this work is to compare and analyze the information obtained by different chemometric methods used in a complementary form (PCA, ASCA, and PARAFAC) to study 1H-NMR spectra variations of four red wine samples kept at different temperatures and time lapses. In conjunction, distinctive changes in the spectra are satisfactorily tracked with each chemometric method. The chemometric analyses reveal variations related to the wine sample, temperature, and time, as well as the interactions among these factors. Moreover, the magnitude and statistical significance of the effects are satisfactorily accounted for by ASCA, while the time-related effects variations are encountered by PARAFAC modeling. Acetaldehyde, formic acid, polyphenols, carbohydrates, lactic acid, ethyl lactate, methanol, choline, succinic acid, proline, acetoin, acetic acid, 1,3-propanediol, isopentanol, and some amino acids are identified as some of the metabolites which present the most important variations. [ABSTRACT FROM AUTHOR]

Antibody testing in inflammatory bowel disease (IBD) can add to diagnostic accuracy of the main subtypes Crohn's disease (CD) and ulcerative colitis (UC). Whether modern modeling techniques such as supervised and unsupervised machine learning are of value for finer distinction of subtypes such as IBD-unclassified (IBD-U) is not known. We determined the antibody profile of 100 adult IBD patients from the Swiss IBD cohort study with known subtype (50 CD, 50 UC) as well as of 76 IBD-U patients. We included ASCA IgG and IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA measurements for computing different antibody panels as well as machine learning models. The AUC of an optimized antibody panel was 85% (95%CI, 78–92%) to distinguish CD from UC patients. The antibody profile of IBD-U patients was closely related to UC. No specific antibody profile was predictive for IBD-U nor for re-classification. The panel diagnostic was in favor of UC reclassification prediction with a correct assignment rate of 69.2–73.1% depending on the cut-off applied. Supervised machine learning could not distinguish between CD, UC, and IBD-U. More so, unsupervised machine learning suggested only two distinct clusters as a likely number of IBD subtypes. Antibodies in IBD are supportive in confirming clinical determined subtypes CD and UC but have limited capacity to predict IBD-U and reclassification during follow-up. In terms of antibody profiles, IBD-U is not a distinct subtype of IBD. [ABSTRACT FROM AUTHOR]

Featured Application: Multi-platform analysis of donkey milk. Donkey milk represents an efficient substitute for human milk in infants' diets being unlikely to cause allergic reactions. In this study, different donkey milks were collected at two lactation times (T0 and T1), subjected to freezing–thawing and freeze-drying, and analyzed by Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) and ATR-FT-IR. The data collected on freeze–thaw (FT-) and reconstituted (R-)milks were investigated by ANOVA–Simultaneous Component Analysis (ASCA) and Principal Component Analysis (PCA). The following concentrations (µg/mL) for FT and R-milks, respectively, at T0, were found: Ca: 712 ± 71, 600 ± 72; Fe: 0.7 ± 0.3, 0.1 ± 0.1; K: 595 ± 49, 551 ± 59; Mg: 75 ± 5, 67 ± 4; Na: 117 ± 16, 114 ± 16; P: 403 ± 30, 404 ± 38; Zn: 1.6 ± 0.2, 1.6 ± 0.3. At T1, the concentrations (µg/mL for FT and R-milks, respectively) were: Ca: 692 ± 60, 583 ± 43; Fe: 0.13 ± 0.02, 0.13 ± 0.03; K: 641 ± 71, 574 ± 61; Mg: 72 ± 4, 63 ± 1; Na: 116 ± 9, 109 ± 8; P: 412 ± 30, 405 ± 24; Zn: 1.6 ± 0.3, 1.6 ± 0.3. ASCA demonstrated the treatment has a substantial effect, and PCA revealed that the largest quantities of metals, specifically Fe, Mg, and Ca for T0 and K, P, and Na for T1, are present in the FT-milk samples. The IR spectra of FT- and R-milks revealed no macroscopic changes among them or between lactation periods, indicating this technique may not suitably capture variability in lactation or conservation processes in donkey milk. Despite the relatively small sample size, this study offers insight on the mineral composition changes in donkey milk and emphasizes the significance of milk preprocessing and the lactation period on it. [ABSTRACT FROM AUTHOR]

The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors "grape maturity over time" and "position of the grape" (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA–simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest. [ABSTRACT FROM AUTHOR]

The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments. [ABSTRACT FROM AUTHOR]

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

The increasing availability of multivariate data within biomedical research calls for appropriate statistical methods that can describe and model complex relationships between variables. The extended ANOVA simultaneous component analysis (ASCA+) framework combines general linear models and principal component analysis (PCA) to decompose and visualize the separate effects of experimental factors. It has recently been demonstrated how linear mixed models can be included in the framework to analyze data from longitudinal experimental designs with repeated measurements (RM-ASCA+). The ALASCA package for R makes the ASCA+ framework accessible for general use and includes multiple methods for validation and visualization. The package is especially useful for longitudinal data and the ability to easily adjust for covariates is an important strength. This paper demonstrates how the ALASCA package can be applied to gain insights into multivariate data from interventional as well as observational designs. Publicly available data sets from four studies are used to demonstrate the methods available (proteomics, metabolomics, and transcriptomics).

Background/Aims Anti-Saccharomyces cerevisiae antibodies (ASCA) has been recognized to be associated with altered gut permeability in the inflammatory bowel disease (IBD). This study investigated how Saccharomyces cerevisiae infection involved in the prognosis of IBD with multi-omics approach. Methods This is a multicenter, prospective cohort study. Enrolled subjects with IBD were categorized with the level of ASCA seropositivity. Clinical characteristics, metagenomic sequencing, metabolomic analysis, and single cell transcriptomics were analyzed. Results A total of 166 subjects were enrolled. Behavior of Crohn’s disease, initial endoscopic severity, and erythrocyte sedimentation rate were significantly different according to the ASCA seropositivity. Fecal metagenomic and metabolomic analysis was conducted. Microbial alpha- and beta-diversity revealed both significantly different between the groups, especially between negative and positive groups. Specific genus relative abundance analysis, volcano plot and random forest analysis showed substantial microbial alteration pattern in the positive group compared with the other groups. Metabolomic analysis expressed significant different in the bile acid pathway in the subjects with ulcerative colitis. It revealed that primary bile acid production was increased and secondary bile acid production was decreased relatively in the positive group. Moreover, indole and trimethylamine N-oxide synthesis were significantly decreased in the positive group. Single-cell RNA sequencing was conducted with colonic mucosa from the subjects with ulcerative colitis. Substantial reduction in the frequency of ILC3 and expression of IL18 and IL22 in ILC3s, along with a significant decrease in tight junction-associated genes (TJP1, CLDN3, CLDN4) within epithelial cells were observed in the positive group. In addition, data from positive group exhibited decreased levels of CSF2 in ILC3s, which is likely contributing to reduced M2 macrophage polarization. Conclusion These data imply that Saccharomyces cerevisiae infection was associated with comprised gut barrier regulation via microbiome-metabolome interaction resulting to affect the gut microenvironment of IBD. [ABSTRACT FROM AUTHOR]

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA-simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple-negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted-based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow-up in the clinical practice. [ABSTRACT FROM AUTHOR]

Background: The present work represents a feasibility study for the realization of an analytical method finalized to the detection of expired antibiotic tablets. The work focuses on a specific antibiotic drug and represents the preliminary study upstream of a larger-scale work. Methods: attenuated Total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) spectra coupled with sequential preprocessing through an orthogonalization (SPORT) chemometric approach were used to discriminate between expired and compliant tablets. Conclusions: The highest predictive accuracy (93.3% of correct classification rate in external validation, corresponding to 1 misclassified test sample over 15) was achieved by analyzing intact tablets. This represents an excellent result because it gives indications regarding the possibility of determining, in a completely non-destructive way, the presence of expired drugs. [ABSTRACT FROM AUTHOR]

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis—ASCA, regularized MANOVA–rMANOVA, and Group-wise ANOVA-simultaneous component analysis—GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method. [ABSTRACT FROM AUTHOR]

The aim of this work is to assess the potentialities of the synergistic combination of an ultra-fast chromatography-based electronic nose as a fingerprinting technique and multivariate data analysis in the context of food quality control and to investigate the influence of some factors, i.e., basil variety, cut, and year of crop, in the final aroma of the samples. A low = level data fusion approach coupled with Principal Component Analysis (PCA) and ANOVA—Simultaneous Component Analysis (ASCA) was used in order to analyze the chromatographic signals acquired with two different columns (MXT-5 and MXT-1701). While the PCA analysis results highlighted the peculiarity of some basil varieties, differing either by a higher concentration of some of the detected chemical compounds or by the presence of different compounds, the ASCA analysis pointed out that variety and year are the most relevant effects, and also confirmed the results of previous investigations. [ABSTRACT FROM AUTHOR]

The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments.

The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors “grape maturity over time” and “position of the grape” (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA–simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest.

Mid-infrared spectroscopy has been developed as a reliable and rapid tool for routine analysis of fat, protein, lactose and other components in liquid milk. However, variations within and between FTIR instruments, even within the same milk testing laboratory, present a challenge to the accuracy of measurement of particularly minor components in the milk, such as individual fatty acids or proteins. In this study we have used Analysis of variance–Simultaneous Component Analysis (ASCA), to monitor the spectral variation between and within each of four different FOSS FTIR spectrometers over each week in an independent milk testing laboratory over 4 years, between August 2017 and March 2021 (223 weeks). On everyday of each week, spectra of the same pilot milk sample were recorded approximately every hour on each of the four instruments. Overall, variations between instruments had the largest effect on spectral variation over each week, making a significant contribution every week. Within each instrument, day-to-day variations over the week were also significant for all but two of the weeks measured, however it contributed less to the variance overall. At certain times other factors not explained by weekday variation or inter-instrument variation dominated the variance in the spectra. Examination of the scores and loadings of the weekly ASCA analysis allowed identification of changes in the spectral regions affected by drifts in each instrument over time. This was found to particularly affect some of the fatty acid predictions.

Protein catabolism by intestinal bacteria is infamous for releasing many harmful compounds, negatively affecting the health status, both locally and systemically. In a previous study, we enriched in protein degraders the fecal microbiota of five subjects, utilizing a medium containing protein and peptides as sole fermentable substrates and we monitored their evolution by 16S rRNA gene profiling. In the present study, we fused the microbiome data and the data obtained by the analysis of the volatile organic compounds (VOCs) in the headspace of the cultures. Then, we utilized ANOVA simultaneous component analysis (ASCA) to establish a relationship between metabolites and bacteria. In particular, ASCA allowed to separately assess the effect of subject, time, inoculum concentration, and their binary interactions on both microbiome and volatilome data. All the ASCA submodels pointed out a consistent association between indole and Escherichia–Shigella, and the relationship of butyric, 3-methyl butanoic, and benzenepropanoic acids with some bacterial taxa that were major determinants of cultures at 6 h, such as Lachnoclostridiaceae (Lachnoclostridium), Clostridiaceae (Clostridium sensu stricto), and Sutterellaceae (Sutterella and Parasutterella). The metagenome reconstruction with PICRUSt2 and its functional annotation indicated that enrichment in a protein-based medium affected the richness and diversity of functional profiles, in the face of a decrease of richness and evenness of the microbial community. Linear discriminant analysis (LDA) effect size indicated a positive differential abundance (p < 0.05) for the modules of amino acid catabolism that may be at the basis of the changes of VOC profile. In particular, predicted genes encoding functions belonging to the superpathways of ornithine, arginine, and putrescine transformation to GABA and eventually to succinyl-CoA, of methionine degradation, and various routes of breakdown of aromatic compounds yielding succinyl-CoA or acetyl-CoA became significantly more abundant in the metagenome of the bacterial community.

Protein catabolism by intestinal bacteria is infamous for releasing many harmful compounds, negatively affecting the health status, both locally and systemically. In a previous study, we enriched in protein degraders the fecal microbiota of five subjects, utilizing a medium containing protein and peptides as sole fermentable substrates and we monitored their evolution by 16S rRNA gene profiling. In the present study, we fused the microbiome data and the data obtained by the analysis of the volatile organic compounds (VOCs) in the headspace of the cultures. Then, we utilized ANOVA simultaneous component analysis (ASCA) to establish a relationship between metabolites and bacteria. In particular, ASCA allowed to separately assess the effect of subject, time, inoculum concentration, and their binary interactions on both microbiome and volatilome data. All the ASCA submodels pointed out a consistent association between indole and Escherichia–Shigella , and the relationship of butyric, 3-methyl butanoic, and benzenepropanoic acids with some bacterial taxa that were major determinants of cultures at 6 h, such as Lachnoclostridiaceae (Lachnoclostridium), Clostridiaceae (Clostridium sensu stricto), and Sutterellaceae (Sutterella and Parasutterella). The metagenome reconstruction with PICRUSt2 and its functional annotation indicated that enrichment in a protein-based medium affected the richness and diversity of functional profiles, in the face of a decrease of richness and evenness of the microbial community. Linear discriminant analysis (LDA) effect size indicated a positive differential abundance (p < 0.05) for the modules of amino acid catabolism that may be at the basis of the changes of VOC profile. In particular, predicted genes encoding functions belonging to the superpathways of ornithine, arginine, and putrescine transformation to GABA and eventually to succinyl-CoA, of methionine degradation, and various routes of breakdown of aromatic compounds yielding succinyl-CoA or acetyl-CoA became significantly more abundant in the metagenome of the bacterial community. [ABSTRACT FROM AUTHOR]

This article outlines the challenges that military-connected students face and discusses ways in which school counselors may utilize each of the four components of the ASCA National Model to help this population. Finally, a case study is presented to demonstrate how school counselors may support military-connected students. Utilizing the ASCA model is especially important when working with military-connected students, who may have needs that are unfamiliar to many school counselors. [ABSTRACT FROM AUTHOR]

Abstract Background Serological markers used for diagnostic purposes and disease stratification in inflammatory bowel disease. We aimed to investigate the frequency of ASCA and ANCA among Arab Bedouin IBD patients and its relationship to disease phenotype and course. Methods From cohort of 68, 25 Crohn’s disease (CD) and 25 Ulcerative colitis (UC) patients were recruited (72%). ASCA IgG was determined by ELISA assay. Immunofluorescence analysis of ANCA was performed. Results The IgG ASCA was detected in 13 (52%) of the CD patients and in three (12%) UC patients. The prevalence of ANCA among UC patients was positive with 76%, sub-grouped, atypical ANCA in 9 patients (36%), pANCA in six patients (24%) and cANCA in 4 patients (16%). The detection of ASCA among CD patients was found not to be a reliable predictor of young age at diagnosis, gender, ileal involvement, anti-TNF treatment or surgery. UC patients with positive ANCA were younger, mean age 40.2 ± 11.9 compared with 57.3 ± 21.2 (p = 0.03), and diagnosed at a younger age, 29.2 ± 11.8 compared with 43.5 ± 15.3 (p = 0.05). Conclusion The frequency of ASCA among Bedouin CD patients and ANCA among UC patients was high, however ASCA was not found to have a predictive value for disease phenotype or course. Positive ANCA in UC patients was predictive for younger age and age at diagnosis.

Vulvovaginal candidiasis (VVC) is primarily caused by Candida albicans and affects 75% of childbearing age women. Although C. albicans can colonize asymptomatically, disease is associated with an increased Candida burden, a loss of epithelial tolerance and a breakdown in vaginal microbiota homeostasis. VVC symptoms have been ascribed to a powerful inflammatory response associated with the infiltration of non-protective neutrophils (PMN). Here, we compared the immunological characteristics of vaginal fluids and cellular protein extracts obtained from 28 VVC women and from 23 healthy women colonized by Candida spp. We measured the levels of antibodies against fungal antigens and human autoantigens (anti-Saccharomyces cerevisiae antibodies (ASCA), C. albicans germ tube antibodies (CAGTAs) and perinuclear anti-neutrophil cytoplasmic antibodies (pANCA)), in addition to other immunological markers. Our results show that the pANCA levels detected in the cellular protein extracts from the vaginal fluids of symptomatic women were significantly higher than those obtained from healthy colonized women. Consistent with a potential physiologically relevant role for this pANCA, we found that specific anti-myeloperoxidase antibodies could completely neutralize the ex vivo killing capacity of polymorphonuclear cells. Collectively, this preliminary study suggests for the first time that pANCA are found in the pathogenic vaginal environment and can promptly impair neutrophil function against Candida, potentially preventing a protective response. [ABSTRACT FROM AUTHOR]

Biomarkers are needed to identify patients at risk for development of inflammatory bowel diseases. We aimed to identify serum biomarkers of Crohn's disease and ulcerative colitis that can be detected and quantified before diagnosis. We obtained serum samples from patients archived before a diagnosis of Crohn's disease (n = 200) or ulcerative colitis (n = 199), as well as from 200 healthy individuals (controls), collected from 1998 through 2013 as part of the US Defense Medical Surveillance System. We measured levels of antibodies against microbes (anti– Saccharomyces cerevisiae IgA or IgG, anti– Escherichia coli outer membrane porin C, anti-CBir1, anti-flagellin 2, anti-flagellin X, and perinuclear anti-neutrophil cytoplasmic antibodies) and 1129 proteins in each sample. We then used functional principal component analysis to derive the time-varying trajectory for each marker, which then was used in a multivariate model to predict disease status. Predictive performances at different prediagnosis timepoints were evaluated using area under the receiver operating characteristic curves (AUROCs). Biological pathways that were up-regulated in serum from patients with Crohn's disease were identified based on changes in protein abundance at different time periods preceding diagnosis. We identified a panel of 51 protein biomarkers that were predictive of Crohn's disease within 5 years with an AUROC of 0.76 and a diagnosis within 1 year with an AUROC of 0.87. Based on the proteins included in the panel, imminent development of CD was associated with changes in the complement cascade, lysosomes, innate immune response, and glycosaminoglycan metabolism. Serum antibodies and proteins identified patients who received a diagnosis of ulcerative colitis within 5 years with an AUROC of only 0.56 and within 1 year with an AUROC of 0.72. We identified a panel of serum antibodies and proteins that were predictive of patients who will receive a diagnosis of Crohn's disease within 5 years with high accuracy. By contrast we did not identify biomarkers associated with future diagnosis of ulcerative colitis. [ABSTRACT FROM AUTHOR]

Solving a system of multivariate quadratic equations obtained through algebraic cryptanalysis is a nondeterministic polynomial time-complete problem. Owing to the trend of stream ciphers based on nonlinear update, the success of algebraic attacks has been limited to their reduced variants. On the other hand, side channel attacks (SCAs), although require a continued access to the target device for capturing leakages, are a potent threat against the stream ciphers. Algebraic SCA (ASCA) combines and solves equations obtained through algebraic cryptanalysis and partial SCA of cipher implementation. ASCA is successfully being applied against block ciphers since 2009; however, there is no existing published work on ASCA against stream ciphers as per our knowledge. In this paper, we propose an idea of mounting ASCA on stream ciphers, and we demonstrated it through the application of ASCA on trivium and grain stream ciphers.

Analyses of multifactorial experimental designs are used as an explorative technique describing hypothesized multifactorial effects based on their variation. The procedure of analyzing multifactorial designs is well established for univariate data, and it is known as analysis of variance (ANOVA) tests, whereas only a few methods have been developed for multivariate data. In this work, we present the weighted-effect ASCA, named WE-ASCA, as an enhanced version of ANOVA-simultaneous component analysis (ASCA) to deal with multivariate data in unbalanced multifactorial designs. The core of our work is to use general linear models (GLMs) in decomposing the response matrix into a design matrix and a parameter matrix, while the main improvement in WE-ASCA is to implement the weighted-effect (WE) coding in the design matrix. This WE-coding introduces a unique solution to solve GLMs and satisfies a constrain in which the sum of all level effects of a categorical variable equal to zero. To assess the WE-ASCA performance, two applications were demonstrated using a biomedical Raman spectral data set consisting of mice colorectal tissue. The results revealed that WE-ASCA is ideally suitable for analyzing unbalanced designs. Furthermore, if WE-ASCA is applied as a preprocessing tool, the classification performance and its reproducibility can significantly improve.

Abstract Acute exposure to nicotinic agonists induces myotoxicity in zebrafish embryos. The main goal of this work was to evaluate the potential myotoxicity of nicotine acetylcholine receptor agonists on adult zebrafish muscle tissue by using nicotine as a model compound. Liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) datasets were processed with different chemometric tools based on the selection of Regions of Interest (ROI) and Multivariate Curve-Resolution (ROI-MCR procedure) Alternating Least Squares (ALS) for the analysis of different exposure experiments. Analysis of Variance Simultaneous Component Analysis (ASCA) of changes on metabolite peak profile areas showed significant nicotine concentration and exposure time-dependent changes, clearly differentiating between exposed and non-exposed samples and between short (2 h) and long exposure times (6 h or 24 h). Most of the changes observed in the concentrations of different metabolites are probably secondary to the observed hyperlocomotion, as they have been also observed in humans after strenuous muscular exercise. The absence of myotoxicity might be related with the reduced calcium permeability of adult muscle-type nicotinic acetylcholine receptors (nAChRs). Graphical abstract fx1 Highlights • Metabolites of zebrafish were determined following LC-HRMS and different chemometric strategies. • ASCA revealed that nicotine dose and exposure times produced significant changes in metabolite concentrations. • Nicotine exposure increased carnitine and acetylcarnitine, content in skeletal muscle. • The increase in serotonin level should be important for the hyperlocomotion observed in nicotine-treated zebrafish. [ABSTRACT FROM AUTHOR]

Background: Serological markers used for diagnostic purposes and disease stratification in inflammatory bowel disease. We aimed to investigate the frequency of ASCA and ANCA among Arab Bedouin IBD patients and its relationship to disease phenotype and course.Methods: From cohort of 68, 25 Crohn's disease (CD) and 25 Ulcerative colitis (UC) patients were recruited (72%). ASCA IgG was determined by ELISA assay. Immunofluorescence analysis of ANCA was performed.Results: The IgG ASCA was detected in 13 (52%) of the CD patients and in three (12%) UC patients. The prevalence of ANCA among UC patients was positive with 76%, sub-grouped, atypical ANCA in 9 patients (36%), pANCA in six patients (24%) and cANCA in 4 patients (16%). The detection of ASCA among CD patients was found not to be a reliable predictor of young age at diagnosis, gender, ileal involvement, anti-TNF treatment or surgery. UC patients with positive ANCA were younger, mean age 40.2 ± 11.9 compared with 57.3 ± 21.2 (p = 0.03), and diagnosed at a younger age, 29.2 ± 11.8 compared with 43.5 ± 15.3 (p = 0.05).Conclusion: The frequency of ASCA among Bedouin CD patients and ANCA among UC patients was high, however ASCA was not found to have a predictive value for disease phenotype or course. Positive ANCA in UC patients was predictive for younger age and age at diagnosis. [ABSTRACT FROM AUTHOR]