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Search Results (1,338)

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14 pages, 2999 KiB  
Article
Metabolomics Profiling of Serum and Urine from Chuanzang Black Pigs with Different Residual Feed Intake
by Xiang Zhou, Chongying Li, Zongze He, Hongwei Liu, Man Wang and Jian He
Animals 2024, 14(16), 2323; https://doi.org/10.3390/ani14162323 (registering DOI) - 12 Aug 2024
Viewed by 68
Abstract
This study was conducted to evaluate associations of blood variables and urine variables with different residual feed intakes (RFIs) in growing Chuanzang black (CB) pigs. A total of 228 growing CB boars from 99 days were used. The same basal diet was offered [...] Read more.
This study was conducted to evaluate associations of blood variables and urine variables with different residual feed intakes (RFIs) in growing Chuanzang black (CB) pigs. A total of 228 growing CB boars from 99 days were used. The same basal diet was offered ad libitum and individual feed intake and body weight were measured over a period of 181 d. The CB pigs were categorized based on their residual feed intake values, with six individuals each from the high and low ends selected and divided into two groups: the low residual feed intake group (LS) and the high residual feed intake group (HS). Serum and urine samples were collected at the end of the experiment for determination of metabolomics profiling. Results showed that there were significantly different metabolites in serum and urine of different RFI groups (fold-change, FC > 2.0 or FC < 0.5, and p < 0.05), and 21 metabolites were identified in serum and 61 in urine. Cluster analysis showed that 20 metabolites were up-regulated and one metabolite was down-regulated in serum; 44 metabolites were up-regulated and 17 metabolites were down-regulated in urine. Kyoto Encyclopedia of Genes and Genomes analysis showed that the differential metabolites of serum were enriched in linoleic acid metabolism, and the differential metabolites of urine were enriched in steroid hormone biosynthesis, taurine and hypotaurine metabolism, and primary bile acid biosynthesis. The correlations between serum metabolites and urine metabolites indicated a significant positive correlation between all fatty acyls in serum metabolites and L-glutamate in urine. However, no compelling genetic or blood biomarkers have been found to explain the differences in RFI, suggesting multiple approaches to effective feed use in pigs. This study provides new insights into the subsequent assessment of RFI by metabolomics profiling, as well as the development of novel feed additives for the factors that will facilitate future research directions in CB pigs. Full article
(This article belongs to the Section Pigs)
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<p>PLS-DA score chart of the HS group (red) and LS group (blue) in serum. LS, the low residual feed intake group; HS, the high residual feed intake group.</p>
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<p>Heatmap of hierarchical clustering analysis for the different metabolites in serum. Each block refers to the abundance of one metabolite from one sample. LS, the low residual feed intake group; HS, the high residual feed intake group.</p>
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<p>Main differential metabolic pathways in serum. The <span class="html-italic">x</span>-axis presents the rich factor. The <span class="html-italic">y</span>-axis presents the concentrated KEGG pathway. The size of the bubble indicates the amount of differential abundance metabolites enriched in this pathway, and the color indicates the significance of enrichment.</p>
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<p>PLS-DA score chart of the HS group (red) and LS group (blue) in urine. LS, the low residual feed intake group; HS, the high residual feed intake group.</p>
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<p>Heatmap of hierarchical clustering analysis for the different metabolites in urine. Each block refers to the abundance of one metabolite from one sample. LS, the low residual feed intake group; HS, the high residual feed intake group.</p>
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<p>Main differential metabolic pathways in urine. The <span class="html-italic">x</span>-axis presents the rich factor. The <span class="html-italic">y</span>-axis presents the concentrated KEGG pathway. The size of the bubble indicates the amount of differential abundance metabolites enriched in this pathway, and the color indicates the significance of enrichment.</p>
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<p>Heat map of correlations between serum differential metabolites and differential metabolites in urine. The redder the color, the stronger the positive correlation, the bluer the color, the stronger the negative correlation, and the flatter the color, the higher the absolute value of the correlation; * indicates <span class="html-italic">p</span> &lt; 0.05.</p>
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20 pages, 341 KiB  
Article
Sex-Specific Associations between Prenatal Exposure to Bisphenols and Phthalates and Infant Epigenetic Age Acceleration
by Gillian England-Mason, Sarah M. Merrill, Jiaying Liu, Jonathan W. Martin, Amy M. MacDonald, David W. Kinniburgh, Nicole Gladish, Julia L. MacIsaac, Gerald F. Giesbrecht, Nicole Letourneau, Michael S. Kobor and Deborah Dewey
Epigenomes 2024, 8(3), 31; https://doi.org/10.3390/epigenomes8030031 (registering DOI) - 10 Aug 2024
Viewed by 235
Abstract
We examined whether prenatal exposure to two classes of endocrine-disrupting chemicals (EDCs) was associated with infant epigenetic age acceleration (EAA), a DNA methylation biomarker of aging. Participants included 224 maternal–infant pairs from a Canadian pregnancy cohort study. Two bisphenols and 12 phthalate metabolites [...] Read more.
We examined whether prenatal exposure to two classes of endocrine-disrupting chemicals (EDCs) was associated with infant epigenetic age acceleration (EAA), a DNA methylation biomarker of aging. Participants included 224 maternal–infant pairs from a Canadian pregnancy cohort study. Two bisphenols and 12 phthalate metabolites were measured in maternal second trimester urines. Buccal epithelial cell cheek swabs were collected from 3 month old infants and DNA methylation was profiled using the Infinium MethylationEPIC BeadChip. The Pediatric-Buccal-Epigenetic tool was used to estimate EAA. Sex-stratified robust regressions examined individual chemical associations with EAA, and Bayesian kernel machine regression (BKMR) examined chemical mixture effects. Adjusted robust models showed that in female infants, prenatal exposure to total bisphenol A (BPA) was positively associated with EAA (B = 0.72, 95% CI: 0.21, 1.24), and multiple phthalate metabolites were inversely associated with EAA (Bs from −0.36 to −0.66, 95% CIs from −1.28 to −0.02). BKMR showed that prenatal BPA was the most important chemical in the mixture and was positively associated with EAA in both sexes. No overall chemical mixture effects or male-specific associations were noted. These findings indicate that prenatal EDC exposures are associated with sex-specific deviations in biological aging, which may have lasting implications for child health and development. Full article
(This article belongs to the Collection Feature Papers in Epigenomes)
14 pages, 282 KiB  
Article
Biomarker Profiling with Targeted Metabolomic Analysis of Plasma and Urine Samples in Patients with Type 2 Diabetes Mellitus and Early Diabetic Kidney Disease
by Maria Mogos, Carmen Socaciu, Andreea Iulia Socaciu, Adrian Vlad, Florica Gadalean, Flaviu Bob, Oana Milas, Octavian Marius Cretu, Anca Suteanu-Simulescu, Mihaela Glavan, Lavinia Balint, Silvia Ienciu, Lavinia Iancu, Dragos Catalin Jianu, Sorin Ursoniu and Ligia Petrica
J. Clin. Med. 2024, 13(16), 4703; https://doi.org/10.3390/jcm13164703 (registering DOI) - 10 Aug 2024
Viewed by 245
Abstract
Background: Over the years, it was noticed that patients with diabetes have reached an alarming number worldwide. Diabetes presents many complications, including diabetic kidney disease (DKD), which can be considered the leading cause of end-stage renal disease. Current biomarkers such as serum [...] Read more.
Background: Over the years, it was noticed that patients with diabetes have reached an alarming number worldwide. Diabetes presents many complications, including diabetic kidney disease (DKD), which can be considered the leading cause of end-stage renal disease. Current biomarkers such as serum creatinine and albuminuria have limitations for early detection of DKD. Methods: In our study, we used UHPLC-QTOF-ESI+-MS techniques to quantify previously analyzed metabolites. Based on one-way ANOVA and Fisher’s LSD, untargeted analysis allowed the discrimination of six metabolites between subgroups P1 versus P2 and P3: tryptophan, kynurenic acid, taurine, l-acetylcarnitine, glycine, and tiglylglycine. Results: Our results showed several metabolites that exhibited significant differences among the patient groups and can be considered putative biomarkers in early DKD, including glycine and kynurenic acid in serum (p < 0.001) and tryptophan and tiglylglycine (p < 0.001) in urine. Conclusions: Although we identified metabolites as potential biomarkers in the present study, additional studies are needed to validate these results. Full article
(This article belongs to the Section Endocrinology & Metabolism)
19 pages, 1290 KiB  
Review
Prospect and Challenges of Volatile Organic Compound Breath Testing in Non-Cancer Gastrointestinal Disorders
by Weiyang Zheng, Ke Pang, Yiyang Min and Dong Wu
Biomedicines 2024, 12(8), 1815; https://doi.org/10.3390/biomedicines12081815 - 9 Aug 2024
Viewed by 359
Abstract
Breath analysis, despite being an overlooked biomatrix, has a rich history in disease diagnosis. However, volatile organic compounds (VOCs) have yet to establish themselves as clinically validated biomarkers for specific diseases. As focusing solely on late-stage or malignant disease biomarkers may have limited [...] Read more.
Breath analysis, despite being an overlooked biomatrix, has a rich history in disease diagnosis. However, volatile organic compounds (VOCs) have yet to establish themselves as clinically validated biomarkers for specific diseases. As focusing solely on late-stage or malignant disease biomarkers may have limited relevance in clinical practice, the objective of this review is to explore the potential of VOC breath tests for the diagnosis of non-cancer diseases: (1) Precancerous conditions like gastro-esophageal reflux disease (GERD) and Barrett’s esophagus (BE), where breath tests can complement endoscopic screening; (2) endoluminal diseases associated with autoinflammation and dysbiosis, such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and coeliac disease, which currently rely on biopsy and symptom-based diagnosis; (3) chronic liver diseases like cirrhosis, hepatic encephalopathy, and non-alcoholic fatty liver disease, which lack non-invasive diagnostic tools for disease progression monitoring and prognostic assessment. A literature search was conducted through EMBASE, MEDLINE, and Cochrane databases, leading to an overview of 24 studies. The characteristics of these studies, including analytical platforms, disorder type and stage, group size, and performance evaluation parameters for diagnostic tests are discussed. Furthermore, how VOCs can be utilized as non-invasive diagnostic tools to complement existing gold standards is explored. By refining study designs, sampling procedures, and comparing VOCs in urine and blood, we can gain a deeper understanding of the metabolic pathways underlying VOCs. This will establish breath analysis as an effective non-invasive method for differential diagnosis and disease monitoring. Full article
(This article belongs to the Section Molecular and Translational Medicine)
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<p>PRISMA flowchart describing literature search strategy.</p>
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<p>Analytical platforms and numbers of studies on VOC breath testing for non-cancer gastrointestinal disorders.</p>
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<p>Quality assessment of the studies of risk of bias and concerns regarding applicability based on QUADAS 2.</p>
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18 pages, 2893 KiB  
Article
Early Detection of Chronic Kidney Disease Using Plasma Neutrophil Gelatinase-Associated Lipocalin and Kidney Injury Molecule-1 in Small-Breed Dogs: A Retrospective Pilot Study
by Hyo-Sung Kim, Han-Jun Kim and Sun-Hee Do
Animals 2024, 14(16), 2313; https://doi.org/10.3390/ani14162313 - 9 Aug 2024
Viewed by 331
Abstract
Multiple diagnostic modalities are urgently needed to identify early-stage kidney diseases. Various molecules have been investigated; however, most studies have focused on identifying specific biomarkers in urine. Considering that assessing the symmetrical dimethylarginine (SDMA) plasma concentration is more suitable as an early diagnostic [...] Read more.
Multiple diagnostic modalities are urgently needed to identify early-stage kidney diseases. Various molecules have been investigated; however, most studies have focused on identifying specific biomarkers in urine. Considering that assessing the symmetrical dimethylarginine (SDMA) plasma concentration is more suitable as an early diagnostic test for chronic kidney disease (CKD) in routine veterinary practice, we aimed to investigate the clinical usefulness of plasma neutrophil gelatinase-associated lipocalin (pNGAL) and plasma kidney injury molecule-1 (pKIM-1) concentrations for CKD detection in small-breed dogs. Through a retrospective analysis, we found that numerous clinicopathological data showed a log-normal distribution, even when they satisfied normality tests. Moreover, the log-transformed pNGAL and pKIM-1 concentrations successfully identified CKD International Renal Interest Society (IRIS) stages 1–4 and the risk group with underlying CKD risk factors. Correlation analysis and group comparison of other factors confirmed the possibility of using these two biomarkers for detecting the CKD risk group and IRIS stage 1. Receiver operating characteristic curve analysis revealed that the diagnostic accuracy for discriminating the risk group was superior in the order of pKIM-1, pNGAL, SDMA, and serum creatinine levels. In conclusion, these results suggest that pKIM-1 and pNGAL are possible early or quantifiable markers of insignificant CKD or can be at least used as an adjunct with traditional indicators. Full article
(This article belongs to the Special Issue Advanced Biomarker Research in Animal Pathological States)
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<p>Correlation between traditional and novel kidney injury biomarkers. Biomarkers showed a linear relationship with each other after the X and Y axes were displayed as log scales. Each scattered dot represents an individual case, colored by the chronic kidney disease stage. The line indicates simple linear regression, accompanied by small black dots describing 95% confidence intervals. Abbreviations: SLG, slope gradient; R<sup>2</sup>, coefficient of determination (R-squared), indicating the goodness-of-fit measure of the linear model; pNGAL, plasma neutrophil gelatinase-associated lipocalin; sCr, serum creatinine; SDMA, symmetrical dimethylarginine; IRIS, International Renal Interest Society.</p>
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<p>Normality and log-normality analysis of novel kidney injury biomarkers. pNGAL and pKIM-1 showed a log-normal distribution. Q-Q plots of two objective biomarkers were compared before and after logarithmic transformation. Q-Q plots of all other data are presented in <a href="#app1-animals-14-02313" class="html-app">Supplementary Figure S2</a>. Red dotted lines indicate the identity line, whereas points forming a straight line indicate a suitable distribution. Abbreviations: pNGAL, plasma neutrophil gelatinase-associated lipocalin; pKIM-1, plasma kidney injury molecule-1.</p>
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<p>Statistical analysis of differences in the concentrations of biomarkers between the groups after log transformation. No significant differences in serum creatinine (sCr) and symmetrical dimethylarginine (SDMA) concentrations were observed between the control, risk, and stage 1 groups, whereas a significant difference in pNGAL and pKIM-1 was found. The <span class="html-italic">p</span>-value between the stage 3–4 group and other groups was omitted because they were all &lt;0.001 for four biomarkers. The lines indicate a mean ± 95% confidence interval.</p>
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<p>Correlation matrix between kidney biomarkers and clinicopathological indicators. The numbers in each cross box are r coefficients, indicating linearity. Red and blue correspond to positive and negative correlations, respectively. Hashes (#) indicate that the markers were analyzed after log<sub>2</sub> transformation, as they showed a log-normal distribution. The black superscripted “ns” indicates that the correlation was not statistically significant; otherwise, the <span class="html-italic">p</span>-value was &lt;0.05. A white superscripted asterisk (*) indicates a comparison with sCr; *, <span class="html-italic">p</span> &lt; 0.05. Abbreviations: BUN, blood urea nitrogen; sCr, serum creatinine; SDMA, symmetric dimethylarginine; pNGAL, plasma neutrophil gelatinase-associated lipocalin; pKIM-1, plasma kidney injury molecule-1; RBC, red blood cell; HCT, hematocrit; HGB, hemoglobin; RETIC, reticulocyte count; INF, inflammation; WBC, white blood cell; NEU, neutrophil count; LYM, lymphocyte count; MONO, monocyte count; CRP, C-reactive protein; TP, total protein; ALB, albumin; GLB, globulin; A/G, albumin-to-globulin ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; AML, amylase; LPS, lipase; CK, creatinine kinase; CO, calculated osmolality; UP, urine protein concentration; UC, urine creatinine concentration; UPC, urine protein-to-urine creatinine ratio; USG, urine-specific gravity; BW, body weight; sysBP, systolic blood pressure; MMVD, myxomatous mitral valve disease; HAC, hyperadrenocorticism.</p>
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<p>Accuracy of the biomarkers in detecting chronic kidney disease. (<b>A</b>) The control group was classified as the negative instance, whereas the risk and IRIS stage 1–4 groups were classified as positive instances. Overall, the areas under the curves of pNGAL and pKIM-1 were found to be comparable and were slightly higher than those of SDMA; sCr exhibited the lowest AUC. (<b>B</b>) The control and risk groups were classified as negative instances, whereas the IRIS stage 1–4 groups were classified as positive instances. Overall, the areas under the curves were highest in the order of pNGAL, symmetrical dimethylarginine (SDMA), pKIM-1, and serum creatinine (sCr). (<b>C</b>) The control, risk, and IRIS stage 1 groups were classified as negative instances, whereas the IRIS stage 2–4 groups were classified as positive instances. Overall, the areas under the curves of pKIM-1 were higher than those of pNGAL. Abbreviations: AUC, area under the curve; pNGAL, plasma neutrophil gelatinase-associated lipocalin; pKIM-1, plasma kidney injury molecule-1.</p>
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25 pages, 723 KiB  
Review
Biomarker-Based Precision Therapy for Alzheimer’s Disease: Multidimensional Evidence Leading a New Breakthrough in Personalized Medicine
by Anastasia Bougea and Philippos Gourzis
J. Clin. Med. 2024, 13(16), 4661; https://doi.org/10.3390/jcm13164661 - 8 Aug 2024
Viewed by 373
Abstract
(1) Background: Alzheimer’s disease (AD) is a worldwide neurodegenerative disorder characterized by the buildup of abnormal proteins in the central nervous system and cognitive decline. Since no radical therapy exists, only symptomatic treatments alleviate symptoms temporarily. In this review, we will explore the [...] Read more.
(1) Background: Alzheimer’s disease (AD) is a worldwide neurodegenerative disorder characterized by the buildup of abnormal proteins in the central nervous system and cognitive decline. Since no radical therapy exists, only symptomatic treatments alleviate symptoms temporarily. In this review, we will explore the latest advancements in precision medicine and biomarkers for AD, including their potential to revolutionize the way we diagnose and treat this devastating condition. (2) Methods: A literature search was performed combining the following Medical Subject Heading (MeSH) terms on PubMed: “Alzheimer’s disease”, “biomarkers”, “APOE”, “APP”, “GWAS”, “cerebrospinal fluid”, “polygenic risk score”, “Aβ42”, “τP-181”, “ p-tau217”, “ptau231”, “proteomics”, “total tau protein”, and “precision medicine” using Boolean operators. (3) Results: Genome-wide association studies (GWAS) have identified numerous genetic variants associated with AD risk, while a transcriptomic analysis has revealed dysregulated gene expression patterns in the brains of individuals with AD. The proteomic and metabolomic profiling of biological fluids, such as blood, urine, and CSF, and neuroimaging biomarkers have also yielded potential biomarkers of AD that could be used for the early diagnosis and monitoring of disease progression. (4) Conclusion: By leveraging a combination of the above biomarkers, novel ultrasensitive immunoassays, mass spectrometry methods, and metabolomics, researchers are making significant strides towards personalized healthcare for individuals with AD. Full article
(This article belongs to the Special Issue Potential Cures of Alzheimer's Dementia)
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<p>A flowchart of the study selection.</p>
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12 pages, 3899 KiB  
Article
Hybrid Polystyrene–Plasmonic Systems as High Binding Density Biosensing Platforms
by Charles M. Darr, Juiena Hasan, Cherian Joseph Mathai, Keshab Gangopadhyay, Shubhra Gangopadhyay and Sangho Bok
Int. J. Mol. Sci. 2024, 25(16), 8603; https://doi.org/10.3390/ijms25168603 - 7 Aug 2024
Viewed by 293
Abstract
Sensitive, accurate, and early detection of biomarkers is essential for prompt response to medical decisions for saving lives. Some infectious diseases are deadly even in small quantities and require early detection for patients and public health. The scarcity of these biomarkers necessitates signal [...] Read more.
Sensitive, accurate, and early detection of biomarkers is essential for prompt response to medical decisions for saving lives. Some infectious diseases are deadly even in small quantities and require early detection for patients and public health. The scarcity of these biomarkers necessitates signal amplification before diagnosis. Recently, we demonstrated single-molecule-level detection of tuberculosis biomarker, lipoarabinomannan, from patient urine using silver plasmonic gratings with thin plasma-activated alumina. While powerful, biomarker binding density was limited by the surface density of plasma-activated carbonyl groups, that degraded quickly, resulting in immediate use requirement after plasma activation. Therefore, development of stable high density binding surfaces such as high binding polystyrene is essential to improving shelf-life, reducing binding protocol complexity, and expanding to a wider range of applications. However, any layers topping the plasmonic grating must be ultra-thin (<10 nm) for the plasmonic enhancement of adjacent signals. Furthermore, fabricating thin polystyrene layers over alumina is nontrivial because of poor adhesion between polystyrene and alumina. Herein, we present the development of a stable, ultra-thin polystyrene layer on the gratings, which demonstrated 63.8 times brighter fluorescence compared to commercial polystyrene wellplates. Spike protein was examined for COVID-19 demonstrating the single-molecule counting capability of the hybrid polystyrene-plasmonic gratings. Full article
(This article belongs to the Special Issue Recent Advances on Bioreceptors and Nanomaterial-Based Biosensors)
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<p>The structure of plasmonic gratings consists of Ag, Al<sub>2</sub>O<sub>3</sub>, and polystyrene on top of PMSSQ.</p>
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<p>The relationship between the film thickness and the concentration (Inset: the relationship in low concentration).</p>
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<p>Optical profilometry after tape test of thin polystyrene films over (<b>a</b>) unsilanized alumina, (<b>b</b>) TMCS, (<b>c</b>) P Silane, (<b>d</b>) N Silane, and (<b>e</b>) G Silane.</p>
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<p>Atomic force microscopy of polystyrene over alumina-coated gratings (<b>a</b>–<b>d</b>) and flat silver (<b>e</b>–<b>h</b>): (<b>a</b>,<b>e</b>) as-prepared ALD alumina; (<b>b</b>,<b>f</b>) 5 nm polystyrene; (<b>c</b>,<b>g</b>) 7 nm polystyrene; and (<b>d</b>,<b>h</b>) 9 nm polystyrene. (Scale bar = 1 µm).</p>
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<p>FTIR spectra of (<b>a</b>) commercial Nunc plates and PS resin and (<b>b</b>) 5 nm and 50 nm PS films.</p>
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<p>Fluorescence intensity of PS coated grating, grating without PS, and commercial plates.</p>
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<p>Schematic of the rectangular wells of the 24-well adapter showing the maximum incidence angles, α = 19.6° and β = 24.1°.</p>
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<p>(<b>a</b>) A fluorescence image of 100 fg/mL of S-protein with 6 μm × 6 μm grids, and (<b>b</b>) single molecule counting from plasmonic grating with various concentrations of spike protein between 1 fg/mL and 10 pg/mL.</p>
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19 pages, 1618 KiB  
Review
Overview of Bovine Mastitis: Application of Metabolomics in Screening Its Predictive and Diagnostic Biomarkers
by Muyang Li, Zhongjie Li, Ming Deng, Dewu Liu, Baoli Sun, Jianying Liu, Jianchao Guo and Yongqing Guo
Animals 2024, 14(15), 2264; https://doi.org/10.3390/ani14152264 - 4 Aug 2024
Viewed by 407
Abstract
Bovine mastitis is an inflammatory disease of the mammary glands, and its pathogenesis and diagnosis are complicated. Through qualitative and quantitative analysis of small-molecule metabolites, the metabolomics technique plays an important role in finding biomarkers and studying the metabolic mechanism of bovine mastitis. [...] Read more.
Bovine mastitis is an inflammatory disease of the mammary glands, and its pathogenesis and diagnosis are complicated. Through qualitative and quantitative analysis of small-molecule metabolites, the metabolomics technique plays an important role in finding biomarkers and studying the metabolic mechanism of bovine mastitis. Therefore, this paper reviews the predictive and diagnostic biomarkers of bovine mastitis that have been identified using metabolomics techniques and that are present in samples such as milk, blood, urine, rumen fluid, feces, and mammary tissue. In addition, the metabolic pathways of mastitis-related biomarkers in milk and blood were analyzed; it was found that the tricarboxylic acid (TCA) cycle was the most significant (FDR = 0.0015767) pathway in milk fluid, and glyoxylate and dicarboxylate metabolism was the most significant (FDR = 0.0081994) pathway in blood. The purpose of this review is to provide useful information for the prediction and early diagnosis of bovine mastitis. Full article
(This article belongs to the Special Issue Advanced Biomarker Research in Animal Pathological States)
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<p>A technical roadmap for the metabolomics technique. The metabolomics technique, regardless of the experimental platform, can be divided into five processes, including (<b>a</b>) sample preparation, sample processing (metabolite extraction and derivatization), chemical analysis (compound identification via platforms, such as NMR, GC−MS, LC−MS), (<b>b</b>) data normalization and statistical analysis, and pathway analysis and biological interpretation. Created with BioRender.com.</p>
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<p>Pathway analysis of differential metabolites for prediction and early diagnosis of bovine mastitis. (<b>a</b>) Mastitis/healthy control metabolomics pathway analysis with milk samples, (<b>b</b>) mastitis/healthy control metabolomics pathway analysis with blood samples. Data were sourced from <a href="#app1-animals-14-02264" class="html-app">Supplementary Tables S1 and S2</a>. Note: The sizes and colors of the circles represent the corresponding metabolites ratio and the log (p-value) of each pathway, respectively.</p>
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<p>The mechanisms of the citrate cycle (TCA cycle), glyoxylate, and dicarboxylate metabolism pathways. The key differential metabolites associated with mastitis are circled in red. (<b>a</b>) The mechanisms of the citrate cycle (TCA cycle) pathway (bta00020). (<b>b</b>) The mechanisms of the glyoxylate and dicarboxylate metabolism pathway (bta00630). Sourced from the KEGG <span class="html-italic">Bos taurus</span> database.</p>
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25 pages, 7059 KiB  
Article
Propionic Acidemia, Methylmalonic Acidemia, and Cobalamin C Deficiency: Comparison of Untargeted Metabolomic Profiles
by Anna Sidorina, Giulio Catesini, Elisa Sacchetti, Cristiano Rizzo and Carlo Dionisi-Vici
Metabolites 2024, 14(8), 428; https://doi.org/10.3390/metabo14080428 - 2 Aug 2024
Viewed by 368
Abstract
Methylmalonic acidemia (MMA), propionic acidemia (PA), and cobalamin C deficiency (cblC) share a defect in propionic acid metabolism. In addition, cblC is also involved in the process of homocysteine remethylation. These three diseases produce various phenotypes and complex downstream metabolic effects. In this [...] Read more.
Methylmalonic acidemia (MMA), propionic acidemia (PA), and cobalamin C deficiency (cblC) share a defect in propionic acid metabolism. In addition, cblC is also involved in the process of homocysteine remethylation. These three diseases produce various phenotypes and complex downstream metabolic effects. In this study, we used an untargeted metabolomics approach to investigate the biochemical differences and the possible connections among the pathophysiology of each disease. The significantly changed metabolites in the untargeted urine metabolomic profiles of 21 patients (seven MMA, seven PA, seven cblC) were identified through statistical analysis (p < 0.05; log2FC > |1|) and then used for annotation. Annotated features were associated with different metabolic pathways potentially involved in the disease’s development. Comparative statistics showed markedly different metabolomic profiles between MMA, PA, and cblC, highlighting the characteristic species for each disease. The most affected pathways were related to the metabolism of organic acids (all diseases), amino acids (all diseases), and glycine and its conjugates (in PA); the transsulfuration pathway; oxidative processes; and neurosteroid hormones (in cblC). The untargeted metabolomics study highlighted the presence of significant differences between the three diseases, pointing to the most relevant contrast in the cblC profile compared to MMA and PA. Some new biomarkers were proposed for PA, while novel data regarding the alterations of steroid hormone profiles and biomarkers of oxidative stress were obtained for cblC disease. The elevation of neurosteroids in cblC may indicate a potential connection with the development of ocular and neuronal deterioration. Full article
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<p>PCA obtained from untargeted metabolomics data acquired by C18 and HILIC columns in negative ionization mode. Both experimental conditions demonstrate the major separation of the cblC group and the closer similarity between the MMA and PA groups.</p>
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<p>Pathway enrichment analysis. Node size (pathway impact) corresponds to the relative number and position of matched metabolites in the selected pathway; colors, varying from yellow to red, indicate the different levels of significance. The named pathways include only those with <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Conventional organic acid biomarkers of MMA, PA, and cblC: (<b>a</b>) methylmalonic acid, (<b>b</b>) 2-methylcitric acid, (<b>f</b>) 2-methyl-3-hydroxy-valeric acid, (<b>g</b>) 2-methyl-oxo-valeric acid, (<b>h</b>) 3-oxo-valeric acid; Krebs cycle organic acids (<b>c</b>) citric acid, (<b>d</b>) malic acid, (<b>e</b>) fumaric acid; and ketones (<b>i</b>) 2-butanone, (<b>j</b>) 3-pentanone significantly changed between three acidemias. *—<span class="html-italic">p</span>-value &lt; 0.05; **—<span class="html-italic">p</span>-value &lt; 0.01; ***—<span class="html-italic">p</span>-value &lt; 0.001; N.S.—non-significant.</p>
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<p>Significantly changed amino acids: (<b>a</b>) valine, (<b>b</b>) isoleucine, (<b>c</b>) threonine, (<b>m</b>) lysine; and small peptides (<b>d</b>) valylvaline, (<b>e</b>) isoleucylvaline, (<b>f</b>) isoleucylalanine, (<b>g</b>) glutamylisoleucine, (<b>h</b>) butyl-alpha-aspartyl-allothreoninate, (<b>j</b>) aspartyl-phenylalanine, (<b>k</b>) prolylproline, (<b>l</b>) glycylglycyl-alanyl-2-methylalanine increased in cblC may be a result of dietary differences between groups. Significantly changed levels of (<b>i</b>) dimethylglycine, (<b>n</b>) citrulline, (<b>o</b>) methionine, and (<b>p</b>) glycine reflect the involvement of different metabolic pathways in the diseases. *—<span class="html-italic">p</span>-value &lt; 0.05; **—<span class="html-italic">p</span>-value &lt; 0.01; ***—<span class="html-italic">p</span>-value &lt; 0.001; N.S.—non-significant.</p>
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<p>Glycine and carnitine conjugates. Glycine conjugates increased only in PA group: (<b>a</b>) propionylglycine, (<b>b</b>) butyrylglycine, (<b>c</b>) tiglylglycine, (<b>g</b>) glycine conjugate of propionylcarnitine; carnitine conjugates increased in MMA and PA: (<b>e</b>) propionylcarnitine; and in MMA and cblC: (<b>f</b>) c4DC-carnitine; (<b>d</b>) free carnitine had no differences between groups. *—<span class="html-italic">p</span>-value &lt; 0.05; **—<span class="html-italic">p</span>-value &lt; 0.01; ***—<span class="html-italic">p</span>-value &lt; 0.001; N.S.—non-significant.</p>
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<p>Increased (<b>a</b>) homocysteine and transsulfuration pathway metabolites in cblC: (<b>b</b>) cystathionine, (<b>c</b>) cysteine, (<b>d</b>) 2-hydroxybyryric acid; and oxidized sulfur-containing anions: (<b>e</b>) sulfuric and (<b>f</b>) thiosulfuric acid. *—<span class="html-italic">p</span>-value &lt; 0.05; **—<span class="html-italic">p</span>-value &lt; 0.01; ***—<span class="html-italic">p</span>-value &lt; 0.001; N.S.—non-significant.</p>
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<p>New characteristic compounds of cblC disease. (<b>a</b>) Oxidative stress biomarkers. (<b>b</b>) Steroid hormones with putative annotations belonging to androstane core class, namely 3β,8xi,9xi,14xi,17β-androst-5-ene-3,17-diyl bis hydrogen sulfate, androsterone sulfate, 16α-hydroxydehydroepiandrosterone-3-sulfate, 4-androstene-3β,17β-diol disulfate, dehydroepiandrosterone sulfate, 16,17-dihydroxyandrost-5-en-3-yl hydrogen sulfate, 9-hydroxyandrosta-1,4-diene-3,17-dione, and pregnane core class, namely 3β-(phenylacetoxy)pregna-5-ene-20-one; 3,20-dioxopregn-4-en-17-yl 4-methylbenzoate; 3α,5α-20-oxopregnan-3-yl beta-D-glucopyranosiduronic acid. *—<span class="html-italic">p</span>-value &lt; 0.05; **—<span class="html-italic">p</span>-value &lt; 0.01; ***—<span class="html-italic">p</span>-value &lt; 0.001; N.S.—non-significant.</p>
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<p>The CxHyNO features significantly increased in PA. ***—<span class="html-italic">p</span>-value &lt; 0.001; N.S.—non-significant.</p>
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<p>(<b>a</b>) The heatmap based on the 25 most up-/down-regulated features in PA (<span class="html-italic">p</span> &lt; 0.001); (<b>b</b>) the heatmap based on the 25 most up-/down-regulated features in MMA (<span class="html-italic">p</span> &lt; 0.001); (<b>c</b>) the heatmap based on the 25 most up-/down-regulated features in cblC (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>The most relevant findings from the relative comparison of the metabolomic profiles in PA, MMA, and cblC. ↑ (↓)—metabolites significantly increased (decreased) with respect to other two acidemias. Dashed boxes—physiological processes involving significantly changed metabolites.</p>
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12 pages, 255 KiB  
Article
Association of Maternal Air Pollution Exposure and Infant Lung Function Is Modified by Genetic Propensity to Oxidative Stress
by Dwan Vilcins, Wen Ray Lee, Cindy Pham, Sam Tanner, Luke D. Knibbs, David Burgner, Tamara L. Blake, Toby Mansell, Anne-Louise Ponsonby, Peter D. Sly and Barwon Infant Study Investigator Group
Children 2024, 11(8), 937; https://doi.org/10.3390/children11080937 - 31 Jul 2024
Viewed by 406
Abstract
Background and objective: The association between air pollution and poor respiratory health outcomes is well established. Children are particularly at risk from air pollution, especially during the prenatal period as their organs and systems are still undergoing crucial development. This study investigated maternal [...] Read more.
Background and objective: The association between air pollution and poor respiratory health outcomes is well established. Children are particularly at risk from air pollution, especially during the prenatal period as their organs and systems are still undergoing crucial development. This study investigated maternal exposure to air pollution during pregnancy and oxidative stress (OS), inflammation, and infant lung function at 4 weeks of age. Methods: Data from the Barwon Infant Study were available for 314 infants. The exposure to NO2 and PM2.5 were estimated. Infant lung function (4 weeks) was measured by multiple-breath washout. Glycoprotein acetyls (GlycA) (36 weeks prenatal), cord blood, and OS biomarkers were measured in maternal urine (28 weeks). A genetic pathway score for OS (gPFSox) was calculated. Linear regression was used and potential modification by the OS genotype was tested. Results: There was no relationship between maternal exposure to air pollution and infant lung function, or with GlycA or OS during pregnancy. We found an association in children with a genetic propensity to OS between NO2 and a lower functional residual capacity (FRC) (β = −5.3 mls, 95% CI (−9.3, −1.3), p = 0.01) and lung clearance index (LCI) score (β = 0.46 turnovers, (95% CI 0.10, 0.82), p = 0.01). Conclusion: High prenatal exposure to ambient NO2 is associated with a lower FRC and a higher LCI score in infants with a genetic propensity to oxidative stress. There was no relationship between maternal exposure to air pollution with maternal and cord blood inflammation or OS biomarkers. Full article
(This article belongs to the Special Issue Updates on Lung Function, Respiratory and Asthma Disease in Children)
11 pages, 409 KiB  
Article
Effects of Environmental Tobacco Smoke on Oxidative Stress in Childhood: A Human Biomonitoring Study
by Arianna Antonucci, Roberta Andreoli, Chiara Maccari, Matteo Vitali and Carmela Protano
Toxics 2024, 12(8), 557; https://doi.org/10.3390/toxics12080557 - 30 Jul 2024
Viewed by 443
Abstract
Household smoking is one of the main sources of environmental tobacco smoke (ETS) exposure for children, a population considered to be at high risk for associated negative health outcomes. Several studies evidenced the occurrence of early effects related to ETS exposure, including the [...] Read more.
Household smoking is one of the main sources of environmental tobacco smoke (ETS) exposure for children, a population considered to be at high risk for associated negative health outcomes. Several studies evidenced the occurrence of early effects related to ETS exposure, including the development of the oxidative stress process. The aim of this study was to evaluate the correlation between urinary levels of 8-oxo-7,8-dihydro-2-deoxyguanosine (8oxodGuo), a nucleic acid oxidation biomarker, and socio-demographic features and lifestyle factors in school children (aged 5–11 years). A cross-sectional study was conducted among 154 healthy children, residing in rural zones of central Italy. For each participant, one urine sample was analyzed by the HPLC-MS/MS technique to simultaneously quantify 8oxodGuo and cotinine (a biomarker of ETS exposure), while information on the children was collected using a questionnaire filled out by the parents. Urinary levels of 8oxodGuo was found to be significantly higher in children exposed to ETS compared to those not exposed (5.53 vs. 4.78 μg/L; p = 0.019). This result was confirmed by the significant association observed between urinary levels of cotinine and 8oxodGuo (r = 0.364, p < 0.0001). Additionally, children exposed to ETS with no smoking ban at home showed a further increased difference than those not exposed (6.35 μg/L vs. 4.78 μg/L; p = 0.008). Considering the great number of adverse effects on human health due to exposure to passive smoking, especially if this exposure begins early in life, it is essential to implement health promotion interventions in this area. Full article
(This article belongs to the Section Human Toxicology and Epidemiology)
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<p>Correlation between the two investigated biomarkers: Log<sub>2</sub>(cotinine) (biomarker of exposure) and Log<sub>2</sub>(8oxodGuo) (biomarker of effect).</p>
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15 pages, 2004 KiB  
Article
QuEChERS Extraction and Simultaneous Quantification in GC-MS/MS of Hexahydrocannabinol Epimers and Their Metabolites in Whole Blood, Urine, and Oral Fluid
by Annagiulia Di Trana, Giorgia Sprega, Giorgi Kobidze, Omayema Taoussi, Alfredo Fabrizio Lo Faro, Giulia Bambagiotti, Eva Montanari, Maria Sofia Fede, Jeremy Carlier, Anastasio Tini, Francesco Paolo Busardò, Alessandro Di Giorgi and Simona Pichini
Molecules 2024, 29(14), 3440; https://doi.org/10.3390/molecules29143440 - 22 Jul 2024
Viewed by 487
Abstract
Recently, hexahydrocannabinol (HHC) was posed under strict control in Europe due to the increasing HHC-containing material seizures. The lack of analytical methods in clinical laboratories to detect HHC and its metabolites in biological matrices may result in related intoxication underreporting. We developed and [...] Read more.
Recently, hexahydrocannabinol (HHC) was posed under strict control in Europe due to the increasing HHC-containing material seizures. The lack of analytical methods in clinical laboratories to detect HHC and its metabolites in biological matrices may result in related intoxication underreporting. We developed and validated a comprehensive GC-MS/MS method to quantify 9(R)-HHC, 9(S)-HHC, 9αOH-HHC, 9βOH-HHC, 8(R)OH-9(R)-HHC, 8(S)OH-9(S)HHC, 11OH-9(R)HHC, 11OH-9(S)HHC, 11nor-carboxy-9(R)-HHC, and 11nor-carboxy-9(S)-HHC in whole blood, urine, and oral fluid. A novel QuEChERS extraction protocol was optimized selecting the best extraction conditions suitable for all the three matrices. Urine and blood were incubated with β-glucuronidase at 60 °C for 2 h. QuEChERS extraction was developed assessing different ratios of Na2SO4:NaCl (4:1, 2:1, 1:1, w/w) to be added to 200 µL of any matrix added with acetonitrile. The chromatographic separation was achieved on a 7890B GC with an HP-5ms column, (30 m, 0.25 mm × 0.25 µm) in 12.50 min. The analytes were detected with a triple-quadrupole mass spectrometer in the MRM mode. The method was fully validated following OSAC guidelines. The method showed good validation parameters in all the matrices. The method was applied to ten real samples of whole blood (n = 4), urine (n = 3), and oral fluid (n = 3). 9(R)-HHC was the prevalent epimer in all the samples (9(R)/9(S) = 2.26). As reported, hydroxylated metabolites are proposed as urinary biomarkers, while carboxylated metabolites are hematic biomarkers. Furthermore, 8(R)OH-9(R)HHC was confirmed as the most abundant metabolite in all urine samples. Full article
(This article belongs to the Section Natural Products Chemistry)
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<p>Molecular structures of the ten target analytes.</p>
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<p>Representative extracted MRM chromatogram of the target analytes in spiked oral fluid (<b>A</b>), urine (<b>B</b>), and whole blood (<b>C</b>). Legend: (1) 9(R)-HHC; (2) 9(S)-HHC; (3) 9αOH-HHC; (4) 8(R)OH-9(R)-HHC; (5) 9βOH-HHC; (6) 8(S)OH-9(S)HHC; (7) 11OH-9(R)HHC; (8) 11OH-9(S)HHC; (9) 11nor-carboxy-9(R)-HHC; and (10) 11nor-carboxy-9(S)-HHC.</p>
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<p>Representative extracted MRM chromatogram of the target analytes in spiked oral fluid (<b>A</b>), urine (<b>B</b>), and whole blood (<b>C</b>). Legend: (1) 9(R)-HHC; (2) 9(S)-HHC; (3) 9αOH-HHC; (4) 8(R)OH-9(R)-HHC; (5) 9βOH-HHC; (6) 8(S)OH-9(S)HHC; (7) 11OH-9(R)HHC; (8) 11OH-9(S)HHC; (9) 11nor-carboxy-9(R)-HHC; and (10) 11nor-carboxy-9(S)-HHC.</p>
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<p>Representative baseline-scaled extracted ion chromatogram of the pooled blank matrix (on the <b>left</b>) of oral fluid (<b>A</b>), urine (<b>B</b>), and whole blood (<b>C</b>) compared to the extracted ion chromatogram of the spiked matrix at the Limit of Quantification for all the target analytes (on the <b>right</b>). Legend: (1) 9(R)-HHC; (2) 9(S)-HHC; (3) 9αOH-HHC; (4) 8(R)OH-9(R)-HHC; (5) 9βOH-HHC; (6) 8(S)OH-9(S)HHC; (7) 11OH-9(R)HHC; (8) 11OH-9(S)HHC; (9) 11nor-carboxy-9(R)-HHC; and (10) 11nor-carboxy-9(S)-HHC.</p>
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<p>Putative metabolic pathways of HHC epimers.</p>
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21 pages, 1627 KiB  
Article
High Sensitivity and Specificity Platform to Validate MicroRNA Biomarkers in Cancer and Human Diseases
by Anastassia Kanavarioti, M. Hassaan Rehman, Salma Qureshi, Aleena Rafiq and Madiha Sultan
Non-Coding RNA 2024, 10(4), 42; https://doi.org/10.3390/ncrna10040042 - 22 Jul 2024
Viewed by 587
Abstract
We developed a technology for detecting and quantifying trace nucleic acids using a bracketing protocol designed to yield a copy number with approximately ± 20% accuracy across all concentrations. The microRNAs (miRNAs) let-7b, miR-15b, miR-21, miR-375 and miR-141 were measured in serum and [...] Read more.
We developed a technology for detecting and quantifying trace nucleic acids using a bracketing protocol designed to yield a copy number with approximately ± 20% accuracy across all concentrations. The microRNAs (miRNAs) let-7b, miR-15b, miR-21, miR-375 and miR-141 were measured in serum and urine samples from healthy subjects and patients with breast, prostate or pancreatic cancer. Detection and quantification were amplification-free and enabled using osmium-tagged probes and MinION, a nanopore array detection device. Combined serum from healthy men (Sigma-Aldrich, St. Louis, MO, USA #H6914) was used as a reference. Total RNA isolated from biospecimens using commercial kits was used as the miRNA source. The unprecedented ± 20% accuracy led to the conclusion that miRNA copy numbers must be normalized to the same RNA content, which in turn illustrates (i) independence from age, sex and ethnicity, as well as (ii) equivalence between serum and urine. miR-21, miR-375 and miR-141 copies in cancers were 1.8-fold overexpressed, exhibited zero overlap with healthy samples and had a p-value of 1.6 × 10−22, tentatively validating each miRNA as a multi-cancer biomarker. miR-15b was confirmed to be cancer-independent, whereas let-7b appeared to be a cancer biomarker for prostate and breast cancer, but not for pancreatic cancer. Full article
(This article belongs to the Special Issue Non-coding RNA as Biomarker in Cancer)
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<p>Graphical abstract of all the processes involved in the miRNA measurement using the MinION platform. From left to right: (i) collection of the biospecimen, blood or urine; (ii) isolation of total RNA using a commercial kit; (iii) measurement of total RNA in the isolate using a DeNovix DS-11 spectrophotometer; and (iv) mixing of an aliquot from the RNA isolate with an aliquot of the probe complementary to the target miRNA, adding ONT buffer and conducting a MinION ion conductance experiment (two experiments running simultaneously, shown here). The experiment measures the ion current (<span class="html-italic">I</span>) in picoamperes (pA) as a function of time (t) in milliseconds (ms). In practice, <span class="html-italic">I</span> is constant at <span class="html-italic">I<sub>o</sub>,</span> which is the open nanopore ion current (<span class="html-italic">I</span><sub>o</sub>). When a single molecule traverses the nanopore, <span class="html-italic">I<sub>o</sub></span> is reduced to a new value, <span class="html-italic">I<sub>r</sub></span>, because the molecule occupies the space that would have been occupied by the electrolyte that produces <span class="html-italic">I<sub>o</sub></span>. Ion current reduction (dip in this platform) lasts for a time, τ; both <span class="html-italic">I<sub>r</sub></span> and τ depend on the molecular characteristics. The data were stored automatically as a <span class="html-italic">fast5</span> file, which was subsequently analyzed by <span class="html-italic">OsBp_detect</span> (see <a href="#sec3dot5-ncrna-10-00042" class="html-sec">Section 3.5</a>.). The analysis determines whether the free probe is in excess and detected (left on the scheme above) or if the probe is not detected because it is hybridized with the target (right on the scheme above). Notably, RNAs, including the target miRNA, traverse much faster than the probes, and they are not detected (bottom on the scheme above) due to the relatively slow acquisition rate of this platform.</p>
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<p>Data from <a href="#ncrna-10-00042-t004" class="html-table">Table 4</a>, miRNAs per individual. miRNA copies were normalized to 16 ng/μL RNA content and then divided by the corresponding miRNA copy number in the H6914 1st lot. This double normalization yields level 1.00 for all 4 miRNAs measured in the H6914 1st lot (data not included in tables or figure). The rectangle across samples with a <span class="html-italic">y</span>-axis ranging from 0.8 to 1.2 (average HL = 1.00 and RSD = 0.2) includes 87% of the healthy data. The vertical dashed line separates cancer samples from healthy samples, whereas the horizontal dotted line at 1.5 HL is the threshold that discriminates healthy samples from samples with elevated levels of miR-21, miR-375 and miR-141, with a <span class="html-italic">p</span>-value of 1.6 × 10<sup>−22</sup> (<span class="html-italic">t</span>-test, two samples assuming equal variances). This unprecedented discrimination is between the group of 28 data from cancer samples (left upper segment) and the group of 24 data—excluding miR-15b —from healthy samples (right lower segment). The three cancer miRNA biomarkers exhibit average = 1.83 HL with RSD = 0.09 for the cancer samples and an average = 1.04 HL with RSD = 0.17 for the healthy samples marking an overexpression of 1.8-fold, which is perhaps too low for any PCR-type platform to measure accurately. The miR-15b data for all samples exhibit an average = 0.96 HL with RSD = 0.14. <a href="#ncrna-10-00042-f002" class="html-fig">Figure 2</a> illustrates that the test exhibits no data overlap, i.e., sensitivity, specificity, PPV and NPV at 1.0 for each cancer miRNA biomarker including the miR-375 + miR-141 pair. The data tentatively validate each miRNA (miR-21, miR-375 and miR-141) as a cancer biomarker for all three—breast, prostate and pancreatic—cancers. Additional samples are required to confirm validation. Outliers in <a href="#ncrna-10-00042-f002" class="html-fig">Figure 2</a> would show up as data from healthy samples that appear on the right upper segment and data from cancer samples measuring cancer biomarkers would appear in the lower left segment. We have not observed any outliers yet, but one expects that more data will eventually lead to the observation of outliers. The miR-15b data (blue circles in <a href="#ncrna-10-00042-f002" class="html-fig">Figure 2</a>, 15 subjects) for both cancer and healthy samples illustrate that a non-cancer biomarker, such as miR-15b, clearly appears independent of the sample in this analytical platform.</p>
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<p>Examples of Yenos tests targeting let-7b taken from <a href="#ncrna-10-00042-t003" class="html-table">Table 3</a>. (<b>Top</b>) illustrate detection experiments and (<b>bottom</b>) illustrate silencing experiments. Each figure shows a test, that is, a set of three experiments with a buffer (blue), followed by the 1st run of the sample, which is a mixture of RNA with the probe (red), followed by a 2nd run of the same sample. All three experiments were conducted at −180 mV for 45 min. Analysis of the events by <span class="html-italic">OsBp_detect</span> typically yielded two maxima: one early <span class="html-italic">Ir/Io</span> = 0.15 and a late <span class="html-italic">Ir/Io</span> = 0.3. As shown, the buffer alone exhibited events at both maxima, but the Yenos probes traversed only at <span class="html-italic">Ir/Io</span> = 0.15. Therefore, the presence of a free probe is consistent with an increase in the early <span class="html-italic">Ir/Io</span> peak and/or a decrease in the late <span class="html-italic">Ir/Io</span> peak because there is a steady decrease in events due to the inactivation of the nanopores. Silencing experiments (bottom) often exhibit a markedly reduced number of events due to nanopore “shielding”, as discussed in <a href="#sec3dot6-ncrna-10-00042" class="html-sec">Section 3.6</a> below, while detection experiments (top) exhibit comparable counts but a reversed distribution, with relatively more events at the early <span class="html-italic">(I<sub>r</sub></span>/<span class="html-italic">I<sub>o</sub>)<sub>max</sub></span> = 0.15 and fewer events at the late <span class="html-italic">(I<sub>r</sub></span>/<span class="html-italic">I<sub>o</sub>)<sub>max</sub></span> = 0.30. For a specific experiment with an x μL probe and y μL of sample RNA, probe molecules P = x μL × (probe concentration in fM) × 600. If the experiment involved detection, then P &gt; target miRNA molecules within the y μL aliquot. If the experiment involved silencing, then P &lt; target miRNA molecules in y μL. It follows that the number of miRNA molecules per 1 μL of isolated RNA sample &lt; or &gt;P/y, depending on the experimental outcome.</p>
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<p>Examples of Yenos tests targeting miR-375 and miR-21 taken from <a href="#ncrna-10-00042-t004" class="html-table">Table 4</a>. (<b>Top</b>) illustrate detection experiments and (<b>bottom</b>) illustrate silencing experiments. For additional information, see the caption of <a href="#ncrna-10-00042-f003" class="html-fig">Figure 3</a>.</p>
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31 pages, 827 KiB  
Review
Hunting Metabolic Biomarkers for Exposure to Per- and Polyfluoroalkyl Substances: A Review
by Xue Ma, Delei Cai, Qing Chen, Zhoujing Zhu, Shixin Zhang, Ziyu Wang, Zhengyan Hu, Haitao Shen and Zhen Meng
Metabolites 2024, 14(7), 392; https://doi.org/10.3390/metabo14070392 - 19 Jul 2024
Viewed by 612
Abstract
Per- and polyfluoroalkyl substances (PFAS) represent a class of persistent synthetic chemicals extensively utilized across industrial and consumer sectors, raising substantial environmental and human health concerns. Epidemiological investigations have robustly linked PFAS exposure to a spectrum of adverse health outcomes. Altered metabolites stand [...] Read more.
Per- and polyfluoroalkyl substances (PFAS) represent a class of persistent synthetic chemicals extensively utilized across industrial and consumer sectors, raising substantial environmental and human health concerns. Epidemiological investigations have robustly linked PFAS exposure to a spectrum of adverse health outcomes. Altered metabolites stand as promising biomarkers, offering insights into the identification of specific environmental pollutants and their deleterious impacts on human health. However, elucidating metabolic alterations attributable to PFAS exposure and their ensuing health effects has remained challenging. In light of this, this review aims to elucidate potential biomarkers of PFAS exposure by presenting a comprehensive overview of recent metabolomics-based studies exploring PFAS toxicity. Details of PFAS types, sources, and human exposure patterns are provided. Furthermore, insights into PFAS-induced liver toxicity, reproductive and developmental toxicity, cardiovascular toxicity, glucose homeostasis disruption, kidney toxicity, and carcinogenesis are synthesized. Additionally, a thorough examination of studies utilizing metabolomics to delineate PFAS exposure and toxicity biomarkers across blood, liver, and urine specimens is presented. This review endeavors to advance our understanding of PFAS biomarkers regarding exposure and associated toxicological effects. Full article
(This article belongs to the Special Issue Effects of Environmental Exposure on Host and Microbial Metabolism)
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<p>PFAS primarily metabolic toxicity and biomarkers.</p>
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12 pages, 2991 KiB  
Article
An Efficient Bio-Receptor Layer Combined with a Plasmonic Plastic Optical Fiber Probe for Cortisol Detection in Saliva
by Francesco Arcadio, Mimimorena Seggio, Rosalba Pitruzzella, Luigi Zeni, Alessandra Maria Bossi and Nunzio Cennamo
Biosensors 2024, 14(7), 351; https://doi.org/10.3390/bios14070351 - 19 Jul 2024
Viewed by 569
Abstract
Cortisol is a clinically validated stress biomarker that takes part in many physiological and psychological functions related to the body’s response to stress factors. In particular, it has emerged as a pivotal tool for understanding stress levels and overall well-being. Usually, in clinics, [...] Read more.
Cortisol is a clinically validated stress biomarker that takes part in many physiological and psychological functions related to the body’s response to stress factors. In particular, it has emerged as a pivotal tool for understanding stress levels and overall well-being. Usually, in clinics, cortisol levels are monitored in blood or urine, but significant changes are also registered in sweat and saliva. In this work, a surface plasmon resonance probe based on a D-shaped plastic optical fiber was functionalized with a glucocorticoid receptor exploited as a highly efficient bioreceptor specific to cortisol. The developed plastic optical fiber biosensor was tested for cortisol detection in buffer and artificial saliva. The biosensor response showed very good selectivity towards other hormones and a detection limit of about 59 fM and 96 fM in phosphate saline buffer and artificial saliva, respectively. The obtained detection limit, with a rapid detection time (about 5 min) and a low-cost sensor system, paved the way for determining the cortisol concentration in saliva samples without any extraction process or sample pretreatment via a point-of-care test. Full article
(This article belongs to the Special Issue Plasmonic Biosensors for Biomedical Applications)
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<p>Scheme of the experimental setup employed to test the GR-SPR-POF biosensor.</p>
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<p>(<b>A</b>) Scheme of the functionalization protocol used. (<b>B</b>) SPR spectra attained by using PBS as bulk solution after each step of the immobilization procedure. (<b>C</b>) Variation in resonance wavelength (Δ<span class="html-italic">λ</span>) computed with respect to the SPR wavelength obtained on the non-functionalized chip.</p>
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<p>GR–cortisol binding tests in PBS. (<b>A</b>) SPR spectra, smoothed and translated along the y-axis direction, obtained in PBS at increasing cortisol concentrations. (<b>B</b>) Result of the signal processing performed to determine the resonance wavelengths useful to obtain the dose–response curve in PBS.</p>
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<p>GR–cortisol binding test in artificial saliva diluted 1:50 with PBS. (<b>A</b>) SPR spectra obtained in diluted artificial saliva at increasing cortisol concentrations. (<b>B</b>) Result of the signal processing performed to determine the resonance wavelength useful to obtain the dose–response curve in artificial saliva (diluted 1:50).</p>
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<p>Dose–response curves obtained through cortisol monitoring in (<b>A</b>) PBS and (<b>B</b>) artificial saliva diluted 1:50. The Δ<span class="html-italic">λ</span> absolute values (computed in relation to the blank) as a function of increasing cortisol concentrations on the GR-SPR-POF platform are reported in a semi-log scale, along with Langmuir fitting of the experimental data.</p>
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<p>(<b>A</b>) Resonance wavelength variation for the structural analogues of cortisol (estradiol 100 pM and progesterone 100 pM) and cortisol (10 pM). One-way ANOVA * <span class="html-italic">p</span> &lt; 0.01 vs. estradiol and <sup>#</sup> <span class="html-italic">p</span> &lt; 0.01 vs. progesterone. (<b>B</b>) Comparison between the resonance wavelength variation achieved by a solution with cortisol only (10 pM) and with cortisol pooled in a mixture with progesterone and estradiol, each of which was considered at a concentration of 10 pM.</p>
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