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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

The introgression of genes originating from native Maíz Dulce breeds from Mexico to non-sweet parent lines was carried out; as a hypothesis, the use of recurrent selection is an alternative for the transfer of the sweetness gene to non-sweet lines for the formation of hybrids. In the research, we look for parent lines with general and specific combining ability with the aim of creating a heterotic pattern for the formation of sweet corn hybrids with su1 sweetness gene because in Mexico there is no seed production of this genetic material. Lines CML311, CML78, LUG282, LUG03, and LUG20 and the breeds Dulcillo del Noroeste and Maíz Dulce. Genetic crossbreeding designs, line by progeny tester and North Carolina Design II; we measured the effects of general and specific combining ability of lines and crosses; a combined analysis of variance of three evaluations in two evaluation cycles and multiple selection indices of the variables of the best crosses were performed to establish heterotic groups; years of evaluation, from 2017 to 2021 in Zapopan, Jalisco. In conclusion, we found three parent lines that belong to the maternal heterotic group with germplasm of Dulcillo del Noroeste and LUG282 and four parent lines for the paternal heterotic group with germplasm of maize dulce and CML311. The results showed a contribution to the transfer of the su1 sweetness gene to non-sweet lines; the hybrid crosses developed presented quality in plant and ear. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC‐MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi‐toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute‐and‐shoot LC‐MS/MS‐based multi‐analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins. RESULTS: We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi‐mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions. CONCLUSION: The results presented in this study underline the added value of combining targeted LC‐MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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]

Summary: Many modern analytical methods are used to analyze samples issued from an experimental design, for example, in medical, biological, chemical, or agronomic fields. Those methods generate most of the time, highly multivariate data like spectra or images, where the number of variables (descriptor responses) tends to be much larger than the number of experimental units. Therefore, multivariate statistical tools are necessary to identify variables that are consistently affected by experimental factors. In this context, two recent methods combining ANOVA and PCA, namely, ASCA (ANOVA‐Simultaneous Component Analysis) and APCA (ANOVA‐Principal Component Analysis), were developed. They provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design. Their main limitation is that they produce biased estimators of the factor effects when the design of experiment is unbalanced. This article presents the R package limpca (for linear models with principal component effects analysis) that implements ASCA+ and APCA+, an enhanced version of ASCA and APCA methods based on several principles from the theory of general linear models (GLM). In this paper, the methodology is reviewed, the package structure and functions are presented, and a metabolomics data set is used to clearly demonstrate the potential of ASCA+ and APCA+ methods to highlight true biomarkers corresponding to effects of interest in unbalanced designs. This article presents the R package limpca that implements ASCA+ and APCA+ methods. Those two methods extend the use of ASCA and APCA to unbalanced designs with several principles from the theory of general linear models. The package structure is presented and applied on metabolomics data to highlight biomarkers corresponding to effects of interest in an experimental design. [ABSTRACT FROM AUTHOR]

A novel chemometric approach is proposed to analyze high‐dimensional data from unbalanced designs of experiments. It combines a rebalancing strategy based on averages with the ASCA method under the name Rebalanced ASCA (RASCA). The ability of RASCA to handle unbalanced designs was compared with standard ASCA, as well as state‐of‐the‐art methods, such as ASCA+ and WE‐ASCA. For that purpose, a controlled framework was designed to provide a systematic comparison of the various approaches. It included two real datasets obtained from initially balanced designs, which were gradually unbalanced by removing observations belonging to specific combinations of factor levels. The results illustrate that all methods considered led to identical solutions when the initial balanced design was kept. Nevertheless, increasing differences appeared when the design was gradually unbalanced. The proposed benchmark showed that RASCA and ASCA+ provided overall similar results for all effects with high agreement with the balanced solutions in comparison to classical ASCA and WE‐ASCA. RASCA was found to be a suitable chemometric tool to tackle unbalanced designs by ensuring unbiased parameter estimators with the added benefit of producing rigorously orthogonal effect matrices, thus facilitating interpretation. [ABSTRACT FROM AUTHOR]

Permutation tests are the standard technique for significance testing in Analysis of Variance Simultaneous Component Analysis. However, there is a vast number of alternative approaches for permutation testing, and the number of choices grows in relation to the complexity of the study design. In this paper, we focus on longitudinal intervention studies with multivariate outcomes, a relevant experimental design in clinical studies where the outcome is an omics profile (such as in genomics, metabolomics, and the like). We propose a new technique to derive power curves tailored to the size and (un)balanced nature of the data set in the study. This technique is useful to identify misleading permutation tests, with lack of power or overly optimistic outcomes. We found that choosing the best permutation approach is far from intuitive and that there is a significant risk of deriving incorrect conclusions in real‐life analyses. Our approach avoids this risk and can be extended to other complex designs of interest. The code is available for free use. Permutation tests are the standard technique for significance testing in ASCA. However, there is a vast number of alternative approaches. In this paper, we focus on clinical studies where the outcome is an omics profile (such as in metabolomics and the like). We propose a new technique to derive power curves tailored to the size and (un)balanced nature of the data set. This technique is useful to identify misleading permutation tests, with lack of power or overly optimistic outcomes. [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.

[Display omitted] • First ASCA SWIR spectroscopy study on barley germination, revealing varietal dynamics. • ASCA shows differences between germinated and ungerminated barley. • Unique varietal chemical profiles confirm germination rate differences. • Future work should include imaging studies to monitor the barley malting process. ANOVA-simultaneous component analysis (ASCA) was applied to short-wave infrared spectral fingerprints of 5 malting barley varieties collected using a hyperspectral imaging system to determine the effect of germination, the influence of time and the influence of barley by means of a full factorial experimental design. ASCA indicated that there was a significant (p < 0.0001) effect of the germination status, the germination time and interaction on the spectral data for all varieties. The biochemical and physiological modification of the samples were characterised by visualisation of the longitudinal scores obtained from simultaneous component analysis for the germination time factor. This resulted in the visualisation and explanation of biochemical change over the course of barley germination as a factor of time. The relevant loadings indicated a significant change to the proteome, lipid and starch structure as driven by the uptake of water over time. The ASCA model were extrapolated to include the effect of barley variety to the already mentioned germination status and germination time factors, resulting once again in all the effects being significant (p < 0.0001). Here it was shown that all the barley varieties are significantly different from one another pre- and post-modification, based on the molecular vibrations observed in the short wave-infrared (SWIR) spectra, suggesting that the detection of biotic stress factors, such as pre-harvest germination, also differ for each variety, by indicating that the germination profile of each barley variety varies as a function of germination time. Thus, also the malting performance, germinative energy and chemical profile of each barley variety tested will vary before, during and after imbibition and germination – indicating the importance of malting commercial barley malt true to variety. These results indicate that (SWIR) spectral imaging instrumentation can possibly be used to monitor controlled germination of barley grain. Due to the shown ability of SWIR spectral imaging to detect small biochemical changes over time of barley grain during germination. [ABSTRACT FROM AUTHOR]

Background: The pathogenic processes in the preclinical phase of inflammatory bowel disease (IBD) are mainly unknown. Aims: To study typical antibodies for IBD in the preclinical phase in a cohort of Northern Sweden. Methods: Antibodies typical for IBD (ASCA, pANCA, lactoferrin-ANCA, antibodies to goblet cells, and pancreas antigen) were analyzed in 123 subjects with preclinical ulcerative colitis (UC), 54 subjects with preclinical Crohn's disease (CD) and in 390 sex- and age-matched controls. In addition, in a subset of subjects, inflammatory markers (CRP, albumin, calprotectin and ferritin) were measured in plasma. Results: The mean years between blood samples and IBD diagnosis were for UC 5.1 (SD 3.5) years and CD 5.6 (SD 3.5) years. There was no difference in the proportion of overall positive antibodies between subjects who later developed IBD compared to controls (16.9% vs. 12.3%; p = 0.137). The subjects who later developed CD had a significantly higher proportion of positive ASCA compared to controls (9.3% vs 2.8%; p = 0.034), but for all other antibodies, there were no differences compared to control subjects. Subjects with preclinical IBD and elevated antibodies showed significantly higher plasma calprotectin levels compared to subjects without antibodies (980 μg/L vs 756 μg/L; p = 0.042), but there was no difference in the levels of CRP, albumin and ferritin. Conclusions: We found no significant increase in antibodies typical for IBD years before diagnosis except for ASCA, which was slightly more common in subjects who later developed CD. Very few subjects had detectable antibodies to goblet cells and pancreas antigen. [ABSTRACT FROM AUTHOR]

Background: Cognitive decline is well-recognized as a normative part of the aging process that strongly affects daily functioning and quality of life. Giving the different levels of cognitive health among people from different socioeconomic groups. Aim: To assess the prevalence and pattern of cognitive impairment among community dwelling elderly in Egypt and the contribution of socioeconomic status to inequality in cognitive impairment. Patients and Methods: This study involved 470 elderly participants ≥ 60 years, males and females. The Montreal Cognitive Assessment (MoCA) was used to measure the prevalence of cognitive impairment, Ain Shams Cognitive Assessment (ASCA) to assess pattern of specific cognitive domain affection, and Socioeconomic status (SES) scale were used to describe the SES of participants. Results: The prevalence of cognitive impairment was 50.2% in Egypt, 37.7% for MCI and 12.5% for Dementia. The most common type of cognitive impairment was degenerative type (47.8%). Pattern of specific domain affection among cognitively impaired subjects, ranged from 94% for visuospatial function to 12.7% for Abstraction. Cognitive impairment was significantly associated with increasing age, female sex, marital status single or widow, low education and with increasing total number of comorbidities, depression and positive family history of cognitive impairment. Cognitive impairment was mainly concentrated in the economically disadvantaged population (low educational level, non-occupied, limited computer use, low income and bad sanitation). Conclusion: In Egypt, cognitive impairment is significantly prevalent and concentrated among those who are in low socioeconomic status. Increasing educational level of low SES population and improving their access to healthcare services will help to improve the inequity of cognitive impairment. [ABSTRACT FROM AUTHOR]

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.

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent‐variable‐based methods, such as principal component analysis and partial least‐squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state‐of‐the‐art multivariate methods for the analysis of NMR‐based metabolomic data as well as alternative methods, highlighting their strengths and limitations. [ABSTRACT FROM AUTHOR]

Accurate serological assays are desirable for the diagnosis of inflammatory bowel disease (IBD). We identify an antigen-like substance called Crohn's disease (CD) antibody binding polypeptide (CABP). As a serological marker, anti-CABP may contribute to the diagnosis of IBD. The present study aims to evaluate the clinical role of anti-CABP as a serological antibody for IBD. Using enzyme-linked immunosorbent assay (ELISA), serum anti-CABP, anti-Saccharomyces cerevisiae antibody (ASCA) and perinuclear anti-neutrophil cytoplasmic antibody (pANCA), titers were tested in 168 CD patients, 123 ulcerative colitis (UC) patients and 170 controls. The correlation between serum antibody and clinical characteristics was investigated. The diagnostic potential of the anti-CABP was evaluated by receiver operating characteristic (ROC) analysis. The titers of anti-CABP (IgA or IgG) and ASCA IgG of CD patients were significantly higher than non-CD group (all p <.01). In the differential diagnosis of CD and non-CD, anti-CABP IgA revealed an area under the curve (AUC) of 0.706 and anti-CABP IgG demonstrated an AUC of 0.788. As an individual antibody, anti-CABP could effectively distinguish CD from non-CD (AUC 0.816), and the diagnostic efficacy was better than that of ASCA (AUC 0.680). The combined use of anti-CABP, ASCA and pANCA significantly improved the diagnostic value (AUC 0.857). Anti-CABP positive rates were associated with perianal lesions and disease location in CD patients (both p <.05). Our results suggested that anti-CABP could be used as a serological marker to assist the diagnosis of CD. This trial is registered with clinical trial registration unique identifier ChiCTR2000037094. [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.

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]

Die vielen neuen Therapiemöglichkeiten für chronisch entzündliche Darmerkrankungen erfordern eine gute Diagnostik und Verlaufskontrolle. Für beide Aspekte bietet die Labordiagnostik eine Vielzahl von hilfreichen Parametern an. Auch für zukünftige Ansätze, die auf genetische Veränderungen und die Mikrobiomzusammensetzung abzielen, entwickelt sich ein interessantes Spektrum an individualisierter Diagnostik. Nicht alle Ansätze werden zum Erfolg führen, aber schon jetzt zeigen wissenschaftliche Studien, dass die gezielte Inhibition von mutierter Genaktivität und ein „schlechtes“ Mikrobiom in Zukunft individualisierte Therapieansätze ermöglichen könnten. [ABSTRACT FROM AUTHOR]

Background and aim: The diagnosis of Crohn's disease (CD) is challenging. Ongoing search for biomarkers to facilitate the diagnosis is a worthwhile endeavor. The aim of this study was to explore the role of serological markers in the diagnosis of CD at an inflammatory bowel disease (IBD) referral center. Methods: This was a retrospective study including 196 suspected CD patients. The expression of ASCA-IgG, ASCA-IgA, AYMA-IgG, AYCA-IgA, FI2Y-IgG, and pANCA in the patient's serum was determined by enzyme-linked immunosorbent assay (ELISA) and indirect immunofluorescence (IF). Results: ASCA was a relatively specific marker for CD (p = 0.0005), but not AYMA-IgG, AYCA-IgA, F12Y-IgG (p = 0.5936, 0.7974, 0.1085, respectively). However, a high sensitivity of 96.77% (95% CI 90.19%-99.83%) was noted for ASCA+/FI2Y+ to identify CD patients among the suspected cases, albeit with low PPV. The more combinations of serological markers, the higher sensitivity, and NPV. No correlation was found between the age of onset or disease location and the expression of ASCA, AYMA, AYCA, FI2Y, or pANCA. There was no significant difference between the expression of ASCA and the disease behavior at diagnosis (p = 0.3307). However, a decreased proportion of AYMA+ CD patients was found in those who received surgery compared with their non-surgical counterparts (p = 0.0488). Conclusions: ASCA was found to be the most accurate serological marker for the differential diagnosis of CD. Combinations of ASCA, AYMA, AYCA, and FI2Y improved diagnostic accuracy of CD. [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]

The study examined school counselor perceptions of the American School Counseling Association's Comprehensive School Counseling Program (CSCP) on academic outcomes (betterreported grades and better-standardized scores) of middle school students with a focus on recommendations for the tasks and responsibilities of school counselors. The study was qualitative and used a phenomenological approach through semi-structured face-to-face interviews to provide inquiry into school counselors' perceptions of an effective school counseling program. [ABSTRACT FROM AUTHOR]

The early non-destructive detection of chilling injury (CI) in aubergine fruit was investigated using spectroscopy. CI is a physiological disorder that occurs when the fruit is subjected to temperatures lower than 12 °C. Reference measurements of CI were acquired by visual appearance analysis, measuring electrolyte leakage (EL), mass loss and firmness evaluations which demonstrated that even before three days of storage at 2 °C, the CI process was initiated. An ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of temperature and storage time on the Fourier transform – near infra-red (FT-NIR) spectral fingerprints. The ASCA model demonstrated that temperature, duration of storage, and their interaction had a significant effect on the spectra. In addition, it was possible to highlight the main variations in the experimental results with reference to the effects of the main factors, and with respect to storage time, to discover any major monotonic trends with time. Partial least squares-discriminant analysis (PLS-DA) was used as a supervised classification method to discriminate between fruit based on chilling and safe temperatures. In this case, only significant spectral wavebands which were significantly influenced by the effect of temperature based on ASCA were utilised. PLS-DA prediction accuracy was 87.4 ± 2.7% as estimated by a repeated double-cross-validation procedure (50 runs) and the significance of the observed discrimination was verified by means of permutation tests. The outcomes of this study indicate a promising potential for near infra-red spectroscopy (NIRS) to provide non-invasive, rapid and reliable detection of CI in aubergine fruit. Image 1 • ASCA method was applied to aubergine fruit stored at safe and chilling temperature. • All main factors and their interaction had a significant effect on FT-NIR spectra. • The most affected spectra introduced by ASCA were assigned as PLS-DA input. • PLS-DA successfully classified aubergine based on their storage temperature. [ABSTRACT FROM AUTHOR]

School counseling as a profession is in its embryonic stage in Vietnam. National education reform initiatives have focused on curricula, standardized tests and textbooks, yet have essentially ignored problems of violence and students' mental health. The American School Counseling Association model can serve as a guide for a culturally appropriate school counseling model for Vietnam. We explore the Vietnam education context and the existing practice of school counseling and make recommendations for professional school counseling in Vietnam. [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]

Nowadays, life science experiments—and especially "omics" fields—often imply a high volume of information from high throughput technologies that is gathered in the form of a wide and short multivariate response. These data are intrinsically correlated and generally produced by another multivariate set of factors or continuous variables, collected in what is defined as the design matrix. Such design factors usually involve the presence of a treatment, but other sources of biological or technical variability in the data are often measured as well. The ASCA framework, based on ANOVA and PCA, leads to promising results. By combining dimension reduction projection methods and classic statistical modelling, it enables to decipher the main sources of variability in the produced response and offers attractive graphical representations of the factors' effect. However, this approach has not yet been extended to more advanced designs involving random factors, being typically involved in longitudinal, hierarchical, or repeatability/reproducibility studies. This paper has its roots in the GLM version of ASCA, called ASCA+, that leads to unbiased estimators of the factors' effects for unbalanced data. It is here extended by replacing GLM by LMM and adapting the methodology. Taking into account the error structure of the data indeed leads to more accurate data modelling and more generalisable results. The suggested methodology is applied to two experimental case studies that highlight the benefits of this approach as it leads to a refined data analysis with interesting inferential properties, while keeping the powerful visualisation outputs produced by ASCA. Omics usually generate wide and short multivariate responses from a multifactorial design. ASCA+ provides graphical representations and quantifies the factors' effect for such (un)balanced designs. It is here extended to include random factors, typically involved in longitudinal, hierarchical, or repeatability/reproducibility studies. The suggested methodology is applied to two experimental case studies that highlight the benefits of this approach as it leads to a refined data analysis with interesting inferential properties, while keeping the powerful visualisation outputs produced by ASCA. [ABSTRACT FROM AUTHOR]