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The rising prevalence of obesity in Saudi Arabia is a major contributor to the nation's high levels of cardiometabolic diseases such as type 2 diabetes. To assess the impact of obesity on the diabetic metabolic phenotype presented in young Saudi Arabian adults, participants ( n = 289, aged 18-40 years) were recruited and stratified into four groups: healthy weight (BMI 18.5-24.99 kg/m 2 ) with ( n = 57) and without diabetes ( n = 58) or overweight/obese (BMI > 24.99 kg/m 2 ) with ( n = 102) and without diabetes ( n = 72). Distinct plasma metabolic phenotypes associated with high BMI and diabetes were identified using nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography mass spectrometry. Increased plasma glucose and dysregulated lipoproteins were characteristics of obesity in individuals with and without diabetes, but the obesity-associated lipoprotein phenotype was partially masked in individuals with diabetes. Although there was little difference between diabetics and nondiabetics in the global plasma LDL cholesterol and phospholipid concentration, the distribution of lipoprotein particles was altered in diabetics with a shift toward denser and more atherogenic LDL5 and LDL6 particles, which was amplified in the presence of obesity. Further investigation is warranted in larger Middle Eastern populations to explore the dysregulation of metabolism driven by interactions between obesity and diabetes in young adults.

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio ( m / z ), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m / z , and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Purpose: Independent calculations of proton therapy plans are an important quality control procedure in treatment planning. When using custom Monte Carlo (MC) models of the beamline, deploying the calculations can be laborious, time consuming, and require in-depth knowledge of the computational environment. We developed an automated framework to remove these barriers and integrate our MC model into the clinical workflow., Materials and Methods: The Eclipse Scripting Application Programming Interface was used to initiate the automation process. A series of MATLAB scripts were then used for preprocessing of input data and postprocessing of results. Additional scripts were used to monitor the calculation process and appropriately deploy calculations to an institutional high-performance computing facility. The automated framework and beamline models were validated against 160 patient specific QA measurements from an ionization chamber array and using a ±3%/3 mm gamma criteria., Results: The automation reduced the human-hours required to initiate and run a calculation to 1-2 min without leaving the treatment planning system environment. Validation comparisons had an average passing rate of 99.4% and were performed at depths ranging from 1 to 15 cm., Conclusion: An automated framework for running MC calculations was developed which enables the calculation of dose and linear energy transfer within a clinically relevant workflow and timeline. The models and framework were validated against patient specific QA measurements and exhibited excellent agreement. Before this implementation, execution was prohibitively complex for an untrained individual and its use restricted to a research environment., (© 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Importance: Chinese women have the highest rate of lung cancer among female never-smokers in the world, and the etiology is poorly understood., Objective: To assess the association between metabolomics and lung cancer risk among never-smoking women., Design, Setting, and Participants: This nested case-control study included 275 never-smoking female patients with lung cancer and 289 never-smoking cancer-free control participants from the prospective Shanghai Women's Health Study recruited from December 28, 1996, to May 23, 2000. Validated food frequency questionnaires were used for the collection of dietary information. Metabolomic analysis was conducted from November 13, 2015, to January 6, 2016. Data analysis was conducted from January 6, 2016, to November 29, 2018., Exposures: Untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance metabolomic profiles were characterized using prediagnosis urine samples. A total of 39 416 metabolites were measured., Main Outcomes and Measures: Incident lung cancer., Results: Among the 564 women, those who developed lung cancer (275 participants; median [interquartile range] age, 61.0 [52-65] years) and those who did not develop lung cancer (289 participants; median [interquartile range] age, 62.0 [53-66] years) at follow-up (median [interquartile range] follow-up, 10.9 [9.0-11.7] years) were similar in terms of their secondhand smoke exposure, history of respiratory diseases, and body mass index. A peak metabolite, identified as 5-methyl-2-furoic acid, was significantly associated with lower lung cancer risk (odds ratio, 0.57 [95% CI, 0.46-0.72]; P < .001; false discovery rate = 0.039). Furthermore, this peak was weakly correlated with self-reported dietary soy intake (ρ = 0.21; P < .001). Increasing tertiles of this metabolite were associated with lower lung cancer risk (in comparison with first tertile, odds ratio for second tertile, 0.52 [95% CI, 0.34-0.80]; and odds ratio for third tertile, 0.46 [95% CI, 0.30-0.70]), and the association was consistent across different histological subtypes and follow-up times. Additionally, metabolic pathway analysis found several systemic biological alterations that were associated with lung cancer risk, including 1-carbon metabolism, nucleotide metabolism, oxidative stress, and inflammation., Conclusions and Relevance: This prospective study of the untargeted urinary metabolome and lung cancer among never-smoking women in China provides support for the hypothesis that soy-based metabolites are associated with lower lung cancer risk in never-smoking women and suggests that biological processes linked to air pollution may be associated with higher lung cancer risk in this population.

Purpose: A good Monte Carlo model with an accurate head shielding model is important in estimating the long-term risks of unwanted radiation exposure during radiation therapy. The aim of this paper was to validate the Monte Carlo simulation of a TrueBeam linear accelerator (linac) head shielding model. We approach this by evaluating the accuracy of out-of-field dose predictions at extended distances which are comprised of scatter from within the patient and treatment head leakage and thus reflect the accuracy of the head shielding model. We quantify the out-of-field dose of a TrueBeam linac for low-energy photons, 6X and 6X-FFF beams, and compare measurements to Monte Carlo simulations using Varian VirtuaLinac that include a realistic head shielding model, for a variety of jaw sizes and angles up to a distance of 100 cm from the isocenter, in both positive and negative directions. Given the high value and utility of the VirtuaLinac model, it is critical that this model is validated thoroughly and the results be available to the medical physics community., Materials and Method: Simulations were done using VirtuaLinac, the GEANT4-based Monte Carlo model of the TrueBeam treatment head from Varian Medical Systems, and an in-house GEANT4-based code. VirtuaLinac included a detailed model of the treatment head shielding and was run on the Amazon Web Services cloud to generate spherical phase space files surrounding the treatment head. These phase space files were imported into the in-house code, which modeled the measurement setup with a solid water buildup, the carbon fiber couch, and the gantry stand. For each jaw size (2 × 2 cm 2 , 4 × 4 cm 2 , 10 × 10 cm 2 , and 20 × 20 cm 2 ) and angular setting (0°, 90°, 45°, 135°), the dose was calculated at intervals of 5 cm along each measurement direction., Results: For the 10 × 10 cm 2 jaw size, both 6X and 6X-FFF showed very good agreement between simulation and measurement in both in-plane directions, with no apparent systematic bias. The percentage deviations for these settings were as follows: (mean, STDEV, maximum) (8.34, 6.44, 24.84) for 6X and (13.21, 8.93, 35.56) for 6X-FFF. For all jaw sizes, simulation agreed well in the in-plane direction going away from the gantry, but, some deviations were observed moving toward the gantry at larger distances. At larger distances, for the jaw sizes smaller than 10 × 10 cm 2 , the simulation underestimates the dose compared with measurement, while for jaw sizes larger than 10 × 10 cm 2 , it overestimates dose. For all comparisons between ±50 cm from isocenter, average absolute agreement between simulation and measurement was better than 28%., Conclusion: We have validated the Varian VirtuaLinac's head shielding model via out-of-field doses and quantified the differences between TrueBeam head shielding model created out-of-field doses and measurements for an extended distance of 100 cm., (© 2018 American Association of Physicists in Medicine.)

Introduction: Differences in the metabolite profiles between serum and plasma are incompletely understood., Objectives: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes., Methods: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1 H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis., Results: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples., Conclusions: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

Numerous metabolic profiling pipelines have been developed to characterize the composition of human biofluids and tissues, the vast majority of these being for studies in adults. To accommodate limited sample volume and to take into account the compositional differences between adult and infant biofluids, we developed and optimized sample handling and analytical procedures for studying urine from newborns. A robust pipeline for metabolic profiling using NMR spectroscopy was established, encompassing sample collection, preparation, spectroscopic measurement, and computational analysis. Longitudinal samples were collected from five infants from birth until 14 months of age. Methods of extraction and effects of freezing and sample dilution were assessed, and urinary contaminants from breakdown of polymers in a range of diapers and cotton wool balls were identified and compared, including propylene glycol, acrylic acid, and tert-butanol. Finally, assessment of urinary profiles obtained over the first few weeks of life revealed a dramatic change in composition, with concentrations of phenols, amino acids, and betaine altering systematically over the first few months of life. Therefore, neonatal samples require more stringent standardization of experimental design, sample handling, and analysis compared to that of adult samples to accommodate the variability and limited sample volume.

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze–thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage. [ABSTRACT FROM AUTHOR]

Mutations in fukutin-related protein (FKRP) are responsible for a common group of muscular dystrophies ranging from adult onset limb girdle muscular dystrophies to severe congenital forms with associated structural brain involvement. The defining feature of this group of disorders is the hypoglycosylation of α-dystroglycan and its inability to effectively bind extracellular matrix ligands such as laminin α2. However, α-dystroglycan has the potential to interact with a number of laminin isoforms many of which are basement membrane/tissue specific and developmentally regulated. To further investigate this we evaluated laminin α-chain expression in the cerebral cortex and eye of our FKRP knock-down mouse (FKRP(KD)). These mice showed a marked disturbance in the deposition of laminin α-chains including α1, α2, α4, and α5, although only laminin α1- and γ1-chain mRNA expression was significantly upregulated relative to controls. Moreover, there was a diffuse pattern of laminin deposition below the pial surface which correlated with an abrupt termination of many of the radial glial cells. This along with the pial basement membrane defects, contributed to the abnormal positioning of both early- and late-born neurons. Defects in the inner limiting membrane of the eye were associated with a reduction of laminin α1 demonstrating the involvement of the α-dystroglycan:laminin α1 axis in the disease process. These observations demonstrate for the first time that a reduction in Fkrp influences the ability of tissue-specific forms of α-dystroglycan to direct the deposition of several laminin isoforms in the formation of different basement membranes.

Muscular dystrophies are a clinically and genetically heterogeneous group of disorders. Until recently most of the proteins associated with muscular dystrophies were believed to be proteins of the sarcolemma associated with reinforcing the plasma membrane or in facilitating its re-sealing following injury. In the last few years a novel and frequent pathogenic mechanism has been identified that involves the abnormal glycosylation of alpha-dystroglycan (ADG). This peripheral membrane protein undergoes complex and crucial glycosylation steps that enable it to interact with LG domain containing extracellular matrix proteins such as laminins, agrin and perlecan. Mutations in six genes (POMT1, POMT2, POMGnT1, fukutin, FKRP and LARGE) have been identified in patients with reduced glycosylation of ADG. While initially a clear correlation between gene defect and phenotype was observed for each of these 6 genes (for example, Walker Warburg syndrome was associated with mutations in POMT1 and POMT2, Fukuyama congenital muscular dystrophy associated with fukutin mutations, and Muscle Eye Brain disease associated with POMGnT1 mutations), we have recently demonstrated that allelic mutations in each of these 6 genes can result in a much wider spectrum of clinical conditions. Thus, the crucial aspect in determining the phenotypic severity is not which gene is primarily mutated, but how severely the mutation affects the glycosylation of ADG. Systematic mutation analysis of these 6 glycosyltransferases in patients with a dystroglycan glycosylation disorder identifies mutations in approximately 65% suggesting that more genes have yet to be identified.

Metabolomics plays a crucial role in understanding metabolic processes within biological systems. Using specific pulse sequences, NMR-based metabolomics detects small and macromolecular metabolites that are altered in blood samples. Here we proposed a method called spectral editing neural network, which can effectively edit and separate the spectral signals of small and macromolecules in 1H NMR spectra of serum and plasma based on the linewidth of the peaks. We applied the model to process the 1H NMR spectra of plasma and serum. The extracted small and macromolecular spectra were then compared with experimentally obtained relaxation-edited and diffusion-edited spectra. Correlation analysis demonstrated the quantitative capability of the model in the extracted small molecule signals from 1H NMR spectra. The principal component analysis showed that the spectra extracted by the model and those obtained by NMR spectral editing methods reveal similar group information, demonstrating the effectiveness of the model in signal extraction. 1H NMR-based metabolomics can detect small and macromolecular metabolites simultaneously from complex biological samples, however, signaling overlap remains a challenge for accurate molecular identification and quantification. Here, the authors develop a spectral editing neural network to effectively edit and separate the spectral signals of small and macromolecules in the 1H NMR spectra of serum and plasma based on the linewidth of the peaks. [ABSTRACT FROM AUTHOR]

The ever-evolving social media discourse has witnessed an overwhelming use of memes to express opinions or dissent. Besides being misused for spreading malcontent, they are mined by corporations and political parties to glean the public's opinion. Therefore, memes predominantly offer affect-enriched insights towards ascertaining the societal psyche. However, the current approaches are yet to model the affective dimensions expressed in memes effectively. They rely extensively on large multimodal datasets for pre-training and do not generalize well due to constrained visual-linguistic grounding. In this paper, we introduce MOOD (Meme emOtiOns Dataset), which embodies six basic emotions. We then present ALFRED (emotion-Aware muLtimodal Fusion foR Emotion Detection), a novel multimodal neural framework that (i) explicitly models emotion-enriched visual cues, and (ii) employs an efficient cross-modal fusion via a gating mechanism. Our investigation establishes ALFRED's superiority over existing baselines by 4.94% F1. Additionally, ALFRED competes strongly with previous best approaches on the challenging Memotion task. We then discuss ALFRED's domain-agnostic generalizability by demonstrating its dominance on two recently-released datasets – HarMeme and Dank Memes, over other baselines. Further, we analyze ALFRED's interpretability using attention maps. Finally, we highlight the inherent challenges posed by the complex interplay of disparate modality-specific cues toward meme analysis. [ABSTRACT FROM AUTHOR]

Mutations in the LMNA gene, which encodes nuclear lamins A and C by alternative splicing, can give rise to Emery-Dreifuss muscular dystrophy. The mechanism by which lamins A and C separately contribute to this molecular phenotype is unknown. To address this question we examined ten LMNA mutations exogenously expressed as lamins A and C in COS-7 cells. Eight of the mutations when expressed in lamin A, exhibited a range of nuclear mislocalisation patterns. However, two mutations (T150P and delQ355) almost completely relocated exogenous lamin A from the nuclear envelope to the cytoplasm, disrupted nuclear envelope reassembly following cell division and altered the protein composition of the mid-body. In contrast, exogenously expressed DsRed2-tagged mutant lamin C constructs were only inserted into the nuclear lamina if co-expressed with any EGFP-tagged lamin A construct, except with one carrying the T150P mutation. The T150P, R527P and L530P mutations reduced the ability of lamin A, but not lamin C from binding to emerin. These data identify specific functional roles for the emerin-lamin C- and emerin-lamin A- containing protein complexes and is the first report to suggest that the A-type lamin mutations may be differentially dysfunctional for the same LMNA mutation.

Pediatric chronic kidney disease (CKD) is a clinical condition characterized by progressive renal function deterioration. CKD diagnosis is based on glomerular filtration rate, but its reliability is limited, especially at the early stages. New potential biomarkers (citrulline (CIT), symmetric dimethylarginine (SDMA), S-adenosylmethionine (SAM), n-butyrylcarnitine (nC4), cis-4-decenoylcarnitine, sphingosine-1-phosphate and bilirubin) in addition to creatinine (CNN) have been proposed for early diagnosis. To verify the clinical value of these biomarkers we performed a comprehensive targeted metabolomics study on a representative cohort of CKD and healthy pediatric patients. Sixty-seven children with CKD and forty-five healthy children have been enrolled in the study. Targeted metabolomics based on liquid chromatography-triple quadrupole mass spectrometry has been used for serum and plasma samples analysis. Univariate data analysis showed statistically significant differences (p < 0.05) in the concentration of CNN, CIT, SDMA, and nC4 among healthy and CKD pediatric patients. The predictive ability of the proposed biomarkers was also confirmed through specificity and sensitivity expressed in Receiver Operating Characteristic curves (AUC = 0.909). In the group of early CKD pediatric patients, AUC of 0.831 was obtained, improving the diagnostic reliability of CNN alone. Moreover, the models built on combined CIT, nC4, SDMA, and CNN allowed to distinguish CKD patients from healthy control regardless of blood matrix type (serum or plasma). Our data demonstrate potential biomarkers in the diagnosis of early CKD stages. [ABSTRACT FROM AUTHOR]

How can we reliably transfer affect models trained in controlled laboratory conditions (in-vitro) to uncontrolled real-world settings (in-vivo)? The information gap between in-vitro and in-vivo applications defines a core challenge of affective computing. This gap is caused by limitations related to affect sensing including intrusiveness, hardware malfunctions and availability of sensors. As a response to these limitations, we introduce the concept of privileged information for operating affect models in real-world scenarios (in the wild). Privileged information enables affect models to be trained across multiple modalities available in a lab, and ignore, without significant performance drops, those modalities that are not available when they operate in the wild. Our approach is tested in two multimodal affect databases one of which is designed for testing models of affect in the wild. By training our affect models using all modalities and then using solely raw footage frames for testing the models, we reach the performance of models that fuse all available modalities for both training and testing. The results are robust across both classification and regression affect modeling tasks which are dominant paradigms in affective computing. Our findings make a decisive step towards realizing affect interaction in the wild. [ABSTRACT FROM AUTHOR]

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time–m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine. [ABSTRACT FROM AUTHOR]

Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App. [ABSTRACT FROM AUTHOR]

The recently uncovered role of Fukutin-related protein (FKRP) in fibronectin glycosylation has challenged our understanding of the basis of disease pathogenesis in the muscular dystrophies. FKRP is a Golgi-resident glycosyltransferase implicated in a broad spectrum of muscular dystrophy (MD) pathologies that are not fully attributable to the well-described α-Dystroglycan hypoglycosylation. By revealing a new role for FKRP in the glycosylation of fibronectin, a modification critical for the development of the muscle basement membrane (MBM) and its associated muscle linkages, new possibilities for understanding clinical phenotype arise. This modification involves an interaction between FKRP and myosin-10, a protein involved in the Golgi organization and function. These observations suggest a FKRP nexus exists that controls two critical aspects to muscle fibre integrity, both fibre stability at the MBM and its elastic properties. This review explores the new potential disease axis in the context of our current knowledge of muscular dystrophies., (© 2022 Wiley Periodicals LLC.)

Recent research has unveiled numerous important functions of protein glycosylation in development, homeostasis, and diseases. A type of glycosylation taking the center stage is protein O-mannosylation, a posttranslational modification conserved in a wide range of organisms, from yeast to humans. In animals, protein O-mannosylation plays a crucial role in the nervous system, whereas protein O-mannosylation defects cause severe neurological abnormalities and congenital muscular dystrophies. However, the molecular and cellular mechanisms underlying protein O-mannosylation functions and biosynthesis remain not well understood. This review outlines recent studies on protein O-mannosylation while focusing on the functions in the nervous system, summarizes the current knowledge about protein O-mannosylation biosynthesis, and discusses the pathologies associated with protein O-mannosylation defects. The evolutionary perspective revealed by studies in the Drosophila model system are also highlighted. Finally, the review touches upon important knowledge gaps in the field and discusses critical questions for future research on the molecular and cellular mechanisms associated with protein O-mannosylation functions. [ABSTRACT FROM AUTHOR]

The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60–90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography–Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset—measured by F1 score (0.76) and AUROC (0.77)—included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations. [ABSTRACT FROM AUTHOR]

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Objective: The purpose of this study was to employ metabolomics for the analysis of urine metabolites in swimmers, with the aim of establishing models for assessing their athletic status and competitive potential. Furthermore, the study sought to compare the identification efficacy of multi-component (urine and blood) model versus single-component (urine or blood) models, in order to determine the optimal approach for evaluating training and competitive status. Methods: A total of 187 Chinese professional swimmers, comprising 103 elite and 84 sub-elite level athletes, were selected as subjects for this study. Urine samples were obtained from each participant and subjected to nuclear magnetic resonance (NMR) metabolomics analysis. Significant urine metabolites were screened through multivariable logistic regression analysis, and an identification model was established. Based on the previously established model of blood metabolites, this study compared the discriminative and predictive performance of three models: either urine or blood metabolites model and urine + blood metabolites model. Results: Among 39 urine metabolites, 10 were found to be significantly associated with the athletic status of swimmers (p < 0.05). Of these, levels of 2-KC, cisaconitate, formate, and LAC were higher in elite swimmers compared to sub-elite athletes, while levels of 3-HIV, creatinine, 3-HIB, hippurate, pseudouridine, and trigonelline were lower in elite swimmers. Notably, 2-KC and 3-HIB exhibited the most substantial differences. An identification model was developed to estimate physical performance and athletic level of swimmers while adjusting for different covariates and including 2-KC and 3-HIB. The urine metabolites model showed an area under the curve (AUC) of 0.852 (95% CI: 0.793–0.912) for discrimination. Among the three identification models tested, the combination of urine and blood metabolites showed the highest performance than either urine or blood metabolites, with an AUC of 0.925 (95% CI: 0.888–0.963). Conclusion: The two urine metabolites, 2-KC and 3-HIV, can serve as significant urine metabolic markers to establish a discrimination model for identifying the athletic status and competitive potential of Chinese elite swimmers. Combining two screened urine metabolites with four metabolites reported exhibiting significant differences in blood resulted in improved predictive performance compared to using urine metabolites alone. These findings indicate that combining blood and urine metabolites has a greater potential for identifying and predicting the athletic status and competitive potential of Chinese professional swimmers. [ABSTRACT FROM AUTHOR]

Selenium is a trace mineral that is essential for health. After being obtained from food and taken up by the liver, selenium performs various physiological functions in the body in the form of selenoproteins, which are best known for their redox activity and anti-inflammatory properties. Selenium stimulates the activation of immune cells and is important for the activation of the immune system. Selenium is also essential for the maintenance of brain function. Selenium supplements can regulate lipid metabolism, cell apoptosis, and autophagy, and have displayed significant alleviating effects in most cardiovascular diseases. However, the effect of increased selenium intake on the risk of cancer remains unclear. Elevated serum selenium levels are associated with an increased risk of type 2 diabetes, and this relationship is complex and nonlinear. Selenium supplementation seems beneficial to some extent; however, existing studies have not fully explained the influence of selenium on various diseases. Further, more intervention trials are needed to verify the beneficial or harmful effects of selenium supplementation in various diseases. [ABSTRACT FROM AUTHOR]

Untargeted metabolomics approaches require complex samples containing the endogenous metabolites of a biological system. Here, we describe a set of protocols that can be applied to various types of samples, including prokaryotic and eukaryotic cells, as well as animal and human samples. Following a single extraction step, samples are analysed using different chromatographic conditions coupled to high-resolution mass spectrometry. Quantification of metabolite changes between samples is performed without internal standards, using peak areas from extracted ion chromatograms for statistical analysis. Bioinformatics annotation of the results allows a pathway- and process-oriented analysis across biological sample conditions, allowing a complete pathway interrogation. [ABSTRACT FROM AUTHOR]

Introduction: Diagnosing myocardial infarction is difficult during the initial phase. As, acute myocardial ischemia is associated with changes in metabolic pathways, metabolomics may provide ways of identifying early stages of ischemia. We investigated the changes in metabolites after induced ischemia in humans using nuclear magnetic resonance spectroscopy (NMR). Methods: We included patients undergoing elective coronary angiography showing normal coronary arteries. These were randomized into 4 groups and underwent coronary artery occlusion for 0, 30, 60 or 90 s. Blood was collected over the next 3 h and analyzed using NMR. We used 2-way ANOVA of time from baseline- and treatment group to find metabolites that changed significantly following the intervention and principal component analysis (PCA) to investigate changes between the 90 s ischemia- and control groups at 15 and 60 min after intervention. Results: We included 34 patients. The most pronounced changes were observed in the lipid metabolism where 38 of 112 lipoprotein parameters (34%) showed a significant difference between the patients exposed to ischemia and the control group. There was a decrease in total plasma triglycerides over the first hour followed by a normalization. The principal component analysis showed a effects of the treatment after just 15 min. These effects were dominated by changes in high-density lipoprotein. An increase in lactic acid levels was detected surprisingly late, 1–2 h after the ischemia. Conclusion: We investigated the earliest changes in metabolites of patients undergoing brief myocardial ischemia and found that ischemia led to changes throughout the lipid metabolism as early as 15 min post-intervention. [ABSTRACT FROM AUTHOR]

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated deep representation learning where hierarchical representations are automatically learned in a data-driven manner. This article presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER. [ABSTRACT FROM AUTHOR]

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis., (© 2024 Wiley‐VCH GmbH.)

Macrophage-derived lipid-laden foam cells from the subendothelium play a crucial role in the initiation and progression of atherosclerosis. However, the molecule mechanism that regulates the formation of foam cells is not completely understood. Here, we found that SLAMF7 was upregulated in mice bone marrow-derived macrophages and RAW264.7 cells stimulated with oxidized low-density lipoprotein (ox-LDL). SLAMF7 promoted ox-LDL-mediated macrophage lipid accumulation and M1-type polarization. SLAMF7 deficiency reduced serum lipid levels and improved the lesions area of carotid plaque and aortic arch in high-fat diet-fed ApoE -/- mice. In response to ox-LDL, SLAMF7 downregulated NR4A1 and upregulated RUNX3 through transcriptome sequencing analysis. Overexpression NR4A1 reversed SLAMF7-induced lipid uptake and M1 polarization via inhibiting RUNX3 expression. Furthermore, RUNX3 enhanced foam cell formation and M1-type polarization. Taken together, the study suggested that SLAMF7 play contributing roles in the pro-atherogenic effects by regulating NR4A1-RUNX3., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Speech emotion recognition, as an important component of humancomputer interaction technology, has received increasing attention. Recent studies have treated emotion recognition of speech signals as a multimodal task, due to its inclusion of the semantic features of two different modalities, i.e., audio and text. However, existing methods often fail in effectively represent features and capture correlations. This paper presents a multi-level circulant cross-modal Transformer (MLCCT) formultimodal speech emotion recognition. The proposed model can be divided into three steps, feature extraction, interaction and fusion. Self-supervised embedding models are introduced for feature extraction, which give a more powerful representation of the original data than those using spectrograms or audio features such as Mel-frequency cepstral coefficients (MFCCs) and low-level descriptors (LLDs). In particular, MLCCT contains two types of feature interaction processes, where a bidirectional Long Short-term Memory (Bi-LSTM) with circulant interaction mechanism is proposed for low-level features, while a two-stream residual cross-modal Transformer block is appliedwhen high-level features are involved. Finally, we choose self-attention blocks for fusion and a fully connected layer to make predictions. To evaluate the performance of our proposed model, comprehensive experiments are conducted on three widely used benchmark datasets including IEMOCAP, MELD and CMU-MOSEI. The competitive results verify the effectiveness of our approach. [ABSTRACT FROM AUTHOR]