So you think you can PLS-DA?

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications

Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimensional (1D) NMR spectrum could limit the accuracy of quantitative analysis for metabolomics applications. Two-dimensional (2D) NMR has been applied to solve the 1D NMR overlap problem, but the data processing is still challenging. In this study, we built an automatic approach to process the 2D NMR data for quantitative applications using machine learning approaches. Partial least square discriminant analysis (PLS-DA), artificial neural network classification (ANN-DA), gradient boosted trees classification (XGBoost-DA), and artificial deep learning neural network classification (ANNDL-DA) were applied in combination with an automatic peak selection approach. Standard mixtures, sea anemone extracts, and mouse fecal samples were tested to demonstrate the approach. Our results showed that ANN-DA and ANNDL-DA have high accuracy in selecting 2D NMR peaks (around 90 %), which have a high potential application in 2D NMR-based metabolomics quantitively study, while PLS-DA and XGBoost-DA showed limitations in either data variation or overfitting. Our study built an automatic approach to applying 2D NMR data to routine quantitative analysis in metabolomics.

Chemical profiling of paper recycling grades using GC-MS and LC-MS: An exploration of contaminants and their possible sources

Paper packaging made with recycled paperboard is used to pack various consumer goods that can include amongst others, electronics, toys, food, cosmetics, and stationery. Chemical profiling of the various paper recycling grades used in the manufacture of recycled paperboard was undertaken to investigate possible sources of contaminants and their propagation in the paper recycling chain. Pre-consumer, retail and post-consumer paper-based materials were collected at papermills, corrugators, grocery stores, household waste, solid waste disposal sites and recycling facilities. In the GC-MS analysis, phthalates, long-chain aliphatic compounds, and fatty acids were the most commonly detected compounds whilst phthalates and bisphenols featured most prevalently in the LC-MS analysis. The factors that were identified as likely contributors to the detection of the different chemical compounds included the presence of wood derivatives, the use of certain chemical additives during manufacturing, and exposure of paper to contaminants from consumers, other goods and the environment. Waste mingling, recovery, sorting and reprocessing into recycled paper were also shown to influence the chemical profile of paper materials. Sparse partial least squares-discriminate analysis indicated that newspaper and office paper had unique chemical constituents, whilst cartons were shown to have higher variability. By looking at key stages of paper recycling, this study showed that the possible persistence and transformation of chemical compounds in additives must be evaluated when considering the recyclability of paper-based materials. Further, it highlighted that different separation approaches may be required to reduce contaminant exposure opportunities in post-consumer paper materials.

Array-optimized artificial olfactory sensor enabling cost-effective and non-destructive detection of mycotoxin-contaminated maize

The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.

Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer

Metabolomics has emerged as a powerful tool for identifying biomarkers of disease, and nuclear magnetic resonance (NMR) spectroscopy allows for the simultaneous detection of a wide range of metabolites. However, due to complex interactions within metabolic networks, metabolites often exhibit high correlation and collinearity. To address this challenge, self-organizing maps (SOMs) of Kohonen maps and counter propagation-artificial neural networks (CP-ANN) were employed in this study to model proton nuclear magnetic resonance spectroscopic (1HNMR) data from control samples and breast cancer (BC) patients. Blood serum samples from a control group (n=24) and BC patients (n=18) were used to extract metabolites using methanol and chloroform solvents in optimum extraction conditions. The 1HNMR data was preprocessed by performing phase, baseline, and shift corrections. Subsequently, the preprocessed data was modeled using Kohonen network as an unsupervised technique and CP-ANN as a supervised technique. In this regard, the model built with CP-ANN successfully distinguished between the two classes with an accuracy of 100 % for both group and sensitivity of 96 % and 100 % for control group and BC patients, respectively. Additionally, CP-ANN algorithm demonstrated predictive capabilities by accurately classifying test samples with 90 % sensitivity, 98 % specificity, and 96 % accuracy for control group and 100 % sensitivity, 90 % specificity, and 96 % accuracy for BC patients. Furthermore, analysis of the resulting topological map revealed 14 significant variables (biomarkers) such as sarcosine, lysine, trehalose, tryptophan, and betaine that effectively differentiated between healthy individuals and BC patients.

Identifying Process Differences with ToF-SIMS: An MVA Decomposition Strategy

In time-of-flight secondary ion mass spectrometry (ToF-SIMS), multivariate analysis (MVA) methods such as principal component analysis (PCA) are routinely employed to differentiate spectra. However, additional insights can often be gained by comparing processes, where each process is characterized by its own start and end spectra, such as when identical samples undergo slightly different treatments or when slightly different samples receive the same treatment. This study proposes a strategy to compare such processes by decomposing the loading vectors associated with them, which highlights differences in the relative behavior of the peaks. This strategy identifies key information beyond what is captured by the loading vectors or the end spectra alone. While PCA is widely used, partial least-squares discriminant analysis (PLS-DA) serves as a supervised alternative and is the preferred method for deriving process-related loading vectors when classes are narrowly separated. The effectiveness of the decomposition strategy is demonstrated using artificial spectra and applied to a ToF-SIMS materials science case study on the photodegradation of N719 dye, a common dye in photovoltaics, on a mesoporous TiO2 anode. The study revealed that the photodegradation process varies over time, and the resulting fragments have been identified accordingly. The proposed methodology, applicable to both labeled (supervised) and unlabeled (unsupervised) spectral data, can be seamlessly integrated into most modern mass spectrometry data analysis workflows to automatically generate a list of peaks whose relative behavior varies between two processes, and is particularly effective in identifying subtle differences between highly similar physicochemical processes.

Relevance of plasma lipoproteins and small metabolites in assessment of nutritional status among patients with severe injuries

Background

This study aimed to identify plasma lipoproteins and small metabolites associated with high risk of malnutrition during intensive care unit (ICU) stay in patients with severe injuries.

Methods

This observational prospective exploratory study was conducted at two level-1 trauma centers in the Netherlands. Adult patients (aged ≥18 years) who were admitted to the ICU for more than 48 h between July 2018 and April 2022 owing to severe injuries (polytrauma, as defined by Injury Severity Scores of ≥16) caused by blunt trauma were eligible for inclusion. Partial least squares discriminant analysis was used to analyze the relationship of 112 lipoprotein-related components and 23 small metabolites with the risk of malnutrition (modified Nutrition Risk in Critically Ill score). Malnutrition was diagnosed based on Subjective Global Assessment scores. The relationship of lipoprotein properties and small metabolite concentrations with malnutrition (during ICU admission) was evaluated using mixed effects logistic regression.

Results

Overall, 51 patients were included. Lower (very) low-density lipoprotein ([V]LDL) (free) cholesterol and phospholipid levels, low particle number, and higher levels of LDL triglycerides were associated with a higher risk of malnutrition (variable importance in projection [VIP] value >1.5). Low levels of most (V)LDL and intermediate-density lipoprotein subfractions and high levels of high-density lipoprotein Apo-A1 were associated with the diagnosis of malnutrition (VIP value >1.5). Increased levels of dimethyl sulfone, trimethylamine N-oxide, creatinine, N, N-dimethylglycine, and pyruvic acid and decreased levels of creatine, methionine, and acetoacetic acid were also indicative of malnutrition (VIP value >1.5). Overall, 14 lipoproteins and 1 small metabolite were significantly associated with a high risk of malnutrition during ICU admission (P <0.05); however, the association did not persist after correcting the false discovery rate (P=0.35 for all).

Conclusion

Increased triglyceride in several lipoprotein subfractions and decreased levels of other lipoprotein subfraction lipids and several small metabolites (involved in the homocysteine cycle, ketone body formation, and muscle metabolism) may be indicative of malnutrition risk. Following validation in larger cohorts, these indicators may guide institution of preventive nutritional measures in patients admitted to the ICU with severe injuries.

Antioxidant activity of the main phenolics found in red fruits: An in vitro and in silico study

The current study analysed the antioxidant capacity of the main phenolics found in red fruits. In total, there were analysed the antioxidant activity against 1,1-diphenyl-2-picrylhydrazyl radical, nitric oxide and superoxide radicals (DPPH, NO and O2-, respectively) of 23 phenolics. Regarding DPPH, anthocyanins, (-)-epicatechin and kaempferol 3-O-rutinoside were the most active, while isorhamnetin 3-O-glucoside was the least active. Anthocyanins, (-)-epicatechin, quercetin 3-O-glucoside and caffeic acid showed the strongest potential against NO, while ρ-hydroxybenzoic acid was the less efficient. Regarding the O2- assay, quercetin aglycone and their derivatives were the best ones, while cyanidin aglycone did not show any potential to quench this radical. To deeper explore the biological potential of the most promising compounds, docking molecular and ADME studies were also done. The obtained data is another support regarding the biological potential of phenolics and might be useful in encouraging their use and incorporation in new products.

Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection's long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. Samples were taken from a cohort of 142 COVID-19, 48 Post-COVID-19, and 38 control patients, comprising 111 identified metabolites. Traditional analysis methods, like PCA and PLS-DA, were compared with ML techniques, particularly eXtreme Gradient Boosting (XGBoost) enhanced by SHAP (SHapley Additive exPlanations) values for explainability. XGBoost, combined with SHAP, outperformed traditional methods, demonstrating superior predictive performance and providing new insights into the metabolic basis of the disease's progression and aftermath. The analysis revealed metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. Key metabolic signatures in Post-COVID-19 include taurine, glutamine, alpha-Ketoglutaric acid, and LysoPC a C16:0. This study highlights the potential of integrating ML and XAI for a fine-grained description in metabolomics research, offering a more detailed understanding of metabolic anomalies in COVID-19 and Post-COVID-19 conditions.

The Content of Volatile Organic Compounds in Calypogeia suecica (Calypogeiaceae, Marchantiophyta) Confirms Genetic Differentiation of This Liverwort Species into Two Groups

Calypogeia is a genus of liverworts in the family Calypogeiaceae. The subject of this study was Calypogeia suecica. Samples of the liverwort Calypogeia suecica were collected from various places in southern Poland. A total of 25 samples were collected in 2021, and 25 samples were collected in 2022. Volatile organic compounds (VOCs) from liverworts were analyzed by gas chromatography-mass spectrometry (GC-MS). A total of 107 compounds were detected, of which 38 compounds were identified. The identified compounds were dominated by compounds from the sesquiterpene group (up to 34.77%) and sesquiterpenoids (up to 48.24%). The tested samples of Calypogeia suecica also contained compounds belonging the aromatic classification (up to 5.46%), aliphatic hydrocarbons (up to 1.66%), and small amounts of monoterpenes (up to 0.17%) and monoterpenoids (up to 0.30%). Due to the observed differences in the composition of VOCs, the tested plant material was divided into two groups, in accordance with genetic diversity.

Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk

Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In this study, a non-specific optical sensor array was proposed for the identification of multiple pathogenic bacteria in contaminated milk samples. Fluorescence-labeled single-stranded DNA was efficiently quenched by two-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial species, enabling the sensor array to identify eight bacteria (pathogenic and spoilage) within a few hours. Four traditional machine learning models and two artificial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubation to 120 min increased the accuracy of the multiplayer perceptron to 98.4 %. This sensor array is a novel, low-cost, and high-accuracy approach for the identification of multiple bacteria, providing an alternative to plate counting and ELISA methods.

Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

IMPORTANCE

We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

CO2 Breathing Prior to Simulated Diving Increases Decompression Sickness Risk in a Mouse Model: The Microbiota Trail Is Not Forgotten

Decompression sickness (DCS) with neurological disorders is the leading cause of major diving accidents treated in hyperbaric chambers. Exposure to high levels of CO2 during diving is a safety concern for occupational groups at risk of DCS. However, the effects of prior exposure to CO2 have never been evaluated. The purpose of this study was to evaluate the effect of CO2 breathing prior to a provocative dive on the occurrence of DCS in mice. Fifty mice were exposed to a maximum CO2 concentration of 70 hPa, i.e., 7% at atmospheric pressure, for one hour at atmospheric pressure. Another 50 mice breathing air under similar conditions served as controls. In the AIR group (control), 22 out of 50 mice showed post-dive symptoms compared to 44 out of 50 in the CO2 group (p < 0.001). We found that CO2 breathing is associated with a decrease in body temperature in mice and that CO2 exposure dramatically increases the incidence of DCS (p < 0.001). More unexpectedly, it appears that the lower temperature of the animals even before exposure to the accident-prone protocol leads to an unfavorable prognosis (p = 0.046). This study also suggests that the composition of the microbiota may influence thermogenesis and thus accidentology. Depending on prior exposure, some of the bacterial genera identified in this work could be perceived as beneficial or pathogenic.

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.

Exposure to PFAS contaminated urban wetland water causes similar metabolic alterations to laboratory-based exposures in the freshwater amphipod Austrochiltonia subtenuis

Assessing the harm caused by pollutants in urban ecosystems remains a significant challenge. Traditional ecotoxicological endpoints are often not sensitive enough to detect the effects of toxicants at environmentally relevant concentrations (≤ng/L). A potential solution is using molecular biology methods to look at small biochemical changes caused by exposure to ng/L concentrations of contaminants. This has been tested in the lab but not conclusively demonstrated in the field. We exposed the freshwater amphipod (Austrochiltonia subtenuis) to water from an urban wetland containing known concentrations of per-and polyfluoroalkyl substances (as well as very low concentrations of pesticides) for 14 days and analyzed their metabolite profiles. Mannose, Myo-inositol, and Isopropyl propionate were found to change in PFAS exposed amphipods, a similar response to that previously observed in laboratory exposures to the same PFAS, but not pesticides. The results give a better understanding of PFAS toxicity at environmentally relevant concentrations and conditions.

1H NMR spectroscopy as a tool to probe potential biomarkers of the drying-salting process: A proof-of-concept study with the Amazon fish pirarucu

Dry-salted pirarucu (Arapaima gigas) plays an important cultural role in the Amazon region - South America. In this study, we explored the changes in the chemical composition of pirarucu meat following the drying-salting process via 1H NMR spectroscopy. Combining multivariate and univariate statistical analyses yielded a robust differentiation of metabolites involved in the process. VIP score (>1), p-value (<0.05), and AUC (>0.7) were considered to selecting compounds that had significant fluctuations in their contents along the process. Our results pointed out acetate, lactate, succinate, and creatinine as metabolites undergoing significant changes during the drying-salting process. Creatinine was not detected in fresh samples. The investigation of multiple components delves deeper into the molecular nuances of the salting-drying process's impact on fish meat, providing a more comprehensive understanding of the possible chemical transformations and how the matrix's quality control and nutritional aspects should be addressed.

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Essential Oils of Laurus nobilis L.: From Chemical Analysis to In Silico Investigation of Anti-Inflammatory Activity by Soluble Epoxide Hydrolase (sEH) Inhibition

Laurus nobilis L. is commonly used in folk medicine in the form of infusion or decoction to treat gastrointestinal diseases and flatulence as a carminative, antiseptic, and anti-inflammatory agent. In this study, the essential oil (EO) composition of wild-grown L. nobilis L. leaves collected from seven different altitudinal locations in the Molise region and adjacent regions (Abruzzo and Campania) was investigated. EOs from the leaves were obtained by hydrodistillation and analyzed by GC-FID and GC/MS, and 78 compounds were identified. The major oil components were 1,8-cineol (43.52-31.31%), methyl-eugenol (14.96-4.07%), α-terpinyl acetate (13.00-8.51%), linalool (11.72-1.08%), sabinene (10.57-4.85%), α-pinene (7.41-3.61%), eugenol (4.12-1.97%), and terpinen-4-ol (2.33-1.25%). Chemometric techniques have been applied to compare the chemical composition. To shed light on the nutraceutical properties of the main hydrophobic secondary metabolites (≥1.0%) of laurel EOs, we assessed the in vitro antioxidant activities based on 2,2-diphenyl-1-picrylhydrazyl (DPPH•) radical scavenging activity and the reducing antioxidant power by using a ferric reducing power (FRAP) assay. Furthermore, we highlighted the anti-inflammatory effects of seven EOs able to interfere with the enzyme soluble epoxide hydrolase (sEH), a key enzyme in the arachidonic acid cascade, in concentrations ranging from 16.5 ± 4.3 to 8062.3 ± 580.9 mg/mL. Thanks to in silico studies, we investigated and rationalized the observed anti-inflammatory properties, ascribing the inhibitory activity toward the disclosed target to the most abundant volatile phytochemicals (≥1.0%) of seven EOs.

Elemental analysis and metabolic profiling of medicinally potent members of Zingiberaceae family using FT-IR and LIBS coupled with PLS-DA

The role of organic and inorganic elemental profiles in the growth, development, and secondary metabolite synthesis of plants is crucial, particularly concerning their medicinal value. However, comprehensive studies addressing both aspects are scarce. Hence, the present manuscript aims to investigate the potential use of Fourier transform infrared spectroscopy (FT-IR) and laser-induced breakdown spectroscopy (LIBS) techniques to obtain the functional groups and organic and inorganic elemental profiles of significant medicinal plants belonging to the Zingiberaceae family collected from two different geographic regions in India. The FT-IR analysis of the methanolic extracts shows the presence of aliphatic and aromatic alcohols, esters, ethers, carboxyl compounds, and their derivatives. In LIBS analysis, the spectral characteristics of atomic and molecular species present in the samples were observed, encompassing both organic and inorganic elements. The presence of heavy metals and trace elements have also been observed in the LIBS spectra of the samples. Furthermore, partial least squares discriminant analysis (PLS-DA) has been used to obtain classification pattern of the samples based on their spectral fingerprints. This study not only helps in reflecting the significance of micronutrients in aiding secondary metabolism thus enhancing the medicinal properties of plants, but also enables the identification of trace elements within plants. This facilitates the determination of the suitable usage and dosage of particular plant components, contributing to the research goal of establishing pharmacological and nutraceutical significance. This study is imperative as it fills a critical gap in research, although further work in this direction is warranted.

Effect of chemically synthesized psilocybin and psychedelic mushroom extract on molecular and metabolic profiles in mouse brain

Psilocybin, a naturally occurring, tryptamine alkaloid prodrug, is currently being investigated for the treatment of a range of psychiatric disorders. Preclinical reports suggest that the biological effects of psilocybin-containing mushroom extract or "full spectrum" (psychedelic) mushroom extract (PME), may differ from those of chemically synthesized psilocybin (PSIL). We compared the effects of PME to those of PSIL on the head twitch response (HTR), neuroplasticity-related synaptic proteins and frontal cortex metabolomic profiles in male C57Bl/6j mice. HTR measurement showed similar effects of PSIL and PME over 20 min. Brain specimens (frontal cortex, hippocampus, amygdala, striatum) were assayed for the synaptic proteins, GAP43, PSD95, synaptophysin and SV2A, using western blots. These proteins may serve as indicators of synaptic plasticity. Three days after treatment, there was minimal increase in synaptic proteins. After 11 days, PSIL and PME significantly increased GAP43 in the frontal cortex (p = 0.019; p = 0.039 respectively) and hippocampus (p = 0.015; p = 0.027) and synaptophysin in the hippocampus (p = 0.041; p = 0.05) and amygdala (p = 0.035; p = 0.004). PSIL increased SV2A in the amygdala (p = 0.036) and PME did so in the hippocampus (p = 0.014). In the striatum, synaptophysin was increased by PME only (p = 0.023). There were no significant effects of PSIL or PME on PSD95 in any brain area when these were analyzed separately. Nested analysis of variance (ANOVA) showed a significant increase in each of the 4 proteins over all brain areas for PME versus vehicle control, while significant PSIL effects were observed only in the hippocampus and amygdala and were limited to PSD95 and SV2A. Metabolomic analyses of the pre-frontal cortex were performed by untargeted polar metabolomics utilizing capillary electrophoresis - Fourier transform mass spectrometry (CE-FTMS) and showed a differential metabolic separation between PME and vehicle groups. The purines guanosine, hypoxanthine and inosine, associated with oxidative stress and energy production pathways, showed a progressive decline from VEH to PSIL to PME. In conclusion, our synaptic protein findings suggest that PME has a more potent and prolonged effect on synaptic plasticity than PSIL. Our metabolomics data support a gradient of effects from inert vehicle via chemical psilocybin to PME further supporting differential effects. Further studies are needed to confirm and extend these findings and to identify the molecules that may be responsible for the enhanced effects of PME as compared to psilocybin alone.

Dietary patterns in mild cognitive impairment and dementia in older adults from Yucatan, Mexico

Background

Some dietary patterns and dietary components have an important role in preventing and helping to improve patients' quality of life of individuals with Mild Cognitive Impairment (MCI) and dementia. In Mexico, it is unknown what the dietary patterns are among older adults with MCI and dementia. We aimed to identify the dietary patterns of older adults with MCI and dementia living in Yucatan, Mexico.

Methods

A cross-sectional study was carried out among 39 patients as controls and 34 individuals as cases (MCI and dementia). A food frequency questionnaire collected diet information, anthropometric and clinical parameters, and lifestyle characteristics. The dietary patterns were evaluated through Partial Least-Squares Discriminant Analysis (PLS-DA).

Results

The food groups that showed discrimination between groups and were classified into the dietary patterns of MCI and dementia individuals were "pastries and cookies," "soups," and "legumes." The dietary pattern of older adults without cognitive impairment was characterized by "nuts and seeds," "candies," "vegetables," "coffee and tea," and "water." The consumption of "pastries and cookies" showed an increasing correlation with serum insulin levels (r = 0.36, p = 0.01), and "soups" showed an inverse correlation with total cholesterol levels (r = -0.36, p = 0.02) in patients with MCI and dementia. In controls, there is a positive correlation between the consumption of "nuts and seeds" (r = 0.333, p = 0.01) and "vegetables" (r = 0.32, p = 0.02) with levels of urea; "coffee and tea" showed a positive association with levels of insulin (r = 0.378, p = 0.05).

Conclusion

The dietary pattern of individuals with MCI and dementia has some nutritional deficiencies. Including an adequate intake of vegetables, fruits, and protein could improve the quality of life of subjects living with these conditions in Yucatan, Mexico.

Distinct cytokine profiles in late pregnancy in Ugandan people with HIV

During pregnancy, multiple immune regulatory mechanisms establish an immune-tolerant environment for the allogeneic fetus, including cellular signals called cytokines that modify immune responses. However, the impact of maternal HIV infection on these responses is incompletely characterized. We analyzed paired maternal and umbilical cord plasma collected during labor from 147 people with HIV taking antiretroviral therapy and 142 HIV-uninfected comparators. Though cytokine concentrations were overall similar between groups, using Partial Least Squares Discriminant Analysis we identified distinct cytokine profiles in each group, driven by higher IL-5 and lower IL-8 and MIP-1α levels in pregnant people with HIV and higher RANTES and E-selectin in HIV-unexposed umbilical cord plasma (P-value < 0.01). Furthermore, maternal RANTES, SDF-α, gro α -KC, IL-6, and IP-10 levels differed significantly by HIV serostatus (P < 0.01). Although global maternal and umbilical cord cytokine profiles differed significantly (P < 0.01), umbilical cord plasma profiles were similar by maternal HIV serostatus. We demonstrate that HIV infection is associated with a distinct maternal plasma cytokine profile which is not transferred across the placenta, indicating a placental role in coordinating local inflammatory response. Furthermore, maternal cytokine profiles in people with HIV suggest an incomplete shift from Th2 to Th1 immune phenotype at the end of pregnancy.

Comparison of the diagnostic effectiveness of ultrasound imaging coupled with three mathematical models for discriminating thyroid nodules

BACKGROUND

The overlapping nature of thyroid lesions visualized on ultrasound (US) images could result in misdiagnosis and missed diagnoses in clinical practice.

PURPOSE

To compare the diagnostic effectiveness of US coupled with three mathematical models, namely logistic regression (Logistics), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM), in discriminating between malignant and benign thyroid nodules.

MATERIAL AND METHODS

A total of 588 thyroid nodules (287 benign and 301 malignant) were collected, among which 80% were utilized for constructing the mathematical models and the remaining 20% were used for internal validation. In addition, an external validation cohort comprising 160 nodules (80 benign and 80 malignant) was employed to validate the accuracy of these mathematical models.

RESULTS

Our study demonstrated that all three models exhibited effective predictive capabilities for distinguishing between benign and malignant nodules, whose diagnostic effectiveness surpassed that of the TI-RADS classification, particularly in terms of true negative diagnoses. SVM achieved a higher diagnostic rate for malignant thyroid nodules (93.8%) compared to Logistics (91.5%) and PLS-DA (91.6%). PLS-DA exhibited higher diagnostic rates for benign thyroid nodules (91.9%) compared to Logistics (86.7%) and SVM (88.7%). Both the area under the receiver operating characteristic curve (AUC) values of PLS-DA (0.917) and SVM (0.913) were higher than that of Logistics (0.891).

CONCLUSION

Our findings indicate that SVM had significantly higher rates of true positive diagnoses and PLS-DA exhibited significantly higher rates of true negative diagnoses. All three models outperformed the TI-RADS classification in discriminating between malignant and benign thyroid nodules.

Integrating Targeted Metabolomics and Targeted Proteomics to Study the Responses of Wheat Plants to Engineered Nanomaterials

This manuscript presents a multiomics investigation into the metabolic and proteomic responses of wheat to molybdenum (Mo)- and copper (Cu)-based engineered nanomaterials (ENMs) exposure via root and leaf application methods. Wheat plants underwent a four-week growth period with a 16 h photoperiod (light intensity set at 150 μmol·m-2·s-1), at 22 °C and 60% humidity. Six distinct treatments were applied, including control conditions alongside exposure to Mo- and Cu-based ENMs through both root and leaf routes. The exposure dosage amounted to 6.25 mg of the respective element per plant. An additional treatment with a lower dose (0.6 mg Mo/plant) of Mo ENM exclusively through the root system was introduced upon the detection of phytotoxicity. Utilizing LC-MS/MS analysis, 82 metabolites across various classes and 24 proteins were assessed in different plant tissues (roots, stems, leaves) under diverse treatments. The investigation identified 58 responsive metabolites and 19 responsive proteins for Cu treatments, 71 responsive metabolites, and 24 responsive proteins for Mo treatments, mostly through leaf exposure for Cu and root exposure for Mo. Distinct tissue-specific preferences for metabolite accumulation were revealed, highlighting the prevalence of organic acids and fatty acids in stem or root tissues, while sugars and amino acids were abundant in leaves, mirroring their roles in energy storage and photosynthesis. Joint-pathway analysis was conducted and unveiled 23 perturbed pathways across treatments. Among these, Mo exposure via roots impacted all identified pathways, whereas exposure via leaf affected 15 pathways, underscoring the reliance on exposure route of metabolic and proteomic responses. The coordinated response observed in protein and metabolite concentrations, particularly in amino acids, highlighted a dynamic and interconnected proteomic-to-metabolic-to-proteomic relationship. Furthermore, the contrasting expression patterns observed in glutamate dehydrogenase (upregulation at 1.38 ≤ FC ≤ 1.63 with high Mo dose, and downregulation at 0.13 ≤ FC ≤ 0.54 with low Mo dose) and its consequential impact on glutamine expression (7.67 ≤ FC ≤ 39.60 with high Mo dose and 1.50 ≤ FC ≤ 1.95 with low Mo dose) following Mo root exposure highlighted dose-dependent regulatory trends influencing proteins and metabolites. These findings offer a multidimensional understanding of plant responses to ENMs exposure, guiding agricultural practices and environmental safety protocols while advancing knowledge on nanomaterial impacts on plant biology.

Development of non-destructive methods for the assessment of authenticity of sports whey protein supplements

In the category of sports supplements, whey protein powder is one of the popular supplements for muscle building applications. Therefore, verification of the sport supplements as authentic products has become a universal concern. This work aimed to propose vibrational spectroscopy including near infrared (NIR) and infrared (IR) as rapid and non-destructive testing tools for the detection and quantification of maltodextrin, milk powder and milk whey powder in whey protein supplements. Initially, principal component analysis was applied to data for pattern recognition and the results displayed a fine pattern of discrimination. Partial least square discrimination analysis (PLS-DA) and K-nearest neighbours (KNN) were exploited as supervised method modelling classification. This process was done in order to respond to two vital questions whether the sample is adulterated or not and what is the kind of adulteration. PLS-DA showed better classification results rather than KNN according to the figure of merits of the model. Partial least square regression (PLSR) was employed on pre-treated spectra to quantify the amount of adulteration in sport whey supplements. Eventually, it seems vibrational spectroscopy could be implemented as a simple, and low-cost analysis method for the detection and quantification of mentioned adulterants in whey protein supplements.

Diagnosing arsenic-mediated biochemical responses in rice cultivars using Raman spectroscopy

Rice (Oryza sativa) is the primary crop for nearly half of the world's population. Groundwater in many rice-growing parts of the world often has elevated levels of arsenite and arsenate. At the same time, rice can accumulate up to 20 times more arsenic compared to other staple crops. This places an enormous amount of people at risk of chronic arsenic poisoning. In this study, we investigated whether Raman spectroscopy (RS) could be used to diagnose arsenic toxicity in rice based on biochemical changes that were induced by arsenic accumulation. We modeled arsenite and arsenate stresses in four different rice cultivars grown in hydroponics over a nine-day window. Our results demonstrate that Raman spectra acquired from rice leaves, coupled with partial least squares-discriminant analysis, enabled accurate detection and identification of arsenic stress with approximately 89% accuracy. We also performed high-performance liquid chromatography (HPLC)-analysis of rice leaves to identify the key molecular analytes sensed by RS in confirming arsenic poisoning. We found that RS primarily detected a decrease in the concentration of lutein and an increase in the concentration of vanillic and ferulic acids due to the accumulation of arsenite and arsenate in rice. This showed that these molecules are detectable indicators of biochemical response to arsenic accumulation. Finally, a cross-correlation of RS with HPLC and ICP-MS demonstrated RS's potential for a label-free, non-invasive, and non-destructive quantification of arsenic accumulation in rice.

Characteristics of Purified Horse Oil by Supercritical Fluid Extraction with Different Deodorants Agents

This study investigated the impact of activated carbon, palm activated carbon, and zeolite on horse oil (HO) extracted from horse neck fat using supercritical fluid extraction with deodorant-untreated HO (CON) as a comparison. The yield and lipid oxidation of deodorant untreated HO (CON) were not significantly affected by the three deodorants. However, deodorant-treated HOs exhibited significantly elevated levels of α-linolenic acid (C18:3n3) and eicosenoic acid (C20:1n9) compared to CON (p<0.05), while other fatty acids remained consistent. Zeolite-purified HO demonstrated significantly lower levels of volatile organic compounds (VOCs) than other treatments (p<0.05). Remarkably, zeolite decreased the concentration of pentane, 2,3-dimethyl (gasoline odor), by over 90%, from 177.17 A.U. ×106 in CON to 15.91 A.U. ×106. Zeolite also effectively eliminates sec-butylamine (ammonia and fishy odor) as compared to other deodorant-treated HOs (p<0.05). Additionally, zeolite reduced VOCs associated with the fruity citrus flavor, such as nonanal, octanal, and D-limonene in HO (p<0.05). This study suggests that integrating zeolite in supercritical fluid extraction enhances HO purification by effectively eliminating undesirable VOCs, presenting a valuable approach for producing high-quality HO production in the cosmetic and functional food industries.

Investigating the impact of feed-induced, subacute ruminal acidosis on rumen epimural transcriptome and metatranscriptome in young calves at 8- and 17-week of age

Introduction

With the goal to maximize intake of high-fermentable diet needed to meet energy needs during weaning period, calves are at risk for ruminal acidosis. Using the calves from previously established model of feed-induced, ruminal acidosis in young calves, we aimed to investigate the changes in rumen epimural transcriptome and its microbial metatranscriptome at weaning (8-week) and post-weaning (17-week) in canulated (first occurred at 3 weeks of age) Holstein bull calves with feed-induced subacute ruminal acidosis.

Methods

Eight bull calves were randomly assigned to acidosis-inducing diet (Treated, n = 4; pelleted, 42.7% starch, 15.1% neutral detergent fiber [NDF], and 57.8% nonfiber carbohydrates), while texturized starter was fed as a control (Control, n = 4; 35.3% starch, 25.3% NDF, and 48.1% nonfiber carbohydrates) starting at 1 week through 17 weeks. Calves fed acidosis-inducing diet showed significantly less (p < 0.01) body weight over the course of the experiment, in addition to lower ruminal pH (p < 0.01) compared to the control group. Rumen epithelial (RE) tissues were collected at both 8 weeks (via biopsy) and 17 weeks (via euthanasia) and followed for whole transcriptome RNA sequencing analysis. Differentially expressed genes (DEGs) analysis was done using cufflinks2 (fold-change ≥2 and p < 0.05) between treated and control groups at 8-week of age, and between 8- and 17-week for the treated group.

Results

At 8-week of age, DEGs between treatment groups showed an enrichment of genes related to the response to lipopolysaccharide (LPS) (p < 0.005). The impact of prolonged, feed-induced acidosis was reflected by the decreased expression (p < 0.005) in genes involved in cell proliferation related pathways in the RE at 17-week of age in the treated group. Unique sets of discriminant microbial taxa were identified between 8-and 17-week calves in the treated group and the treatment groups at 8-week, indicating that active microbial community changes in the RE are an integral part of the ruminal acidosis development and progression.

Surface-enhanced Raman Spectroscopy in urinalysis of hypertension patients with kidney disease

Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.

Evaluation of Lipid Oxidation Characteristics in Salmon after Simulation of Cold Chain Interruption Using Rapid Evaporation Ionization Mass Spectrometry

Temperature fluctuations occurring during the cold chain logistics of salmon contribute to lipid oxidation. This study aimed to simulate cold chain interruption through freeze-thaw operations and evaluate the lipidomics data from salmon samples subjected to different numbers of freeze-thaw cycles by using rapid evaporative ionization mass spectrometry (REIMS) combined with an intelligent surgical knife (iKnife). The results indicated significant differences in the relative abundance of characteristic ions among the samples (p < 0.05). A total of 34 ions with variable importance for the projection values ≥1 were identified as potential biomarkers, including m/z 719.4233 ([PCC36:5-NH(CH3)3]-), m/z 337.3134 ([FAC22:1]-), m/z 720.4666 ([PEC35:6-H]-), m/z 309.2780 ([FAC20:1]-), m/z 777.4985 ([PCC40:4-NH(CH3)3]-), m/z 745.4421 ([PCC38:6-NH(CH3)3]-/[PEC38:6-NH3]-), m/z 747.4665 ([PCC38:5-NH(CH3)3]-/[PEC38:5-NH3]-), etc. The degree of lipid oxidation was found to be associated with the number of freeze-thaw cycles, exhibiting the most significant alterations in the relative abundance of lipid ions in the 8T samples. Additionally, sensory evaluation by the CIE-L*a*b* method and volatile analysis by headspace solid-phase microextraction gas chromatography-mass spectrometry demonstrated significant differences (p < 0.05) in color and odor among the salmon samples, with a correlation to the number of freeze-thaw cycles. The primary compounds responsible for alterations in salmon odor were aldehydes with lower odor thresholds. In summary, the iKnife-REIMS method accurately differentiated salmon muscle tissues based on varying levels of lipid oxidation, thus expanding the application of REIMS.

Assessing the Impacts of Cu and Mo Engineered Nanomaterials on Crop Plant Growth Using a Targeted Proteomics Approach

In this study, we investigated the effects of molybdenum (Mo)-based nanofertilizer and copper (Cu)-based nanopesticide exposure on wheat through a multifaceted approach, including physiological measurements, metal uptake and translocation analysis, and targeted proteomics analysis. Wheat plants were grown under a 16 h photoperiod (light intensity 150 μmol·m-2·s-1) for 4 weeks at 22 °C and 60% humidity with 6 different treatments, including control, Mo, and Cu exposure through root and leaf. The exposure dose was 6.25 mg of element per plant through either root or leaf. An additional low-dose (0.6 mg Mo/plant) treatment of Mo through root was added after phytotoxicity was observed. Using targeted proteomics approach, 24 proteins involved in 12 metabolomic pathways were quantitated to understand the regulation at the protein level. Mo exposure, particularly through root uptake, induced significant upregulation of 16 proteins associated with 11 metabolic pathways, with the fold change (FC) ranging from 1.28 to 2.81. Notably, a dose-dependent response of Mo exposure through the roots highlighted the delicate balance between nutrient stimulation and toxicity as a high Mo dose led to robust protein upregulation but also resulted in depressed physiological measurements, while a low Mo dose resulted in no depression of physiological measurements but downregulations of proteins, especially in the first leaf (0.23 < FC < 0.68) and stem (0.13 < FC < 0.68) tissues. Conversely, Cu exposure exhibited tissue-specific effects, with pronounced downregulation (18 proteins involved in 11 metabolic pathways) particularly in the first leaf tissues (root exposure: 0.35 < FC < 0.74; leaf exposure: 0.49 < FC < 0.72), which indicated the quick response of plants to Cu-induced stress in the early stage of exposure. By revealing the complexities of plants' response to engineered nanomaterials at both physiological and molecular levels, this study provides insights for optimizing nutrient management practices in crop production and advancing toward sustainable agriculture.

Comparative study of Raman spectroscopy techniques in blood plasma-based clinical diagnostics: A demonstration on Alzheimer's disease

Nowadays, there are still many diseases with limited or no reliable methods of early diagnosis. A popular approach in clinical diagnostic research is Raman spectroscopy, as a relatively simple, cost-effective, and high-throughput method for searching for disease-specific alterations in the composition of blood plasma. However, the high variability of the experimental designs, targeted diseases, or statistical processing in the individual studies makes it challenging to compare and compile the results to critically assess the applicability of Raman spectroscopy in real clinical practice. This study aimed to compare data from a single series of blood plasma samples of patients with Alzheimer's disease and non-demented elderly controls obtained by four different techniques/experimental setups - Raman spectroscopy with excitation at 532 and 785 nm, Raman optical activity, and surface-enhanced Raman scattering spectroscopy. The obtained results showed that the spectra from each Raman spectroscopy technique contain different information about biomolecules of blood plasma or their conformation and may, therefore, offer diverse points of view on underlying biochemical processes of the disease. The classification models based on the datasets generated by the three non-chiroptical variants of Raman spectroscopy exhibited comparable diagnostic performance, all reaching an accuracy close to or equal to 80%. Raman optical activity achieved only 60% classification accuracy, suggesting its limited applicability in the specific case of Alzheimer's disease diagnostics. The described differences in the outputs of the four utilized techniques/setups of Raman spectroscopy imply that their choice may crucially affect the acquired results and thus should be approached carefully concerning the specific purpose.

Impact of Suramin on Key Pathological Features of Sporadic Alzheimer's Disease-Derived Forebrain Neurons

Background

Alzheimer's disease (AD) is characterized by disrupted proteostasis and macroautophagy (hereafter "autophagy"). The pharmacological agent suramin has known autophagy modulation properties with potential efficacy in mitigating AD neuronal pathology.

Objective

In the present work, we investigate the impact of forebrain neuron exposure to suramin on the Akt/mTOR signaling pathway, a major regulator of autophagy, in comparison with rapamycin and chloroquine. We further investigate the effect of suramin on several AD-related biomarkers in sporadic AD (sAD)-derived forebrain neurons.

Methods

Neurons differentiated from ReNcell neural progenitors were used to assess the impact of suramin on the Akt/mTOR signaling pathway relative to the autophagy inducer rapamycin and autophagy inhibitor chloroquine. Mature forebrain neurons were differentiated from induced pluripotent stem cells (iPSCs) sourced from a late-onset sAD patient and treated with 100μM suramin for 72 h, followed by assessments for amyloid-β, phosphorylated tau, oxidative/nitrosative stress, and synaptic puncta density.

Results

Suramin treatment of sAD-derived neurons partially ameliorated the increased p-Tau(S199)/Tau ratio, and fully remediated the increased glutathione to oxidized nitric oxide ratio, observed in untreated sAD-derived neurons relative to healthy controls. These positive results may be due in part to the distinct increases in Akt/mTOR pathway mediator p-p70S6K noted with suramin treatment of both ReNcell-derived and iPSC-derived neurons. Longer term neuronal markers, such as synaptic puncta density, were unaffected by suramin treatment.

Conclusions

These findings provide initial evidence supporting the potential of suramin to reduce the degree of dysregulation in sAD-derived forebrain neurons in part via the modulation of autophagy.

Laser Ablation Plasmas and Spectroscopy for Nuclear Applications

The development of measurement methodologies to detect and monitor nuclear-relevant materials remains a consistent and significant interest across the nuclear energy, nonproliferation, safeguards, and forensics communities. Optical spectroscopy of laser-produced plasmas is becoming an increasingly popular diagnostic technique to measure radiological and nuclear materials in the field without sample preparation, where current capabilities encompass the standoff, isotopically resolved and phase-identifiable (e.g., UO and UO2 ) detection of elements across the periodic table. These methods rely on the process of laser ablation (LA), where a high-powered pulsed laser is used to excite a sample (solid, liquid, or gas) into a luminous microplasma that rapidly undergoes de-excitation through the emission of electromagnetic radiation, which serves as a spectroscopic fingerprint for that sample. This review focuses on LA plasmas and spectroscopy for nuclear applications, covering topics from the wide-area environmental sampling and atmospheric sensing of radionuclides to recent implementations of multivariate machine learning methods that work to enable the real-time analysis of spectrochemical measurements with an emphasis on fundamental research and development activities over the past two decades. Background on the physical breakdown mechanisms and interactions of matter with nanosecond and ultrafast laser pulses that lead to the generation of laser-produced microplasmas is provided, followed by a description of the transient spatiotemporal plasma conditions that control the behavior of spectroscopic signatures recorded by analytical methods in atomic and molecular spectroscopy. High-temperature chemical and thermodynamic processes governing reactive LA plasmas are also examined alongside investigations into the condensation pathways of the plasma, which are believed to serve as chemical surrogates for fallout particles formed in nuclear fireballs. Laser-supported absorption waves and laser-induced shockwaves that accompany LA plasmas are also discussed, which could provide insights into atmospheric ionization phenomena from strong shocks following nuclear detonations. Furthermore, the standoff detection of trace radioactive aerosols and fission gases is reviewed in the context of monitoring atmospheric radiation plumes and off-gas streams of molten salt reactors. Finally, concluding remarks will present future outlooks on the role of LA plasma spectroscopy in the nuclear community.

Reevaluation and novel insights into amino sugar and neutral sugar necromass biomarkers in archaea, bacteria, fungi, and plants

Soil microbial necromass is an important contributor to soil organic matter (>50%) and it is largely composed of microbial residues. In soils, fragmented cell wall residues are mostly found in their polysaccharide forms of fungal chitin and bacterial peptidoglycan. Microbial necromass biomarkers, particularly amino sugars (AS) such as glucosamine (GlcN) and muramic acid (MurA) have been used to trace fungal and bacterial residues in soils, and to distinguish carbon (C) found in microbial residues from non-microbial organic C. Neutral sugars (NS), particularly the hexose/pentose ratio, have also been proposed as tracers of plant polysaccharides in soils. In our study, we extended the range of biomarkers to include AS and NS compounds in the biomass of 120 species belonging to archaea, bacteria, fungi, or plants. GlcN was the most common AS found in all taxa, contributing 42-91% to total AS content, while glucose was the most common NS found, contributing 56-79% to total NS. We identified talosaminuronic acid, found in archaeal pseudopeptidoglycan, as a new potential biomarker specific for Euryarchaeota. We compared the variability of these compounds between the different taxonomic groups using multivariate approaches, such as non-metric multidimensional scaling (NMDS) and partial least squares discriminant analysis (PLS-DA) and statistically evaluated their biomarker potential via indicator species analysis. Both NMDS and PLS-DA showcased the variability in the AS and NS contents between the different taxonomic groups, highlighting their potential as necromass residue biomarkers and allowing their extension from separating bacterial and fungal necromass to separating microbes from plants. Finally, we estimated new conversion factors where fungal GlcN is converted to fungal C by multiplying by 10 and MurA is converted to bacterial C by multiplying by 54. Conversion factors for talosaminuronic acid and galactosamine are also proposed to allow estimation of archaeal or all-microbial necromass residue C, respectively.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Optical Screening and Classification of Drug Binding to Proteins in Human Blood Serum

Protein-drug interactions in the human bloodstream are important factors in applications ranging from drug design, where protein binding influences efficacy and dose delivery, to biomedical diagnostics, where rapid, quantitative measurements could guide optimized treatment regimes. Current measurement approaches use multistep assays, which probe the protein-bound drug fraction indirectly and do not provide fundamental structural or dynamic information about the in vivo protein-drug interaction. We demonstrate that ultrafast 2D-IR spectroscopy can overcome these issues by providing a direct, label-free optical measurement of protein-drug binding in blood serum samples. Four commonly prescribed drugs, known to bind to human serum albumin (HSA), were added to pooled human serum at physiologically relevant concentrations. In each case, spectral changes to the amide I band of the serum sample were observed, consistent with binding to HSA, but were distinct for each of the four drugs. A machine-learning-based classification of the serum samples achieved a total cross-validation prediction accuracy of 92% when differentiating serum-only samples from those with a drug present. Identification on a per-drug basis achieved correct drug identification in 75% of cases. These unique spectroscopic signatures of the drug-protein interaction thus enable the detection and differentiation of drug containing samples and give structural insight into the binding process as well as quantitative information on protein-drug binding. Using currently available instrumentation, the 2D-IR data acquisition required just 1 min and 10 μL of serum per sample, and so these results pave the way to fast, specific, and quantitative measurements of protein-drug binding in vivo with potentially invaluable applications for the development of novel therapies and personalized medicine.

The effects of a hydrolyzed protein diet on the plasma, fecal and urine metabolome in cats with chronic enteropathy

Hydrolyzed protein diets are extensively used to treat chronic enteropathy (CE) in cats. However, the biochemical effects of such a diet on feline CE have not been characterized. In this study an untargeted 1H nuclear magnetic resonance spectroscopy-based metabolomic approach was used to compare the urinary, plasma, and fecal metabolic phenotypes of cats with CE to control cats with no gastrointestinal signs recruited at the Royal Veterinary College (RVC). In addition, the biomolecular consequences of a hydrolyzed protein diet in cats with CE was also separately determined in cats recruited from the RVC (n = 16) and the University of Bristol (n = 24) and whether these responses differed between dietary responders and non-responders. Here, plasma metabolites related to energy and amino acid metabolism significantly varied between CE and control cats in the RVC cohort. The hydrolyzed protein diet modulated the urinary metabolome of cats with CE (p = 0.005) in both the RVC and Bristol cohort. In the RVC cohort, the urinary excretion of phenylacetylglutamine, p-cresyl-sulfate, creatinine and taurine at diagnosis was predictive of dietary response (p = 0.025) although this was not observed in the Bristol cohort. Conversely, in the Bristol cohort plasma betaine, glycerol, glutamine and alanine at diagnosis was predictive of outcome (p = 0.001), but these same results were not observed in the RVC cohort. The biochemical signature of feline CE in the RVC cohort was consistent with that identified in human and animal models of inflammatory bowel disease. The hydrolyzed protein diet had the same effect on the urinary metabolome of cats with CE at both sites. However, biomarkers that were predictive of dietary response at diagnosis differed between the 2 sites. This may be due to differences in disease severity, disease heterogeneity, factors unrelated to the disease or small sample size at both sites. As such, further studies utilizing larger number of cats are needed to corroborate these findings.

A systematic scientometric review of paternal inheritance of acquired metabolic traits

BACKGROUND

The concept of the inheritance of acquired traits, a foundational principle of Lamarck's evolutionary theory, has garnered renewed attention in recent years. Evidence for this phenomenon remained limited for decades but gained prominence with the Överkalix cohort study in 2002. This study revealed a link between cardiovascular disease incidence and the food availability experienced by individuals' grandparents during their slow growth periods, reigniting interest in the inheritance of acquired traits, particularly in the context of non-communicable diseases. This scientometric analysis and systematic review comprehensively explores the current landscape of paternally transmitted acquired metabolic traits.

RESULTS

Utilizing Scopus Advanced search and meticulous screening, we included mammalian studies that document the inheritance or modification of metabolic traits in subsequent generations of unexposed descendants. Our inclusive criteria encompass intergenerational and transgenerational studies, as well as multigenerational exposures. Predominantly, this field has been driven by a select group of researchers, potentially shaping the design and focus of existing studies. Consequently, the literature primarily comprises transgenerational rodent investigations into the effects of ancestral exposure to environmental pollutants on sperm DNA methylation. The complexity and volume of data often lead to multiple or redundant publications. This practice, while understandable, may obscure the true extent of the impact of ancestral exposures on the health of non-exposed descendants. In addition to DNA methylation, studies have illuminated the role of sperm RNAs and histone marks in paternally acquired metabolic disorders, expanding our understanding of the mechanisms underlying epigenetic inheritance.

CONCLUSIONS

This review serves as a comprehensive resource, shedding light on the current state of research in this critical area of science, and underscores the need for continued exploration to uncover the full spectrum of paternally mediated metabolic inheritance.

BzATP reverses ferroptosis-induced gut microbiota disorders in collagen-induced arthritis mice

Recent studies suggested that altered gut microbiota may be related to the pathogenesis of rheumatoid arthritis (RA), albeit the exact mechanisms are unknown. In this study, we aimed to discover the particular mechanism of RA treatment by microbiota by investigating the effects of ferroptosis on gut microbiota and its metabolites in collagen-induced arthritis (CIA) mice. Mice were divided into five groups: control, CIA, erastin, BzATP, and BzATP + erastin group. We performed 16S rDNA sequencing and metabolomics analysis on mouse feces and found that erastin and BzATP altered the microbiota and metabolites. The findings demonstrated that the microbiota was significantly disturbed at the phylum (Proteobacteria, Firmicutes, and Bacteroidota) and genus level (Lachnospiraceae_NK4A136, Lactobacillus, and Bifidobacterium) in the CIA group, and erastin exacerbated this disturbance. Unexpectedly, BzATP treatment could repair the disruptive effects of erastin. Additionally, there were significant variations in metabolites between each group. Erastin worsened metabolite abnormalities in CIA mice, while BzATP mitigated them, consistent with the microbiota results. These findings provide novel perspectives and insights into the therapy of RA.

An integrated chemical characterization based on FT-NIR, and GC-MS for the comparative metabolite profiling of 3 species of the genus Amomum

BACKGROUND

The fruits and seeds of genus Amomum are well-known as medicinal plants and edible spices, and are used in countries such as China, India and Vietnam to treat malaria, gastrointestinal disorders and indigestion. The morphological differences between different species are relatively small, and technical characterization and identification techniques are needed.

RESULTS

Fourier transform near infrared spectroscopy (FT-NIR) and gas chromatography-mass spectrometry (GC-MS), combined with principal component analysis and two-dimensional correlation analysis were used to characterize the chemical differences of Amomum tsao-ko, Amomum koenigii, and Amomum paratsaoko. The targets and pathways for the treatment of diabetes mellitus in three species were predicted using network pharmacology and screened for the corresponding pharmacodynamic components as potential quality markers. The results of "component-target-pathway" network showed that (+)-Nerolidol, 2-Nonanol, α-Terpineol, α-Pinene, 2-Nonanone had high degree values and may be the main active components. Partial least squares-discriminant analysis (PLS-DA) was further used to select for differential metabolites and was identified as a potential quality marker, 11 in total. PLS-DA and residual network (ResNet) classification models were developed for the identification of 3 species of the genus Amomum, ResNet model is more suitable for the identification study of large volume samples.

SIGNIFICANCE

This study characterizes the differences between the three species in a visual way and also provides a reliable technique for their identification, while demonstrating the ability of FT-NIR spectroscopy for fast, easy and accurate species identification. The results of this study lay the foundation for quality evaluation studies of genus Amomum and provide new ideas for the development of new drugs for the treatment of diabetes mellitus.

Developing A Baseline Metabolomic Signature Associated with COVID-19 Severity: Insights from Prospective Trials Encompassing 13 U.S. Centers

Metabolic disease is a significant risk factor for severe COVID-19 infection, but the contributing pathways are not yet fully elucidated. Using data from two randomized controlled trials across 13 U.S. academic centers, our goal was to characterize metabolic features that predict severe COVID-19 and define a novel baseline metabolomic signature. Individuals (n = 133) were dichotomized as having mild or moderate/severe COVID-19 disease based on the WHO ordinal scale. Blood samples were analyzed using the Biocrates platform, providing 630 targeted metabolites for analysis. Resampling techniques and machine learning models were used to determine metabolomic features associated with severe disease. Ingenuity Pathway Analysis (IPA) was used for functional enrichment analysis. To aid in clinical decision making, we created baseline metabolomics signatures of low-correlated molecules. Multivariable logistic regression models were fit to associate these signatures with severe disease on training data. A three-metabolite signature, lysophosphatidylcholine a C17:0, dihydroceramide (d18:0/24:1), and triacylglyceride (20:4_36:4), resulted in the best discrimination performance with an average test AUROC of 0.978 and F1 score of 0.942. Pathways related to amino acids were significantly enriched from the IPA analyses, and the mitogen-activated protein kinase kinase 5 (MAP2K5) was differentially activated between groups. In conclusion, metabolites related to lipid metabolism efficiently discriminated between mild vs. moderate/severe disease. SDMA and GABA demonstrated the potential to discriminate between these two groups as well. The mitogen-activated protein kinase kinase 5 (MAP2K5) regulator is differentially activated between groups, suggesting further investigation as a potential therapeutic pathway.

Exploring new dimensions: Single and multi-block analysis of essential oils using DBDI-MS and FT-IR for enhanced authenticity control

BACKGROUND

Essential oils (EOs) are complex mixtures of volatile hydrocarbons with a wide range of applications in the pharmaceutical, fragrance and food industry. The composition of EOs is highly variable and can affect their quality and pharmaceutical efficacy. Moreover, the high economic value of EOs, such as those obtained from Rosa damascena, make falsification and misclassification a lucrative business. Consequently, adulterations can lead to serious health consequences for consumers. While current quality control methods for EOs involve analysing their chromatographic profile or comparing their Fourier transform infrared (FT-IR) spectra, these methods can be time-consuming or lack sensitivity. To address these issues, we compared state-of-the-art quality control methods, including gas chromatography flame ionization detection (GC-FID) quantification and enantiomeric ratio determination, FT-IR spectrometry with dielectric barrier discharge ionization coupled to triple quadrupole mass spectrometer (DBDI-MS), in a chemometric single- and multi-block approach.

RESULTS

Our results show that the best classification accuracy of 94.7% for R. damascena samples was obtained using GC-FID combined with partial least square discriminant analysis (PLS-DA). Comparatively, the enantiomeric ratios did not improve classification accuracy. In contrast, fragmentation data from DBDI-MS (Q3), which was acquired in a fraction of the analysis time and without extensive sample preparation, achieved a classification accuracy of 84.2%. We also found that combining FT-IR with parent ion DBDI-MS (Q1) data in a multi-block sequentially orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) model improved classification accuracy, compared to their respective single-block PLS-DA models.

SIGNIFICANCE

Overall, our study demonstrates that DBDI, as an ambient ionization method, has significant potential for high-throughput screening. When combined with MS, it can produce comparable classification accuracies to conventional methods, while offering the added benefits of speed and convenience. As such, DBDI-MS is a promising tool for EO quality control.

Surface-enhanced Raman spectroscopy hair analysis after household contamination

Trace evidence found at crime scenes is rarely in an unsullied condition. Surface-enhanced Raman spectroscopy (SERS) is a modern analytical technique that can be used for the detection of artificial hair colourants (S. Higgins and D. Kurouski, Surface-Enhanced Raman Spectroscopy Enables Highly Accurate Identification of Different Brands, Types and Colors of Hair Dyes, Talanta, 2022, 251, 123762). However, contaminants pose a problem to collecting accurate spectra from the dyes. In this study, we sought to analyze how the different physical properties of contaminants can influence the collected spectra. We utilized 11 household substances of varying viscosity and opacity to contaminate hair dyed with permanent black or semi-permanent blue dyes. We discovered that contaminant opacity generally does not affect the spectral quality but that high contaminant viscosity does and that acidic substances could destroy the colourant's spectral identity altogether. Cleaning the contaminated hair with a water rinse allowed the underlying colourant to be identified in 21 out of 22 cases. Overall, this study provided a clearer understanding of the capabilities and limitations of SERS in forensic hair analysis.

A primer on correlation-based dimension reduction methods for multi-omics analysis

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

A multi-level approach reveals key physiological and molecular traits in the response of two rice genotypes subjected to water deficit at the reproductive stage

Rice is more vulnerable to drought than maize, wheat, and sorghum because its water requirements remain high throughout the rice life cycle. The effects of drought vary depending on the timing, intensity, and duration of the events, as well as on the rice genotype and developmental stage. It can affect all levels of organization, from genes to the cells, tissues, and/or organs. In this study, a moderate water deficit was applied to two contrasting rice genotypes, IAC 25 and CIRAD 409, during their reproductive stage. Multi-level transcriptomic, metabolomic, physiological, and morphological analyses were performed to investigate the complex traits involved in their response to drought. Weighted gene network correlation analysis was used to identify the specific molecular mechanisms regulated by each genotype, and the correlations between gene networks and phenotypic traits. A holistic analysis of all the data provided a deeper understanding of the specific mechanisms regulated by each genotype, and enabled the identification of gene markers. Under non-limiting water conditions, CIRAD 409 had a denser shoot, but shoot growth was slower despite better photosynthetic performance. Under water deficit, CIRAD 409 was weakly affected regardless of the plant level analyzed. In contrast, IAC 25 had reduced growth and reproductive development. It regulated transcriptomic and metabolic activities at a high level, and activated a complex gene regulatory network involved in growth-limiting processes. By comparing two contrasting genotypes, the present study identified the regulation of some fundamental processes and gene markers, that drive rice development, and influence its response to water deficit, in particular, the importance of the biosynthetic and regulatory pathways for cell wall metabolism. These key processes determine the biological and mechanical properties of the cell wall and thus influence plant development, organ expansion, and turgor maintenance under water deficit. Our results also question the genericity of the antagonism between morphogenesis and organogenesis observed in the two genotypes.

Impact of varying levels of pasture allowance on the nutritional quality and functionality of milk throughout lactation

The objective of this study was to examine the impact of increasing proportions of grazed pasture in the diet on the composition, quality, and functionality of bovine milk across a full lactation. Fifty-four spring-calving cows were randomly assigned to 1 of 3 groups (n = 18), blocked on the basis of mean calving date (February 15, 2020 ± 0.8 d), pre-experimental daily milk yield (24.70 ± 3.70 kg), milk solids yield (2.30 ± 0.27 kg), lactation number (3.10 ± 0.13), and economic breeding index (182 ± 19). Raw milk samples were obtained weekly from each group between March and November 2020. Group 1 (GRS) consumed perennial ryegrass and was supplemented with 5% concentrates (dry matter basis); group 2 was maintained indoors and consumed a total mixed ration (TMR) diet consisting of maize silage, grass silage, and concentrates; and group 3 consumed a partial mixed ration diet (PMR), rotating between perennial ryegrass during the day and indoor TMR feeding at night. Raw milk samples consisted of a pooled morning and evening milking and were analyzed for gross composition, free amino acids, fatty acid composition, heat coagulation time, color, fat globule size, and pH. The TMR milks had a significantly higher total solids, lactose, protein, and whey protein as a proportion of protein content compared with both GRS and PMR milks. The GRS milks demonstrated a significantly lower somatic cell count (SCC), but a significantly higher pH and b*-value than both TMR and PMR milks. The PMR milks exhibited significantly lower total solids and fat content, but also demonstrated significantly higher SCC and total free amino acid content compared with GRS and TMR. Partial least squares discriminant analysis of fatty acid profiles displayed a distinct separation between GRS and TMR samples, while PMR displayed an overlap between both GRS and TMR groupings. Variable importance in projection analysis identified conjugated linoleic acid cis-9,trans-11, C18:2n-6 cis, C18:3n-3, C11:0, and C18:2n-6 trans as the largest contributors to the variation between the diets. Milk fats derived from GRS diets exhibited the highest proportion of unsaturated fats and higher unsaturation, health-promoting, and desaturase indices. The lowest proportions of saturated fats and the lowest atherogenic index were also exhibited by GRS-derived milk fats. This work highlights the positive influence of grass-fed milk for human consumption through its more nutritionally beneficial fatty acid profile, despite the highest milk solid percentages derived from TMR feeding systems. Furthermore, this study demonstrates the proportional response of previously highlighted biomarkers of pasture feeding to the proportion of pasture in the cow's diet.