Partial Least Squares Discriminant Analysis Analysis Research Articles

Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect

ObjectiveDevelop a new method for diagnosing leprosy and monitoring the pharmacological treatment effect of patients. Material and methodsPlasma samples from patients diagnosed with leprosy (n = 211) who had not yet received any pharmacological treatment were collected at a basic health unit in Brazil. After treatment, samples were collected from the same patients (n = 125). Plasma samples from healthy volunteers were also collected (n = 179) and used as a control group. All samples were analyzed by Fourier transform mid-infrared spectrophotometry (MIR-FTIR). The spectral data of the samples were subjected to chemometric analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to predict diagnosis and monitor pharmacological treatment. ResultsThe PCA model successfully distinguished among three sample classes: healthy individuals, pre-treatment leprosy patients, and post-treatment leprosy patients. The PLS-DA algorithm accurately classified healthy, treated, and diseased samples, facilitating both reliable diagnosis and treatment monitoring for leprosy. The model achieved a sensitivity of 97% to 100%, specificity of 100%, and accuracy ranging from 99% to 100%. Furthermore, when tested on plasma samples from patients with other conditions—renal failure (n=1032), hypertriglyceridemia (n=100), hypercholesterolemia (n=100), and mixed dyslipidemia (n=100)—the model correctly classified these as negative for leprosy, with diagnostic specificity between 93% and 96%. ConclusionThe MIR-FTIR technique combined with PLS-DA analysis proved to be a highly effective tool for screening leprosy patients and monitoring treatment outcomes. Given its low cost, this method could be easily implemented in laboratories across emerging and low-income countries.

Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics.

Background:Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MSE) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MSE, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC's proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MSE combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

In-Depth Immune Profiling in Children with BCP-ALL within the AIEOP-BFM ALL 2017 Study

The international multicenter frontline therapy trial AIEOP-BFM ALL 2017 prospectively randomizes CD19-targeting T-cell engager blinatumomab (BLN) against standard of care (SOC) to improve outcomes in pediatric patients (pts) with B-cell precursor acute lymphoblastic leukemia (BCP-ALL) stratified for high (HR) and medium (MR) risks of relapse. In HR arm BLN is given earlier, directly after intensified HR therapy block, while in the MR arm, it is given towards the end of chemotherapy, before maintenance. The immune composition prior to BLN might thus differ between both treatment groups (HR vs MR) which may impact BLN-mediated anti-leukemic activity. Within the Germany-wide immune monitoring program accompanying the AIEOP-BFM ALL 2017 trial we addressed this question and studied a total of 1056 longitudinal peripheral blood samples collected between 11/2018 and 05/2022 from 381 BCP-ALL pts. Samples were prospectively analyzed with multicolor flow cytometry to study immune cell composition (Panel 1) at the time of BLN application (d0) and under ongoing immunotherapy within first 24h (d1), at day 14 (d14), at the end of the cycle I (d28) and end of cycle 2 (day28_II), if applicable (Figure 1A). In-depth T-cell profiling was performed in the BLN arm, utilizing 4 additional 12-color panels concentrated on markers associated with T-cell functionality (measured with standardized cytometer settings). Data were analyzed using classical 2D-manual gating strategies (&amp;gt; 2000 variables) and completed with R-based machine learning algorithms; namely partial Least-Squares Discriminant Analysis (PLS-DA), a multivariate dimensionality-reduction tool for identifying HR- and MR-defining immune signatures and PhenoGraph algorithm for unsupervised T-cell cluster detection. T-cell receptor excision circles (TRECs) and TR-repertoire analysis were also performed among paired samples of 117 BLN-treated pts. Baseline immune composition differed notably between HR and MR pts directly prior to BLN therapy, as shown by PLS-DA analysis on manually gated immune subsets. HR pts showed higher neutrophil counts and lower lymphocyte and monocyte counts compared to MR pts. Interestingly, MR pts had higher levels of B- and T-cells, which are crucial for BLN-mediated T-cell activation. HR pts displayed an elevated CD4+/CD8+ ratio, shifted naïve/memory T-cell phenotypes and increased proportions of immunosuppressive T-regulatory cells. These results suggest that more intensive therapy protocols directly prior to BLN influence the immune composition towards compromised states in the HR arm, potentially affecting BLN-mediated T-cell leukemia control. BLN effectively depleted CD19+ cells in 221 out of 224 pts. We observed significantly stronger T-cell activation/expansion in MR pts compared to HR pts under BLN therapy. This expansion that was primarily attributed to naïve CD8+ T-cell populations, resulting from clonal T-cell proliferation independent of thymic contribution, as supported by TREC and TR-repertoire analysis. The PhenoGraph clustering also identified this expanding naïve CD8+ T-cell populations, surprisingly marked by loss of adhesion molecule DNAM1 (panel 1), normally involved in NK and T-cell mediated cytotoxicity and by gaining the expression of TIM-3 (panel 3), an inhibitory molecule usually co-expressed with PD1 on exhausted T-cells with memory phenotypes. In this setting however, TIM3 was not co-expressed with PD1 (Figure 1B) and its expression was correlated with the B-cell levels at baseline and was strongest in MR pts under BLN therapy. Our data suggest an until now undescribed association between DNAM1 and TIM3 with BLN-mediated T-cell activation, hinting that they may serve as a measure of T-cell activation strength or an early T-cell exhaustion marker. In conclusion our study provides clinically relevant insights in how chemotherapy prior BLN might affect the patients´ immune system, possibly influencing the BLN anti-leukemic activity, and helps to determine the optimal place of immunotherapy with BLN in multimodal treatment schedules. Here, we report until know undescribed effect of BLN on T-cell compartment. Upcoming clinical data, which will be correlated with obtained immune profiles will answer the open question regarding the clinical relevance of these findings, and shed light into the mechanisms determining response to BLN, for its maximum benefit in childhood ALL.

The serum proteome of VA-ECMO patients changes over time and allows differentiation of survivors and non-survivors: an observational study

BackgroundVeno-arterial extracorporeal membrane oxygenation (VA-ECMO) is applied in patients with refractory hemodynamic failure. Exposure of blood components to high shear stress and the large extracorporeal surfaces in the ECMO circuit trigger a complex inflammatory response syndrome and coagulopathy which are believed to worsen the already poor prognosis of these patients. Mass spectrometry-based proteomics allow a detailed characterization of the serum proteome as it provides the identity and concentration of large numbers of individual proteins at the same time. In this study, we aimed to characterize the serum proteome of patients receiving VA-ECMO.MethodsSerum samples were collected on day 1 and day 3 after initiation of VA-ECMO. Samples underwent immunoaffinity based depletion for the 14 most abundant serum proteins, in-solution digestion and PreOmics clean-up. A spectral library was built with multiple measurements of a master-mix sample using variable mass windows. Individual samples were measured in data independent acquisition (DIA) mode. Raw files were analyzed by DIA-neural network. Unique proteins were log transformed and quantile normalized. Differential expression analysis was conducted with the LIMMA—R package. ROAST was applied to generate gene ontology enrichment analyses.ResultsFourteen VA-ECMO patients and six healthy controls were recruited. Seven patients survived. Three hundred and fifty-one unique proteins were identified. One hundred and thirty-seven proteins were differentially expressed between VA-ECMO patients and controls. One hundred and forty-five proteins were differentially expressed on day 3 compared to day 1. Many of the differentially expressed proteins were involved in coagulation and the inflammatory response. The serum proteomes of survivors and non-survivors on day 3 differed from each other according to partial least-squares discriminant analysis (PLS-DA) and 48 proteins were differentially expressed. Many of these proteins have also been ascribed to processes in coagulation and inflammation (e.g., Factor IX, Protein-C, Kallikrein, SERPINA10, SEMA4B, Complement C3, Complement Factor D and MASP-1).ConclusionThe serum proteome of VA-ECMO patients displays major changes compared to controls and changes from day 1 until day 3. Many changes in the serum proteome are related to inflammation and coagulation. Survivors and non-survivors can be differentiated according to their serum proteomes using PLS-DA analysis on day 3. Our results build the basis for future studies using mass-spectrometry based serum proteomics as a tool to identify novel prognostic biomarkers.Trial registration: DRKS00011106.

NMR-Based Metabolomics Demonstrates a Metabolic Change during Early Developmental Stages from Healthy Infants to Young Children.

The present study aims to identify the salivary metabolic profile of healthy infants and young children, and to correlate this with age, salivary gland maturation, and dentition. Forty-eight children were selected after clinical evaluation in which all intraoral structures were examined. Total unstimulated saliva was collected, and salivary metabolites were analyzed by 1H Nuclear Magnetic Resonance (NMR) at 25 °C. Partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (O-PLS-DA), and univariate analysis were used, adopting a 95% confidence interval. The study showed a distinct salivary metabolomic profile related to age and developmental phase. The saliva of children in the pre-eruption teeth period showed a different metabolite profile than that of children after the eruption. However, more evident changes were observed in the saliva profile of children older than 30 months. Alanine, choline, ethanol, lactate, and sugar region were found in higher levels in the saliva of patients before 30 months old. Acetate, N-acetyl sugar, butyrate, caproate, creatinine, leucine, phenylalanine, propionate, valine, succinate, and valerate were found to be more abundant in the saliva of children after 30 months old. The saliva profile is a result of changes in age and dental eruption, and these findings can be useful for monitoring the physiological changes that occur in infancy.

UPLC-Q-TOF/MS-based metabolomic analysis reveals the effects of asomate on the citrus fruit

The regulation of the sugar-acid ratio is of great significance to the improvement of citrus fruit quality. The citric acid level in fruit is influenced by many factors. Among them, cultivar selection and production practices are the most important strategies under the grower's control. In recent years, an arsenic-containing preparation called “Tianmisu”, with the main ingredient of asomate, has occasionally been reported to be used in citrus cultivation to improve the sweetness of fruits. In order to reveal the effects of the pesticide on citrus fruits, ‘Harumi’ tangor was treated with “Tianmisu”, and the impact of this pesticide on fruit quality and metabolites was investigated through UPLC-Q-TOF/MS-based metabolomic analysis. Compared with the control, the concentration of titratable acidity, in particular citric acid, in the pulp of ‘Harumi’ tangor treated with the pesticide, was significantly reduced by 60.5%. The differences in metabolites between the pesticide-treated samples and the control were illustrated by Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA analysis demonstrated a clear discrimination, with R2Y and Q2 values of 0.982 and 0.933 in the positive mode and 0.984 and 0.900 in the negative mode, respectively. A total of 155 compounds were identified, and 63 characteristic components were screened out from the pesticide-treated samples compared to the control. Aside from the upregulation observed for a few metabolites, the majority of the compounds, including citric acid and various lipids, were down-regulated in the treated citrus fruits compared to the control. This study can serve as a basis for understanding the regulatory mechanism of organic acids in citrus and will be helpful in developing different strategies to improve citrus quality.

Metabolomics analysis of buck semen cryopreserved with trehalose.

Trehalose is commonly used as an impermeable cryoprotectant for cryopreservation of cells, but its cryoprotective mechanism has now not but been determined. This study investigated the cryopreservation impact of trehalose on buck semen cryopreservation and finished metabolic profiling of freeze-thawed media by way of the GC–MS-based metabolomics for the first time. Metabolic pattern recognition and metabolite identification by means of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and metabolic pathway topology analysis revealed the results of trehalose on buck sperm metabolism at some point of cryopreservation. The results confirmed that trehalose drastically progressed sperm motility parameters and structural integrity after thawing. PCA and PLS-DA analysis discovered that the metabolic patterns of the freezing-thawing media of buck semen cryopreserved with trehalose (T group) or without trehalose (G group, Control) were certainly separated. Using screening conditions of VIP >1.5 and p vaule <0.05, a total of 48 differential metabolites have been recognized, whithin l-isoleucine, L-leucine, L-threonine, and dihydroxyacetone were notably enriched in valine, leucine and isoleucine biosynthesis, glycerolipid metabolism, and aminoacyl-tRNA biosynthesis pathways. In brief, trehalose can efficiently improve membrane structural integrity and motion parameters in buck sperm after thawing, and it exerts a cryoprotective impact with the aid of changing sperm amino acid synthesis and the glycerol metabolism pathway.

MO652: Comparison of Two Medium Cut-Off Dialysers Versus Online Haemodiafiltration Regarding Albumin Loss and the Removal of a Broad Spectrum of Molecules

Abstract BACKGROUND AND AIMS Ideally, the goal of haemodialysis (HD) is to maximize the removal of molecules smaller than 50 000 Daltons and minimize albumin loss. The techniques that seem to best achieve this purpose are online haemodiafiltration (HDF) and, recently, HD with medium cut-off membranes. On the other hand, we now have new methods, such as metabolomics, which allow us to detect potential new uraemic toxins in a non-targeted way. The aim of this study is to compare, in terms of efficacy and safety, online HDF using Fx800Cordiax, with 2 medium cut-off dialysers: Theranova 500 and the new Elisio21 HX dialyser. METHOD A total of 14 patients following a baseline treatment with online HDF using Fx800Cordiax were included. Each patient was considered his/her own control and was randomized to receive a consecutive 1-week haemodialysis treatment with Theranova 500 and Elisio 21HX. Pre- and post-HD blood samples were obtained on the middle day of the week with the three dialysers and proportional volumes of dialysate effluent were collected to quantify albumin loss during the session. We compared the reduction rate of differently sized molecules, as well as the variation rate in molecules smaller than 1000 Daltons, detected and quantified by NMR based chemometrics. For the analysis of differences, we used PLS-DA (Partial Least Squares-Discriminant Analysis) and ANOVA with multiple comparisons (Bonferroni). RESULTS The figure shows the result of the PLS-DA analysis carried out via the MetaboAnalyst web platform, showing, in terms of variable importance in projection (VIP) scores, the importance of different metabolites for the discrimination between the pre-HD and post-HD state regarding each of the three dialysis procedures. The metabolites that generate the 30 most important first signals or spectral bins are shown. The right-hand columns show the relative concentrations of the different metabolites on a colour-coded qualitative scale from red to blue. The predominance of dark blue in post-haemodialysis using online HDF (postCntr) indicates a greater elimination of metabolites with this technique, followed in efficacy by ElisioHX(postE), with a predominance of light blue and Theranova 500 (postT) in third place, with a predominance of orange. In any case, these are semi-quantitative differences. The table shows the reduction rate of different molecules and albumin losses. CONCLUSION In this study, haemodialysis with Theranova 500 has proven to be very similar in efficacy to online HDF, although with a significantly higher albumin loss. Haemodialysis with Elisio21HX resulted in lower removal of molecules around 20 000 Da compared to online HDF, but there was no significant difference compared to haemodialysis with Theranova 500. Albumin loss was significantly higher with Theranova 500 than with Elisio21HX. The semi-quantitative metabolomics study suggests that, in terms of clearance of low molecular weight metabolites (&amp;lt;1 kDa), on-line HDF is more effective, followed by haemodialysis with Elisio HX and finally with Theranova 500.

Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome With Partial Least Squares Discriminant Analysis: Relevance of Blood Extracellular Vesicles.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a chronic disease characterized by long-lasting persistent debilitating widespread fatigue and post-exertional malaise, remains diagnosed by clinical criteria. Our group and others have identified differentially expressed miRNA profiles in the blood of patients. However, their diagnostic power individually or in combinations seems limited. A Partial Least Squares-Discriminant Analysis (PLS-DA) model initially based on 817 variables: two demographic, 34 blood analytic, 136 PBMC miRNAs, 639 Extracellular Vesicle (EV) miRNAs, and six EV features, selected an optimal number of five components, and a subset of 32 regressors showing statistically significant discriminant power. The presence of four EV-features (size and z-values of EVs prepared with or without proteinase K treatment) among the 32 regressors, suggested that blood vesicles carry relevant disease information. To further explore the features of ME/CFS EVs, we subjected them to Raman micro-spectroscopic analysis, identifying carotenoid peaks as ME/CFS fingerprints, possibly due to erythrocyte deficiencies. Although PLS-DA analysis showed limited capacity of Raman fingerprints for diagnosis (AUC = 0.7067), Raman data served to refine the number of PBMC miRNAs from our previous model still ensuring a perfect classification of subjects (AUC=1). Further investigations to evaluate model performance in extended cohorts of patients, to identify the precise ME/CFS EV components detected by Raman and to reveal their functional significance in the disease are warranted.

ATR-FTIR-based rapid solution for the discrimination of lentils from different origins, with a special focus on PGI and Slow Food typical varieties

Three hundred forty-six (346) samples of lentils have been collected and analyzed by ATR-MIR spectroscopy. Of the investigated individuals, 283 were harvested in two Central-Italy regions (Umbria and Abruzzo), whereas the others were grown in Canada. At first, Partial Least Squares Discriminant Analysis (PLS-DA) was used to discriminate samples among the three origins. The outcome of PLS-DA analysis was noteworthy: only one individual (over 86 of the external test set) was erroneously assigned by the model, indicating the suitability of the proposed approach. Furthermore, Variable Importance in Projection (VIP) was exploited to inquire which spectral variables significantly contribute to the discrimination. Eventually, the focus has been circumscribed to two categories of high-valued lentils, e.g., lentils from Castelluccio di Norcia (CDN), a sub-set of the Umbria class, and from Santo Stefano di Sessanio (SSS), a sub-set of the Abruzzo class. These are of particular interest because CDN presents the Protected Geographical Indication (PGI) while SSS belongs to the Slow Food Presidium. The models for these classes provided interesting results.

Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall's chemical composition; thus, the compound's distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.

Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology

Fat content is one of the most important parameters of beef grading. In this study, a hyperspectral imaging (HSI) system, combined with multivariate data analysis, was adopted for the classification of beef grades. Three types of beef samples, namely Akaushi (AK), USDA prime, and USDA choice, were used for HSI image acquisition in the spectral range of 400–1000 nm. Spectral information was extracted from the image by applying the partial least squares discriminant analysis (PLS-DA) for the three classifications. A total of eight different types of data pre-processing procedures were tested during PLS-DA to evaluate their individual performance, with the accepted pre-processing method selected based on the highest accuracy. Chemical and binary images were generated to visualize the fat mapping of the samples. Quantitative analysis of the samples was performed for the reference measurement of the dry matter and fat content. The highest overall accuracy, 86.5%, was found using the Savitzky–Golay second derivatives pre-processing method for PLS-DA analysis. The optimal wavelength values were found from the beta coefficient curve. The chemical and binary images showed significant differences in fat mapping among the three groups of samples, with AK having the greatest intramuscular fat content and USDA choice having the least. Similar results were observed during the proximate analysis. The findings of this study demonstrate that the HSI technique is a potential tool for the fast and non-destructive determination of beef grades based on fat mapping.

Cockroach-induced IL9, IL13, and IL31 expression and the development of allergic asthma in urban children

Cockroach-induced IL9, IL13, and IL31 expression and the development of allergic asthma in urban children

Multiple-fingerprint analysis of Poria cocos polysaccharide by HPLC combined with chemometrics methods

In this study, the multiple fingerprints, which were integrated with HPGFC-ELSD (high performance gel filtration chromatography - evaporative light scattering detector) fingerprint, PMP-HPLC-DAD (1-phenyl-3-methyl-5-pyrazolone-high performance liquid chromatography - diode array detector) fingerprint of complete acid hydrolysates and HILIC-HPLC-ELSD (hydrophilic interaction - high performance liquid chromatography - evaporative light scattering detector) fingerprint of enzyme hydrolysates, were established to evaluate the quality of polysaccharides from Poria cocos (PCPs). The similarity evaluation showed that 16 batches of PCPs from different origins had high similarity in structural characteristics based on the multiple fingerprints. The chromatographic data of multiple fingerprints of PCPs were fused, processed and analyzed by chemometric methods including HCA (hierarchical cluster analysis), PCA (principal component analysis) and PLS-DA (partial least squares discriminant analysis). The 16 batches of PCPs were divided into 3 categories in PCA, indicating a certain relationship between the structural characteristics and the origins. PLS-DA analysis indicated that Man, Glc, Gal, Fuc, the components with m/z of 2.22 × 104∼1.53 × 104 Da and 3.46 × 103∼2.69 × 103 Da, oligosaccharides with DPs of 6 and 7, respectively, could be regarded as potential chemical markers for the classification of PCPs from different origins. According to the multiple fingerprints and chemometric analysis, the two commercial samples were proved to be adulterants.

Authentication of Rice (Oryza sativa L.) Using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies

Rice is a staple food in Vietnam, and the concern about rice is much greater than that for other foods. Preventing fraud against this product has become increasingly important in order to protect producers and consumers from possible economic losses. The possible adulteration of this product is done by mixing, or even replacing, high-quality rice with cheaper rice. This highlights the need for analytical methodologies suitable for its authentication. Given this scenario, the present work aims at testing a rapid and non-destructive approach to detect adulterated rice samples. To fulfill this purpose, 200 rice samples (72 authentic and 128 adulterated samples) were analyzed by near infrared (NIR) spectroscopy coupled, with partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA). The two approaches provided different results; while PLS-DA analysis was a suitable approach for the purpose of the work, SIMCA was unable to solve the investigated problem. The PLS-DA approach provided satisfactory results in discriminating authentic and adulterated samples (both 5% and 10% counterfeits). Focusing on authentic and 10%-adulterated samples, the accuracy of the approach was even better (with a total classification rate of 82.6% and 82.4%, for authentic and adulterated samples, respectively).

Surface-enhanced Raman scattering for the diagnosis of ulcerative colitis: will it change the rules of the game?

Ulcerative colitis (UC) is a relapsing-remitting inflammatory bowel disease that requires numerous costly invasive investigations which lead to physical and psychological patient discomfort. We need a non-invasive technological approach that would significantly improve its diagnosis. Surface-enhanced Raman scattering (SERS) is a growing technique that can provide a molecular diagnostic fingerprint in just a few minutes, without the need for prior sample preparation. The aim of this pilot in vivo study was to prove that multivariate analysis of SER spectra collected on plasma samples could be employed for non-invasive diagnosis of UC. Plasma samples were collected from healthy subjects (n = 35) and patients with UC (n = 28). SERS spectra were acquired using a 785-nm excitation laser line and a solid plasmonic substrate developed in our laboratory using an original procedure described in the literature. The classification accuracy yielded by SERS was assessed by principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA). PCA-LDA differentiated UC samples from those of healthy subjects with a sensitivity of 86%, a specificity of 92%, and an accuracy of 89%, the AUC being 0.96. The PLS-DA analysis resulted in a sensitivity of 89%, a specificity of 94%, an accuracy of 92%, and an AUC value of 0.92. Several spectral bands were associated with UC: 376-420, 440-513, 686-715, 919-939, 1035-1062, 1083-1093, 1120-1132, 1148-1156, 1191-1211, 1234-1262, 1275-1294, 1382-1405, 1511-1526, and 1693-1702cm-1. Changes in plasma levels of amino acids, proteins, lipids, and other compounds were noted using SERS in patients with UC. Multivariate analysis of SER spectra collected on a solid plasmonic substrate represents a promising alternative to diagnosing UC, as it is non-invasive, easy to use, and fast.

The main effects of elevated CO2 and soil-water deficiency on 1H NMR-based metabolic fingerprints of Coffea arabica beans by factorial and mixture design

The metabolic response of Coffea arabica trees in the face of the rising atmospheric concentration of carbon dioxide (CO2) combined with the reduction in soil-water availability is complex due to the various (bio)chemical feedbacks. Modern analytical tools and the experimental advance of agronomic science tend to advance in the understanding of the metabolic complexity of plants. In this work, Coffea arabica trees were grown in a Free-Air Carbon Dioxide Enrichment dispositive under factorial design (22) conditions considering two CO2 levels and two soil-water availabilities. The 1H NMR mixture design-fingerprinting effects of CO2 and soil-water levels on beans were strategically investigated using the principal component analysis (PCA), analysis of variance (ANOVA) - simultaneous component analysis (ASCA) and partial least squares-discriminant analysis (PLS-DA). From the ASCA, the CO2 factor had a significant effect on changing the 1H NMR profile of fingerprints. The soil-water factor and interaction (CO2 × soil-water) were not significant. 1H NMR fingerprints with PCA, ASCA and PLS-DA analysis determined spectral profiles for fatty acids, caffeine, trigonelline and glucose increases in beans from current CO2, while quinic acid/chlorogenic acids, malic acid and kahweol/cafestol increased in coffee beans from elevated CO2. PLS-DA results revealed a good classification performance between the significant effect of the atmospheric CO2 levels on the fingerprints, regardless of the soil-water availabilities. Finally, the PLS-DA model showed good prediction ability, successfully classifying validation data-set of coffee beans collected over the vertical profile of the plants and included several fingerprints of different extracting solvents. The results of this investigation suggest that the association of experimental design, mixture design, PCA, ASCA and PLS-DA can provide accurate information on a series of metabolic changes provoked by climate changes in products of commercial importance, in addition to minimizing the extra work necessary in classic analytical approaches, encouraging the development of similar strategies.

Salivary metabolome of children and adolescents under peritoneal dialysis.

To study the influence of peritoneal dialysis (PD) on the salivary metabolite profile of children and adolescents with renal failure. Healthy children/adolescents (n = 31; mean age: 12.18 ± 3.76) and children/adolescents subjected to PD (n = 12; mean age: 10.10 ± 4.25) were recruited. Oral health status assessed by the dmft/DMFT and Volpe-Manhold calculus indices. The 1H spectra were acquired in a 600-MHz Bruker nuclear magnetic resonance spectrometer and were subjected to multivariate analysis using partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (O-PLS-DA), and univariate analysis through chi-square and t tests (SPSS 20.0, IL, USA), with a significance level of p < 0.05. A similar caries pattern (p = 0.57; chi-square test) was observed between the healthy (dmft = 0.72 ± 1.28 and DMFT 0.93 ± 2.30) and PD groups (dmft = 2.14 ± 3.67, DMFT 0.33 ± 0.71) and dental calculus (p > 0.05, t test). PLS-DA and O-PLS-DA were able to distinguish both groups (ACC = 0.85, R2 = 0.80, Q2 = 0.15). Salivary metabolites decrease in creatine, propionate, and sugar levels in the PD group and an increase in creatinine, butyrate, and lactate levels when compared with the healthy group. Children and adolescents subjected to PD have a different salivary metabolic profile from that of their healthy subjects. Complications of peritoneal dialysis procedures could be monitored by proper knowledge of saliva characteristics as predictors of peritonitis-related outcome. The use of metabolomics in pediatric nephrology may be an innovative methodology for the early diagnosis and monitoring of kidney diseases.

NMR-based clinical metabolomics revealed distinctive serum metabolic profiles in patients with spondyloarthritis.

Spondyloarthritis (SpA) is a common rheumatic disorder of the young, marred by delay in diagnosis, and paucity of biomarkers of disease activity. The present study aimed to explore the potential of serum metabolic profiling of patients with SpA to identify biomarker for the diagnosis and assessment of disease activity. The serum metabolic profiles of 81 patients with SpA were compared with that of 86 healthy controls (HCs) using nuclear magnetic resonance (NMR)-based metabolomics approach. Seventeen patients were followed up after 3 months of standard treatment, and paired sera were analyzed for effects of therapy. Comparisons were done using the multivariate partial least squares discriminant analysis (PLS-DA), and the discriminatory metabolic entities were identified based on variable importance in projection (VIP) statistics and further evaluated for statistical significance (p value < 0.05). We found that the serum metabolic profiles differed significantly in SpA as compared with HCs. Compared with HC, the SpA patients were characterized by increased serum levels of amino acids, acetate, choline, N-acetyl glycoproteins, Nα-acetyl lysine, creatine/creatinine, and so forth and decreased levels of low-/very low-density lipoproteins and polyunsaturated lipids. PLS-DA analysis also revealed metabolic differences between axial and peripheral SpA patients. Further metabolite profiles were found to differ with disease activity and treatment in responding patients. The results presented in this study demonstrate the potential of serum metabolic profiling of axial SpA as a useful tool for diagnosis, prediction of peripheral disease, assessment of disease activity, and treatment response.

Muscle Metabolome Profiles in Woody Breast-(un)Affected Broilers: Effects of Quantum Blue Phytase-Enriched Diet.

Woody breast (WB) myopathy is significantly impacting modern broilers and is imposing a huge economic burden on the poultry industry worldwide. Yet, its etiology is not fully defined. In a previous study, we have shown that hypoxia and the activation of its upstream mediators (AKT/PI3K/mTOR) played a key role in WB myopathy, and supplementation of quantum blue (QB) can help to reduce WB severity via modulation of hypoxia-related pathways. To gain further insights, we undertook here a metabolomics approach to identify key metabolite signatures and outline their most enriched biological functions. Ultra performance liquid chromatography coupled with high resolution mass spectrometry (UPLC–HRMS) identified a total of 108 known metabolites. Of these, mean intensity differences at P < 0.05 were found in 60 metabolites with 42 higher and 18 lower in WB-affected compared to unaffected muscles. Multivariate analysis and Partial Least Squares Discriminant analysis (PLS-DA) scores plot displayed different clusters when comparing metabolites profile from affected and unaffected tissues and from moderate (MOD) and severe (SEV) WB muscles indicating that unique metabolite profiles are present for the WB-affected and unaffected muscles. To gain biologically related molecule networks, a stringent pathway analyses was conducted using IPA knowledge-base. The top 10 canonical pathways generated, using a fold-change −1.5 and 1.5 cutoff, with the 50 differentially abundant-metabolites were purine nucleotide degradation and de novo biosynthesis, sirtuin signaling pathway, citrulline-nitric oxide cycle, salvage pathways of pyrimidine DNA, IL-1 signaling, iNOS, Angiogenesis, PI3K/AKT signaling, and oxidative phosphorylation. The top altered bio-functions in term of molecular and cellular functions in WB-affected tissues included cellular development, cellular growth and proliferation, cellular death and survival, small molecular biochemistry, inflammatory response, free radical scavenging, cell signaling and cell-to-cell interaction, cell cycles, and lipid, carbohydrate, amino acid, and nucleic acid metabolisms. The top disorder functions identified were organismal injury and abnormalities, cancer, skeletal and muscular disorders, connective tissue disorders, and inflammatory diseases. Breast tissues from birds fed with high dose (2,000 FTU) of QB phytase exhibited 22 metabolites with significantly different levels compared to the control group with a clear cluster using PLS-DA analysis. Of these 22 metabolites, 9 were differentially abundant between WB-affected and unaffected muscles. Taken together, this study determined many metabolic signatures and disordered pathways, which could be regarded as new routes for discovering potential mechanisms of WB myopathy.