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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.

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]

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]

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]

Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late‐onset AD. This study analyzed genome‐wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi‐omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell‐type enrichment analyses. A predicted lipid signature was extracted from genome‐scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA‐7 null mice, a mouse model of one of the genes associated with late‐onset AD. Moreover, a metabolome‐wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS‐derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over‐represented across the AD multi‐omics data sets. Microglia and astrocytes were significantly enriched in the lipid‐predominant AD‐metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms‐metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi‐omics data into GSMNs to identify metabolic alterations. [ABSTRACT FROM AUTHOR]

Purpose: We quantified the effect of various forward‐based treatment‐planning strategies in proton therapy on dose‐weighted linear energy transfer (LETd). By maintaining the dosimetric quality at a clinically acceptable level, we aimed to evaluate the differences in LETd among various treatment‐planning approaches and their practicality in minimizing biologic uncertainties associated with LETd. Method: Eight treatment‐planning strategies that are achievable in commercial treatment‐planning systems were applied on a cylindrical water phantom and four pediatric brain tumor cases. Each planning strategy was compared to either an opposed lateral plan (phantom study) or original clinical plan (patient study). Deviations in mean and maximum LETd from clinically acceptable dose distributions were compared. Results: In the phantom study, using a range shifter and altering the robust scenarios during optimization had the largest effect on the mean clinical target volume LETd, which was reduced from 4.5 to 3.9 keV/μm in both cases. Variations in the intersection angle between beams had the largest effect on LETd in a ring defined 3 to 5 mm outside the target. When beam intersection angles were reduced from opposed laterals (180°) to 120°, 90°, and 60°, corresponding maximum LETd increased from 7.9 to 8.9, 10.9, and 12.2 keV/μm, respectively. A clear trend in mean and maximum LETd variations in the clinical cases could not be established, though spatial distribution of LETd suggested a strong dependence on patient anatomy and treatment geometry. Conclusion: Changes in LETd from treatment‐plan setup follow intuitive trends in a controlled phantom experiment. Anatomical and other patient‐specific considerations, however, can preclude generalizable strategies in clinical cases. For pediatric cranial radiation therapy, we recommend using opposed lateral treatment fields to treat midline targets. [ABSTRACT FROM AUTHOR]

Before the partial correlation analysis and equation construction, the measured glomerular filtration rate (mGFR), normalized abundances of the putative metabolite markers, serum/plasma creatinine level and serum/plasma cystatin-C (cysC) level were logarithmically transformed. They were not significantly different from those of the current eGFRcr-cys(ASR) equation ( I p i -values >.890) but were significantly different from the I P i SB 30 sb value of the new eGFRcr(AS) equation ( I p i -values <.05). Performance of the equations was examined with reference to the GFR measured by iohexol plasma clearance (mGFR).[[2], [4]] The bias and precision of the current and new CKD-EPI equations were similar for the two cohorts (Table 1). [Extracted from the article]

Purpose: Challenges in proton therapy include identifying patients most likely to benefit; ensuring consistent, high‐quality plans as its adoption becomes more widespread; and recognizing biological uncertainties that may be related to increased relative biologic effectiveness driven by linear energy transfer (LET). Knowledge‐based planning (KBP) is a domain that may help to address all three. Methods: Artificial neural networks were trained using 117 unique treatment plans and associated dose and dose‐weighted LET (LETD) distributions. The data set was split into training (n = 82), validation (n = 17), and test (n = 18) sets. Model performance was evaluated on the test set using dose‐ and LETD‐volume metrics in the clinical target volume (CTV) and nearby organs at risk and Dice similarity coefficients (DSC) comparing predicted and planned isodose lines at 50%, 75%, and 95% of the prescription dose. Results: Dose‐volume metrics significantly differed (α = 0.05) between predicted and planned dose distributions in only one dose‐volume metric, D2% to the CTV. The maximum observed root mean square (RMS) difference between corresponding metrics was 4.3 GyRBE (8% of prescription) for D1cc to optic chiasm. DSC were 0.90, 0.93, and 0.88 for the 50%, 75%, and 95% isodose lines, respectively. LETD‐volume metrics significantly differed in all but one metric, L0.1cc of the brainstem. The maximum observed difference in RMS differences for LETD metrics was 1.0 keV/μm for L0.1cc to brainstem. Conclusions: We have devised the first three‐dimensional dose and LETD‐prediction model for cranial proton radiation therapy has been developed. Dose accuracy compared favorably with that of previously published models in other treatment sites. The agreement in LETD supports future investigations with biological doses in mind to enable the full potential of KBP in proton therapy. [ABSTRACT FROM AUTHOR]

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food. [ABSTRACT FROM AUTHOR]

Plants produce a variety of high-value chemicals (e.g., secondary metabolites) which have a plethora of biological activities, which may be utilised in many facets of industry (e.g., agrisciences, cosmetics, drugs, neutraceuticals, household products, etc.). Exposure to various different environments, as well as their treatment (e.g., exposure to chemicals), can influence the chemical makeup of these plants and, in turn, which chemicals will be prevalent within them. Essential oils (EOs) usually have complex compositions (>300 organic compounds, e.g., alkaloids, flavonoids, phenolic acids, saponins and terpenes) and are obtained from botanically defined plant raw materials by dry/steam distillation or a suitable mechanical process (without heating). In certain cases, an antioxidant may be added to the EO (EOs are produced by more than 17,500 species of plants, but only ca. 250 EOs are commercially available). The interesting bioactivity of the chemicals produced by plants renders them high in value, motivating investment in their production, extraction and analysis. Traditional methods for effectively extracting plant-derived biomolecules include cold pressing and hydro/steam distillation; newer methods include solvent/Soxhlet extractions and sustainable processes that reduce waste, decrease processing times and deliver competitive yields, examples of which include microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), subcritical water extraction (SWE) and supercritical CO2 extraction (scCO2). Once extracted, analytical techniques such as chromatography and mass spectrometry may be used to analyse the contents of the high-value extracts within a given feedstock. The bioactive components, which can be used in a variety of formulations and products (e.g., displaying anti-aging, antibacterial, anticancer, anti-depressive, antifungal, anti-inflammatory, antioxidant, antiparasitic, antiviral and anti-stress properties), are biorenewable high-value chemicals. [ABSTRACT FROM AUTHOR]

The dystrophin-associated protein complex (DAPC) is a highly organized multiprotein complex that plays a pivotal role in muscle fiber structure integrity and cell signaling. The complex is composed of three distinct interacting subgroups, intracellular peripheral proteins, transmembrane glycoproteins, and extracellular glycoproteins subcomplexes. Dystrophin protein nucleates the DAPC and is important for connecting the intracellular actin cytoskeletal filaments to the sarcolemma glycoprotein complex that is connected to the extracellular matrix via laminin, thus stabilizing the sarcolemma during muscle fiber contraction and relaxation. Genetic mutations that lead to lack of expression or altered expression of any of the DAPC proteins are associated with different types of muscle diseases. Hence characterization of this complex in healthy and dystrophic muscle might bring insights into its role in muscle pathogenesis. This review highlights the role of mass spectrometry in characterizing the DAPC interactome as well as post-translational glycan modifications of some of its components such as α-dystroglycan. Detection and quantification of dystrophin using targeted mass spectrometry are also discussed in the context of healthy versus dystrophic skeletal muscle., (© 2022 John Wiley & Sons Ltd.)

Background and Purpose: Membranous nephropathy (MN) is an immune-mediated glomerular disease in adults. Antibody- and antigen-bonding mechanisms have been largely clarified, but the subepithelium immune complex deposition-mediated downstream molecular mechanisms are currently unresolved. Increasing evidence has suggested that gut microbiota contribute to MN pathogenesis., Experimental Approach: In this study, we identified alterations in faecal gut microbiota and serum metabolites that mediate an aryl hydrocarbon receptor (AhR) mechanism in cationic bovine serum albumin (CBSA)-induced MN rats and in patients with idiopathic MN (IMN)., Key Results: Impaired renal function correlated with the relative abundance of reduced faecal probiotics, Lactobacillus and Bifidobacterium, and altered serum levels of tryptophan-produced indole derivatives (TPIDs) in MN rats. Further results showed that reduced relative abundance of five probiotics, namely Lactobacillus johnsonii, Lactobacillus murinus, Lactobacillus vaginalis, Lactobacillus reuteri and Bifidobacterium animalis, positively correlated with decreased levels of indole-3-pyruvic acid, indole-3-aldehyde and tryptamine and negatively correlated with increased levels of indole-3-lactic acid and indole-3-acetic acid in serum of MN rats. Altered five probiotics and five TPIDs also were observed in patients with IMN. Further studies showed that MN rats exhibited a significant increase in intrarenal mRNA expression of AhR and its target genes CYP1A1, CYP1A2 and CYP1B1, which were accompanied by protein expression of down-regulated cytoplasmic AhR, but up-regulated nuclear AhR, in MN rats and IMN patients., Conclusion and Implications: Activation of the intrarenal AhR signalling pathway may involve five TPIDs. These data suggest that gut microbiota could influence MN through TPIDs that engage host receptors., (© 2023 British Pharmacological Society.)

The health of humans and the planet are the most vital contemporary issues and essential components of the Sustainable Development Goals (SDGs). Scientists and professionals strive for integrated, evolving, healthy, and sustainable solutions encompassing biodiversity and industrial ecology, while offering viable economic attainments. The building industry, especially construction, is an extensive economic counterpart that largely influences health on various levels. On a practical scale, most direct or indirect impacts on health are related to conventional construction systems (CCSs), particularly their materialisations and implementation methods. Therefore, from a global perspective, emerging technologies or remodelled methods to accomplish sustainable use, reuse, and recycling, and improving the planet's health to ensure the wellbeing of its inhabitants, are crucial. The current research is part of a broader study on "programmable construction systems" (PCSs), concentrating on "programmable construction materials" (PCMs) for health. Therefore, issues are reviewed, relevancies are addressed, and health-oriented concepts are discussed. Example concepts of formulation and the simplified toolkit creations follow the problems' sources in a case study, providing insight into the resulting multiscale impacts on real-life practices. The results prove the method's potential and validate its simplicity and applicability through an abstract examination of a newly built case study. Finally, the summarised outcomes of other extensive studies on societal preferences also confirm the feasibility of the hypothesis (i.e., the healthy materialisation) also from a social perspective. [ABSTRACT FROM AUTHOR]

Microsamples are collections usually less than 50 µL, although all devices that we have captured as part of this review do not fit within this definition (as some can perform collections of up to 600 µL); however, they are considered microsamples that can be self-administered. These microsamples have been introduced in pre-clinical, clinical, and research settings to overcome obstacles in sampling via traditional venepuncture. However, venepuncture remains the sampling gold standard for the metabolic phenotyping of blood. This presents several challenges in metabolic phenotyping workflows: accessibility for individuals in rural and remote areas (due to the need for trained personnel), the unamenable nature to frequent sampling protocols in longitudinal research (for its invasive nature), and sample collection difficulty in the young and elderly. Furthermore, venous sample stability may be compromised when the temperate conditions necessary for cold-chain transport are beyond control. Alternatively, research utilising microsamples extends phenotyping possibilities to inborn errors of metabolism, therapeutic drug monitoring, nutrition, as well as sport and anti-doping. Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into the metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the 'haematocrit effect' in dried blood spot microsampling can be overcome. In this review, we provide an overview of the common uses and workflows for microsampling in metabolic phenotyping research. We discuss the advancements in technologies, highlighting key considerations and remaining knowledge gaps for the employment of microsamples in metabolic phenotyping research. This review supports the translation of research from the 'bench to the community'. [ABSTRACT FROM AUTHOR]

The aim of the study was to determine the influence of a key treatment plan and beam parameters on overall dose distribution and on doses in organs laying in further distance from the target during prostate SBRT. Multiple representative treatment plans (n = 12) for TrueBeam and CyberKnife were prepared and evaluated. Nontarget doses were measured with anionization chamber, in a quasi-humanoid phantom at four sites corresponding to the intestines, right lung, thyroid, and head. The following parameters were modified: radiotherapy technique, presence or not of a flattening filter, degree of modulation, and use or not of jaw tracking function for TrueBeam and beam orientation set-up, optimization techniques, and number of MUs for CyberKnife. After usual optimization doses in intestines (near the target) were 0.73% and 0.76%, in head (farthest from target) 0.05% and 0.19% for TrueBeam and CyberKnife, respectively. For TrueBeam the highest peripheral (head, thyroid, lung) doses occurred for the VMAT with the flattening filter while the lowest for 3DCRT. For CyberKnife the highest doses were for gantry with caudal direction beams blocked (gantry close to OARs) while the lowest was the low modulated VOLO optimization technique. The easiest method to reduce peripheral doses was to combine FFF with jaw tracking and reducing monitor units at TrueBeam and to avoid gantry position close to OARs together with reduction of monitor units at CyberKnife, respectively. The presented strategies allowed to significantly reduce out-of-field and nontarget doses during prostate radiotherapy delivered with TrueBeam and CyberKnife. A different approach was required to reduce peripheral doses because of the difference in dose delivery techniques: non-coplanar using CyberKnife and coplanar using TrueBeam, respectively. [ABSTRACT FROM AUTHOR]

Background: Lamin A/C gene (LMNA) mutations frequently cause cardiac and/or skeletal muscle diseases called striated muscle laminopathies. We created a zebrafish muscular laminopathy model using CRISPR/Cas9 technology to target the zebrafish lmna gene. Results: Heterozygous and homozygous lmna mutants present skeletal muscle damage at 1 day post‐fertilization (dpf), and mobility impairment at 4 to 7 dpf. Cardiac structure and function analyses between 1 and 7 dpf show mild and transient defects in the lmna mutants compared to wild type (WT). Quantitative RT‐PCR analysis of genes implicated in striated muscle laminopathies show a decrease in jun and nfκb2 expression in 7 dpf homozygous lmna mutants compared to WT. Homozygous lmna mutants have a 1.26‐fold protein increase in activated Erk 1/2, kinases associated with striated muscle laminopathies, compared to WT at 7 dpf. Activated Protein Kinase C alpha (Pkc α), a kinase that interacts with lamin A/C and Erk 1/2, is also upregulated in 7 dpf homozygous lmna mutants compared to WT. Conclusions: This study presents an animal model of skeletal muscle laminopathy where heterozygous and homozygous lmna mutants exhibit prominent skeletal muscle abnormalities during the first week of development. Furthermore, this is the first animal model that potentially implicates Pkc α in muscular laminopathies. Key Findings: This study presents an animal model of skeletal muscle laminopathy where heterozygous and homozygous lmna mutants exhibit prominent skeletal muscle abnormalities during the first week of development.The heterozygous and homozygous lmna mutants displayed mild and transient cardiac defects during the first week of development.This is the first animal model that potentially implicates Pkc α in muscular laminopathies. [ABSTRACT FROM AUTHOR]

Planar movable tooth drives are compact in structure and simple in processing technology. However, the speed ratio of the single-stage drive is small, whereas the size of the double-stage drive is large. To obtain a planar movable tooth drive with small size and large speed ratio, a two-stage planar movable tooth drive with integrated structure is proposed. For it, the quasi-static force analysis is completed, and the equations of the force distribution and output torque for the drive are derived. According to Hertz's theory of point contact, the equation for the contact stress in the drive is given. Using these equations, the distribution of forces and output torque in the drive is studied. Changes of forces and torque distribution with main parameters are revealed. The contact stress of the drive is calculated under a given load. The finite element analysis software is used to simulate the contact stress of the drive, and the theoretical calculation is verified. [ABSTRACT FROM AUTHOR]

Lipid mediators have been suggested to have a role in pain sensitivity and response; however, longitudinal data on lipid metabolites and persistent multisite musculoskeletal pain (MSMP) are lacking. This study was to identify lipid metabolic markers for persistent MSMP. Lipidomic profiling of 807 lipid species was performed on serum samples of 536 participants from a cohort study. MSMP was measured by a questionnaire and defined as painful sites ≥4. Persistent MSMP was defined as having MSMP at every visit. Logistic regression was used with adjustment for potential confounders. The Benjamini–Hochberg method was used to control for multiple testing. A total of 530 samples with 807 lipid metabolites passed quality control. Mean age at baseline was 61.54 ± 6.57 years and 50% were females. In total, 112 (21%) of the participants had persistent MSMP. Persistent MSMP was significantly associated with lower levels of monohexosylceramide (HexCer)(d18:1/22:0 and d18:1/24:0), acylcarnitine (AC)(26:0) and lysophosphatidylcholine (LPC)(18:1 [sn1], 18:2 [sn1], 18:2 [sn2], and 15-MHDA[sn1] [104_sn1]) after controlling for multiple testing. After adjustment for age, sex, body mass index, comorbidities, and physical activity, HexCer(d18:1/22:0 and d18:1/24:0) and LPC(15-MHDA [sn1] [104_sn1]) were significantly associated with persistent MSMP [Odds Ratio (OR) ranging from 0.25–0.36]. Two lipid classes—HexCer and LPC—were negatively associated with persistent MSMP after adjustment for covariates (OR = 0.22 and 0.27, respectively). This study identified three novel lipid signatures of persistent MSMP, suggesting that lipid metabolism is involved in the pathogenesis of persistent pain. [ABSTRACT FROM AUTHOR]

Background: Postoperative risk stratification is challenging in patients with ST-segment elevation myocardial infarction (STEMI) who undergo percutaneous coronary intervention. This study aimed to characterize the metabolic fingerprints of patients with STEMI with different inhospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model. Methods: Plasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6, 2020 to April 20, 2021. Cohort 1 consisted of 48 survivors and 48 non-survivors. Cohort 2 included 48 patients with unstable angina pectoris, 48 patients with STEMI, and 48 age- and sex-matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. The comprehensive metabolomic data analysis was performed using MetaboAnalyst version 5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves. Results: Metabonomic analysis from the two cohorts showed that patients with STEMI with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting inhospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC = 0.998, 95% CI 0.983–1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969–0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981–0.998). Conclusion: A survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for patients with STEMI. The validation of internal and external cohorts is required. [ABSTRACT FROM AUTHOR]

Emerging evidence indicates that probiotics have been proved to influence liver injury and regeneration. In the present study, the effects of Lactiplantibacillus plantarum AR113 on the liver regeneration were investigated in 70% partial hepatectomy (PHx) rats. Sprague-Dawley (SD) rats were gavaged with L. plantarum AR113 suspensions (1 × 1010 CFU/mL) both before and after partial hepatectomy. The results showed that L. plantarum AR113 administration 2 weeks before partial hepatectomy can accelerate liver regeneration by increased hepatocyte proliferation and tumor necrosis factor-α (TNF-α), hepatocyte growth factor (HGF), and transforming growth factor-β (TGF-β) expression. Probiotic administration enriched Lactobacillus and Bacteroides and depleted Flavonifractor and Acetatifactor in the gut microbiome. Meanwhile, L. plantarum AR113 showed decline of phosphatidylethanolamine (PE), phosphatidylcholine (PC), phosphatidyl serine (PS), and lysophosphatidyl choline (LysoPC) levels in the serum of the rats after the L. plantarum AR113 administration. Moreover, L. plantarum AR113 treated rats exhibited higher concentrations of L-leucine, L-isoleucine, mevalonic acid, and lower 7-oxo-8-amino-nonanoic acid in plasma than that in PHx. Spearman correlation analysis revealed a significant correlation between changes in gut microbiota composition and glycerophospholipid. These results indicate that L. plantarum AR113 is promising for accelerating liver regeneration and provide new insights regarding the correlations among the microbiome, the metabolome, and liver regeneration. [ABSTRACT FROM AUTHOR]

Metabolic markers, offering sensitive information on biological dysfunction, play important roles in diagnosing and treating cancers. However, the discovery of effective markers is limited by the lack of well-established metabolite selection approaches. Here, we propose a network-based strategy to uncover the metabolic markers with potential clinical availability for non-small cell lung cancer (NSCLC). First, an integrated mass spectrometry-based untargeted metabolomics was used to profile the plasma samples from 43 NSCLC patients and 43 healthy controls. We found that a series of 39 metabolites were altered significantly. Relying on the human metabolic network assembled from Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we mapped these differential metabolites to the network and constructed an NSCLC-related disease module containing 23 putative metabolic markers. By measuring the PageRank centrality of molecules in this module, we computationally evaluated the network-based importance of the 23 metabolites and demonstrated that the metabolism pathways of aromatic amino acids and long-chain fatty acids provided potential molecular targets of NSCLC (i.e., IL4l1 and ACOT2). Combining network-based ranking and support-vector machine modeling, we further found a panel of eight metabolites (i.e., pyruvate, tryptophan, and palmitic acid) that showed a high capability to differentiate patients from controls (accuracy > 97.7%). In summary, we present a meaningful network method for metabolic marker discovery and have identified eight strong candidate metabolites for NSCLC diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: To measure the out‐of‐field mean photon energy and dose imparted by the secondary radiation field generated by 6 MV and 6 MV FFF beams using TLD‐300 and TLD‐100 dosimeters and to use the technique to quantify the contributions from the different sources that generate out‐of‐field radiation. Methods: The mean photon energy and the dose were measured using the TLD‐300 glow curve properties and the TLD‐100 response, respectively. The TLD‐300 glow curve shape was energy‐calibrated with gamma rays from 99mTc, 18F, 137Cs, and 60Co sources, and its energy dependence was quantified by a parameter obtained from the curve deconvolution. The TLD‐100 signal was calibrated in absorbed dose‐to‐water inside the primary field. Dosimeters were placed on the linac head, and on the surface and at 4.5 cm depth in PMMA at 1–15 cm lateral distances from a 10 × 10 cm2 field edge at the isocenter plane. Three configurations of dosimeters around the linac were defined to identify and quantify the contributions from the different sources of out‐of‐field radiation. Results: Typical energies of head leakage were about 500 keV for both beams. The mean energy of collimator‐scattered radiation was equal to or larger than 1250 keV and, for phantom‐scattered radiation, mean photon energies were 400 keV for the 6 MV and 300 keV for the 6 MV FFF beam. Relative uncertainties to determine mean photon energy were better than 15% for energies below 700 keV, and 40% above 1000 keV. The technique lost its sensitivity to the incident photon energy above 1250 keV. On the phantom surface and at 1–15 cm from the field edge, 80%–90% of out‐of‐field dose came from scattering in the secondary collimator. At 4.5 cm deep in the phantom and 1–5 cm from the field edge, 50%–60% of the out‐of‐field dose originated in the phantom. At the points of measurement, the head leakage imparted less than 0.1% of the dose at the isocenter. The 6 MV FFF beam imparted 8–36% less out‐of‐field dose than the 6 MV beam. These energy results are consistent with general Monte Carlo simulation predictions and show excellent agreement with simulations for a similar linac. The measured out‐of‐field doses showed good agreement with independent evaluations. Conclusions: The out‐of‐field mean photon energy and dose imparted by the secondary radiation field were quantified by the applied TLD‐300/TLD‐100 method. The main sources of out‐of‐field dose were identified and quantified using three configurations of dosimeters around the linac. This technique could be of value to validate Monte Carlo simulations where the linac head design, configuration, or material composition are unavailable. [ABSTRACT FROM AUTHOR]