New immune phenotypes for treatment response in high-grade serous ovarian carcinoma patients.

Despite advances in surgical and therapeutic approaches, high-grade serous ovarian carcinoma (HGSOC) prognosis remains poor. Surgery is an indispensable component of therapeutic protocols, as removal of all visible tumor lesions (cytoreduction) profoundly improves the overall survival. Enhanced predictive tools for assessing cytoreduction are essential to optimize therapeutic precision. Patients' immune status broadly reflects the tumor cell biological behavior and the patient responses to disease and treatment. Serum cytokine profiling is a sensitive measure of immune adaption and deviation, yet its integration into treatment paradigms is underexplored. This study is part of the IMPACT trial (NCT03378297) and aimed to characterize immune responses before and during primary treatment for HGSOC to identify biomarkers for treatment selection and prognosis. Longitudinal serum samples from 22 patients were collected from diagnosis until response evaluation. Patients underwent primary cytoreductive surgery or neoadjuvant chemotherapy (NACT) based on laparoscopy scoring. Twenty-seven serum cytokines analyzed by Bio-Plex 200, revealed two immune phenotypes at diagnosis: Immune High with marked higher serum cytokine levels than Immune Low. The immune phenotypes reflected the laparoscopy scoring and allocation to surgical treatment. The five Immune High patients undergoing primary cytoreductive surgery exhibited immune mobilization and extended progression-free survival, compared to the Immune Low patients undergoing the same treatment. Both laparoscopy and cytoreductive surgery induced substantial and transient changes in serum cytokines, with upregulation of the inflammatory cytokine IL-6 and downregulation of the multifunctional cytokines IP-10, Eotaxin, IL-4, and IL-7. Over the study period, cytokine levels uniformly decreased in all patients, leading to the elimination of the initial immune phenotypes regardless of treatment choice. This study reveals distinct pre-treatment immune phenotypes in HGSOC patients that might be informative for treatment stratification and prognosis. This potential novel biomarker holds promise as a foundation for improved assessment of treatment responses in patients with HGSOC. ClinicalTrials.gov Identifier: NCT03378297.

Non-invasive exploration of malting barley (Hordeum vulgare) in vitro germination and varietal effects using short wave infrared spectral imaging and ANOVA simultaneous component analysis.

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.

Influence of regional and yearly weather patterns on multi-mycotoxin occurrence in Austrian wheat: a liquid chromatographic-tandem mass spectrometric and multivariate statistics approach.

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.

Coincidence of antibodies against Hwp1 and ASCA, two distinct molecular targets of Candida albicans, reinforces the link between this fungal species and coeliac disease.

Candida 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.

From ASCA breakthrough in Crohn's disease and Candida albicans research to thirty years of investigations about their meaning in human health.

Anti-Saccharomyces cerevisiae antibodies (ASCA) are human antibodies that can be detected using an enzyme-linked immunosorbent assay involving a mannose polymer (mannan) extracted from the cell wall of the yeast S. cerevisiae. The ASCA test was developed in 1993 with the aim of differentiating the serological response in two forms of inflammatory bowel disease (IBD), Crohn's disease and ulcerative colitis. The test, which is based on the detection of anti-oligomannosidic antibodies, has been extensively performed worldwide and there have been hundreds of publications on ASCA. The earlier studies concerned the initial diagnostic indications of ASCA and investigations then extended to many human diseases, generally in association with studies on intestinal microorganisms and the interaction of the micro-mycobiome with the immune system. The more information accumulates, the more the mystery of the meaning of ASCA deepens. Many fundamental questions remain unanswered. These questions concern the heterogeneity of ASCA, the mechanisms of their generation and persistence, the existence of self-antigens, and the relationship between ASCA and inflammation and autoimmunity. This review aims to discuss the gray areas concerning the origin of ASCA from an analysis of the literature. Structured around glycobiology and the mannosylated antigens of S. cerevisiae and Candida albicans, this review will address these questions and will try to clarify some lines of thought. The importance of the questions relating to the pathophysiological significance of ASCA goes far beyond IBD, even though these diseases remain the preferred models for their understanding.

A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data.

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.

Single-cell multiomics of the human retina reveals hierarchical transcription factor collaboration in mediating cell type-specific effects of genetic variants on gene regulation.

BACKGROUND: Systematic characterization of how  genetic variation modulates gene regulation in a cell type-specific context is essential for understanding complex traits. To address this question, we profile gene expression and chromatin accessibility in cells from healthy retinae of 20 human donors through single-cell multiomics and genomic sequencing. RESULTS: We map eQTL, caQTL, allelic-specific expression, and allelic-specific chromatin accessibility in major retinal cell types. By integrating these results, we identify and characterize regulatory elements and genetic variants effective on gene regulation in individual cell types. The majority of identified sc-eQTLs and sc-caQTLs display cell type-specific effects, while the cis-elements containing genetic variants with cell type-specific effects are often accessible in multiple cell types. Furthermore, the transcription factors whose binding sites are perturbed by genetic variants tend to have higher expression levels in the cell types where the variants exert their effects, compared to the cell types where the variants have no impact. We further validate our findings with high-throughput reporter assays. Lastly, we identify the enriched cell types, candidate causal variants and genes, and cell type-specific regulatory mechanism underlying GWAS loci. CONCLUSIONS: Overall, genetic effects on gene regulation are highly context dependent. Our results suggest that cell type-dependent genetic effect is driven by precise modulation of both trans-factor expression and chromatin accessibility of cis-elements. Our findings indicate hierarchical collaboration among transcription factors plays a crucial role in mediating cell type-specific effects of genetic variants on gene regulation.

Kestose Increases the Relative Abundance of Faecalibacterium spp. and Nominally Increases Cow Milk Tolerant Dose in Children with Cow's Milk Allergy - Preliminary Results.

A single-arm study was conducted with 10 children aged 2-12 years with severe cow's milk allergy (CMA) requiring complete allergen elimination. Subjects were administered kestose, a prebiotic, at 1 or 2 g/day for 12 weeks. Results of a subsequent oral food challenge (OFC) showed a statistically significant increase in the total dose of cow's milk ingestion (1.6 ml vs. 2.7 ml, p = 0.041). However, the overall evaluation of the OFC results, TS/Pro (total score of Anaphylaxis Scoring Aichi (ASCA)/cumulative dose of protein), showed no statistically significant improvement, although the values were nominally improved in seven out of 10 subjects. The 16S rDNA analysis of fecal samples collected from the subjects revealed a statistically significant increase in the proportion of Faecalibacterium spp. (3.8 % vs. 6.8%, p = 0.013), a type of intestinal bacterium that has been reported to be associated with food allergy. However, no statistically significant correlation was found between Faecalibacterium spp. abundance and the results of the OFC.

Biomarkers in IBD: What to Utilize for the Diagnosis?

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.

Study of the Stability of Wine Samples for 1H-NMR Metabolomic Profile Analysis through Chemometrics Methods.

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.

Machine Learning in Antibody Diagnostics for Inflammatory Bowel Disease Subtype Classification.

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.

Effect of wheat roasting conditions and wheat type on short-wave infrared (SWIR) spectral data of whole and milled wheat by ANOVA-simultaneous component analysis.

ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of roasting and wheat type on shortwave-infrared (SWIR) spectra of whole wheat and flour through assessment of statistical significance and characterisation of the contributing spectral features. The full factorial experimental design included two wheat types, three roasting temperatures and three roasting frequencies. SWIR spectral images were collected from the two roasted wheat types and their two milled samples. Three ASCA models, one for each wheat conformation (kernel, whole wheat flour, white flour) were investigated. It was evidenced that all factors and interaction in the whole wheat flour model had a significant (p ≤ 0.05) effect on spectral data. Only the factor roasting frequency was not significant in white flour model and only the interaction between roasting frequency and wheat type was not significant for the kernel model. The main variations in the loading line plots were identified and characterised by chemical structural differences that occur within the sample. The effect of roasting frequency had a more adverse effect on protein stability, moisture evaporation, water soluble carbohydrates and aromatic amino acids, compared to roasting temperature. A Rapid Visco-Analyser (RVA) was used to further investigate difference in wheat type as almost all spectral data sets differed significantly. The most prominent difference between the two wheat types was observed as differences in amylase activity and presence of lipids. ASCA applied to SWIR whole wheat and flour spectral data effectively characterised the significant effect of roasting on wheat starch and protein structures.

Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling.

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.

Batch-wise versus continuous dough mixing of Danish butter cookies. A near infrared hyperspectral imaging study.

The Danish buttered cookie is a famous confectionery product. Its success makes manufacturing of the large volumes required challenging, introducing the need for different strategies to increase production while maintaining a high-quality standard. Two manufacturing lines used are batch-wise and continuous dough mixing. Despite the recipe being the same, the outcome of the two production types differs in texture and external appearance. While this does not infringe on the quality, changes in texture are observable. This manuscript analyses the physicochemical differences of the cookies after baking using Near Infrared hyperspectral imaging and Chemometrics. The study demonstrates that the changes in texture between batch and continuous production are mostly due to the difference in crystalline sucrose emerging in invisible spots on or near the surface of the cookies and a higher tendency of migrated butter-fat spots on the surface of the cookies for the continuous manufacturing procedure.

Emerging Biomarkers in Alcohol-associated Hepatitis.

Alcohol-associated hepatitis (AH) is a clinical syndrome of jaundice, abdominal pain, and anorexia due to prolonged heavy alcohol intake. AH is associated with changes in gene expression, cytokines, immune response, and the gut microbiome. There are limited biomarkers to diagnose and prognosticate in AH, but several non-invasive biomarkers are emerging. In this review, clinical risk-stratifying algorithms, promising AH biomarkers like cytokeratin-18 fragments, genetic polymorphisms, and microRNAs will be reviewed.

Anti-Saccharomyces cerevisiae Antibodies Are Only Modestly More Common in Subjects Later Developing Crohn's Disease.

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.

ALASCA: An R package for longitudinal and cross-sectional analysis of multivariate data by ASCA-based methods.

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).

Role of Immunohistochemistry and serology in subclassifying the Inflammatory Bowel Disease cases diagnosed as Inflammatory Bowel Diseases---unclassified on colonic biopsies.

Background: Inflammatory bowel diseases (IBDs) mainly consist of Crohn's Disease (CD) and Ulcerative Colitis (UC). These two categories have overlapping histopathological features and sometimes it is difficult to diagnose them into distinct category and such biopsies are categorised as Inflammatory Bowel Disease (IBD-U). Recently, there has been an increase in interest to discover new biomarkers of IBD to differentiate UC and CD and predict their prognosis. Method: In the present study, 273 non-neoplastic colonic biopsies with clinicoendoscopic features of IBD were studied and categorized into UC (88; 32.3%) and CD (03; 1.1%) but a major chunk remained in category of IBD-U (182; 66.6%). 161 (58.9%) of these biopsies were then subjected to IHC for RB protein and ß-catenin and Serology for pANCA and ASCA was done in only 85 (31.13%) of these selected cases for identification of UC and CD on colonic biopsies. Result: 161 biopsies that were subjected to IHC analysis included 57 cases of UC, 03 cases of CD, and rest 101 cases of IBD-U. Out of 101 cases of IBD-U, 87 (86.13%) cases were reclassified as UC (61; 60.3%) and CD (14; 13.86%) on the basis of results of IHC and Serology. Conclusion: The two major tools IHC for ß-catenin and RB protein and the assay of serum ASCA and p-ANCA along with proper history and clinical presentation can act as a good adjunct to conventional H and E in subclassifying cases of IBD-U into UC and CD.

Advanced Analytical Approach Based on Combination of FT-IR and Chemometrics for Quality Control of Pharmaceutical Preparations.

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.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies.

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.