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Deuterium metabolic imaging (DMI) is a non-invasive magnetic resonance spectroscopic imaging technique enabling in vivo mapping of glucose metabolism. Dynamic DMI provides time-resolved metabolite maps and allows spatially resolved fitting of metabolic models to capture metabolite concentration dynamics. However, conventional fitting tools often require long processing times for high-resolution datasets, limiting their practical utility. To address this bottleneck, we propose a deep autoencoder (DAE) for rapid spectral–temporal fitting of dynamic DMI data, supporting arbitrary parametric model constraints to describe metabolite concentration dynamics. The DAE was benchmarked against spectral–temporal fitting using FSL-MRS and LCModel. Fitting accuracy was evaluated on in vivo and synthetic whole-brain dynamic DMI data acquired at 7T using Bland–Altman metrics, Pearson correlation coefficients, structural similarity index measures, and root mean squared errors for both metabolite concentrations and model constraint parameters. The DAE achieved processing times of 0.29 ms per voxel, corresponding to an acceleration of more than three orders of magnitude compared to LCModel/FSL-MRS (0.55/0.65 s per voxel). On in vivo data, it showed excellent agreement with LCModel, and on synthetic data, it consistently outperformed both reference methods across all evaluation metrics. The proposed DAE enables accurate spectral–temporal fitting for whole-brain dynamic DMI scans within less than a second, matching or exceeding the performance of conventional state-of-the-art fitting methods. This makes it a promising tool for integration into efficient post-processing pipelines for research and clinical DMI workflows. [Display omitted] •Deep Autoencoder approach for dynamic fitting of dynamic DMI data.•Proposed model shows strong agreement with reference standard on in vivo data.•Proposed model outperforms reference methods on synthetic fitting accuracy.•Proposed model fits whole-brain dynamic DMI datasets in under one second.•Proposed model achieves >1000× speedup over reference methods.

Understanding protein fermentation in the hindgut of pigs is essential due to its implications for health, and ileal digesta is commonly used to study this process in vitro. This study aimed to assess the feasibility of utilising in vitro digested residues as a replacement for ileal digesta in evaluating the protein fermentation potential. In vitro residues from cottonseed meal, maize germ meal, peanut meal, rapeseed cake, rapeseed meal, soyabean meal and sunflower meal were analysed using a modified gas production (GP) technique and curve fitting model to determine their fermentation dynamics and compare with the use of ileal digesta. Significant variations were observed in GP parameters between in vitro digested residues, indicating differences in nitrogen utilisation by fecal microbiota. Soyabean meal and sunflower meal exhibited the highest maximum GP rates (Rmax), with values of 29·5 ± 0·6 and 28·0 ± 1·2 ml/h, respectively, while maize germ meal showed slowest protein utilisation (17·3 ± 0·2 ml/h). A positive relationship was found between the Rmax of in vitro residues and ileal digesta (R2 = 0·85, P < 0·01). However, GP potential (GPs) showed a tendency for a negative relationship (R2 = 0·39, P < 0·1), likely due to narrow observed GPs values and the presence of varied endogenous proteins in ileal digesta. Our results demonstrate the potential of using in vitro digested residues as a substitute for ileal digesta in assessing the fermentation potential of protein ingredients, particularly regarding the rate of protein fermentation.

NMR spectroscopy is uniquely suited for atomic resolution studies of biomolecules such as proteins, nucleic acids and metabolites, since detailed information on structure and dynamics are encoded in positions and line shapes of peaks in NMR spectra. Unfortunately, accurate determination of these parameters is often complicated and time consuming, in part due to the need for different software at the various analysis steps and for validating the results. Here, we present an integrated, cross-platform and open-source software that is significantly more versatile than the typical line shape fitting application. The software is a completely redesigned version of PINT ( https://pint-nmr.github.io/PINT/ ). It features a graphical user interface and includes functionality for peak picking, editing of peak lists and line shape fitting. In addition, the obtained peak intensities can be used directly to extract, for instance, relaxation rates, heteronuclear NOE values and exchange parameters. In contrast to most available software the entire process from spectral visualization to preparation of publication-ready figures is done solely using PINT and often within minutes, thereby, increasing productivity for users of all experience levels. Unique to the software are also the outstanding tools for evaluating the quality of the fitting results and extensive, but easy-to-use, customization of the fitting protocol and graphical output. In this communication, we describe the features of the new version of PINT and benchmark its performance.

Many metabolomics, and other high-content or high-throughput, experiments are set up such that the primary aim is the discovery of biomarker metabolites that can discriminate, with a certain level of certainty, between nominally matched 'case' and 'control' samples. However, it is unfortunately very easy to find markers that are apparently persuasive but that are in fact entirely spurious, and there are well-known examples in the proteomics literature. The main types of danger are not entirely independent of each other, but include bias, inadequate sample size (especially relative to the number of metabolite variables and to the required statistical power to prove that a biomarker is discriminant), excessive false discovery rate due to multiple hypothesis testing, inappropriate choice of particular numerical methods, and overfitting (generally caused by the failure to perform adequate validation and cross-validation). Many studies fail to take these into account, and thereby fail to discover anything of true significance (despite their claims). We summarise these problems, and provide pointers to a substantial existing literature that should assist in the improved design and evaluation of metabolomics experiments, thereby allowing robust scientific conclusions to be drawn from the available data. We provide a list of some of the simpler checks that might improve one's confidence that a candidate biomarker is not simply a statistical artefact, and suggest a series of preferred tests and visualisation tools that can assist readers and authors in assessing papers. These tools can be applied to individual metabolites by using multiple univariate tests performed in parallel across all metabolite peaks. They may also be applied to the validation of multivariate models. We stress in particular that classical p-values such as “p < 0.05”, that are often used in biomedicine, are far too optimistic when multiple tests are done simultaneously (as in metabolomics). Ultimately it is desirable that all data and metadata are available electronically, as this allows the entire community to assess conclusions drawn from them. These analyses apply to all high-dimensional 'omics' datasets.

The aim of this study was mainly to investigate the association between Serum uric acid (SUA) to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) and all-cause mortality in hypertensive patients,and to further investigate the mediating role of neutrophils. Our cohort study included 4533 hypertensive patients drawn from the 2005-2018 National Health and Nutrition Examination Survey (NHANES) database and combined with the National Death Index (NDI) database to obtain mortality data for subjects. Kaplan-Meier survival curves, multifactorial Cox risk-proportional modeling, restricted cubic spline (RCS)-based smoothed curve fitting, threshold effects analysis, and subgroup analyses were performed to evaluate the associations between UHR and all-cause mortality, and, finally,causal mediating effects were performed to analyze the mediating role of neutrophils. Over a mean duration of 90.32 months, the follow-up all-cause mortality occurred in 1003 individuals, and the mean age of all subjects included was (61.69 ± 14.28) years, and the Kaplan-Meier survival curves demonstrated that high levels of UHR were notably connected to lower survival. In multivariate Cox regression analysis, high quartile UHR was positively connected to all-cause mortality (HR: 1.36, 95% CI: 1.03,1.80, P = 0.031), and smoothed curve fitting combined with threshold effect analysis showed a nonlinear relationship between UHR and all-cause mortality, with a curve inflection point of 0.14, i.e., when UHR < 0.14, an increase in UHR did not affect the increase in all-cause mortality (HR: 0.84, 95% CI: 0.06, 11.51, P = 0.8968), and when UHR > 0.14, the all-cause mortality increased with the increase in UHR. We further stratify by gender and find that the inflection point for male UHR is 0.13, the suggesting that the association between UHR and all-cause mortality increased with increasing UHR when UHR was < 0.13, HR (95% CI): 0.01 (0.00, 0.22), P < 0.01 and when UHR > 0.13, HR (95% CI): 0.41 (0.04, 1.36), P < 0.01. However there was a significant linear correlation for females (HR: 1.31 95% CI: 0.15, 11.55, P < 0.0001). Analysis of causal mediating effects elucidated that the proportion of neutrophils mediating the association between UHR and all-cause mortality was 18.63%. There was a significant positive correlation between elevated UHR and all-cause mortality in hypertensive patients, and this association may be mediated with neutrophils.

•Permanent MCAO mice exhibited increased GuanCEST in stroke lesions with low B1 CEST, along with a significant decrease in Guan and amide CEST with high B1, due to pH changes.•AmineCEST is a highly sensitive MRI contrast for detecting reperfusion damage at high MRI fields, as demonstrated in transient MCAO mice.•At lower B1 values, the changes in GuanCEST within stroke lesions were primarily driven by increased creatine concentrations in permanent MCAO mice, which remained stable in transient MCAO mice.•While Guan and amineCEST are highly sensitive in delineating stroke lesions, amideCEST is better suited for precise pH mapping. This study aims to investigate the variations in guanidino (Guan), amine and amide chemical exchange saturation transfer (CEST) contrasts in ischemic stroke using permanent middle cerebral artery occlusion (pMCAO) and transient MCAO (tMCAO) models at high (9.4T) and clinical (3T) MRI fields. CEST contrasts were extracted using the Polynomial and Lorentzian Line-shape Fitting (PLOF) method. Both pMCAO and tMCAO models were utilized to examine the B1-dependence patterns and pH sensitivity of the different CEST contrasts in ischemic lesions compared to contralateral region. At 9.4T, GuanCEST showed the highest signal in the contralateral hemisphere for both stroke models, followed by lower signals from amideCEST and amineCEST, with maximum signals at B1=1.2 μT for all CEST contrasts. In both stroke models, GuanCEST exhibited a significant decrease of 1.15–1.5 % in stroke lesions compared to the contralateral hemisphere (ΔGuanCEST) at an optimal B1 range of 1.2–1.6 μT at 9.4T. This represents more than double the pH sensitivity compared to amideCEST, which showed a reduction of 0.5–0.62 % under the same B1 conditions. In the tMCAO model, amineCEST increased by 3.85 % in the stroke lesion compared to the contralateral hemisphere at an optima B1 range of 1.6–2.5 μT. In contrast, for the pMCAO model, amineCEST increased by 0.87–1.0 % in the stroke lesion. At lower B1 values (<0.8 μT at 9.4T and <0.4 μT at 3T), the GuanCEST changes in the stroke lesion were dominated by creatine concentration changes, which increased in the pMCAO and remained stable in the tMCAO. While GuanCEST and amineCEST are highly sensitive for delineating stroke lesions, amideCEST is more suitable for precise pH mapping as it is not influenced by metabolite changes within the stroke lesion. Additionally, at low B1 values, amideCEST and GuanCEST can be used to map protein and creatine concentrations separately, since they are independent of pH changes at these lower B1 values. Lastly, amineCEST serves as a highly sensitive MRI contrast for detecting reperfusion damage at high MRI fields.

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed '-omics' techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.

Exposure to environmental pollutants is increasingly recognized as a risk factor for the development of psoriasis. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the air and might induce reactions such as oxidative stress. Nevertheless, it is still unclear if PAHs have any influence on the prevalence of psoriasis over the entire population of the United States. The objective of this study was to assess the association between urine PAHs and psoriasis. The research included 3,673 individuals aged 20 years or older who participated in the 2003-2006 and 2009-2012 National Health and Nutrition Examination Surveys (NHANES). We employed logistic regression models to evaluate the relationship between levels of urine PAH metabolites and psoriasis and smoothed curve fitting to illustrate the concentration-response relationship. Additionally, subgroup and interaction analyses were conducted to elucidate these associations. Furthermore, we employed weighted quartile sum (WQS) regressions to examine the distinct effects of individual and mixed urine PAH metabolites on psoriasis. However, it is important to note that the NHANES sample may be subject to selectivity and self-reporting bias, which may influence the data' generalisability. We observed that the highest tertiles of 2-NAP and 2-FLU had a 63% (95% CI 1.02, 2.61) and 83% (95% CI 1.14, 2.96) higher odds of association with psoriasis prevalence, respectively. Meanwhile, tertile 2 and tertile 3 of 3-PHE were also significantly associated with psoriasis, with higher odds of 65% (95% CI 1.01, 2.69) and 14% (95% CI 1.17, 3.00), respectively. The subgroup analyses revealed a significant correlation between urine PAH metabolites and the odds of psoriasis in specific groups, including males, aged 40-60 years, with a BMI > 30, and those with hyperlipidemia. In the WQS model, a positive association was found between the combination of urine PAH metabolites and psoriasis (OR 1.43, 95% CI 1.11, 1.84), with 2-FLU being the most prevalent component across all mixtures (0.297). Our findings indicate a significant association between urine PAH metabolites and the odds of psoriasis prevalence in adults. Among these metabolites, 2-FLU demonstrated the most prominent impact. Controlling PAH exposure, as an important strategy for minimizing exposure to environmental contaminants and lowering the risk of psoriasis, is critical for raising public knowledge about environmental health and preserving public health.

PurposeWe report findings from the first-in-human study of [11C]MDTC, a radiotracer developed to image the cannabinoid receptor type 2 (CB2R) with positron emission tomography (PET).MethodsTen healthy adults were imaged according to a 90-min dynamic PET protocol after bolus intravenous injection of [11C]MDTC. Five participants also completed a second [11C]MDTC PET scan to assess test-retest reproducibility of receptor-binding outcomes. The kinetic behavior of [11C]MDTC in human brain was evaluated using tissue compartmental modeling. Four additional healthy adults completed whole-body [11C]MDTC PET/CT to calculate organ doses and the whole-body effective dose.Results[11C]MDTC brain PET and [11C]MDTC whole-body PET/CT was well-tolerated. A murine study found evidence of brain-penetrant radiometabolites. The model of choice for fitting the time activity curves (TACs) across brain regions of interest was a three-tissue compartment model that includes a separate input function and compartment for the brain-penetrant metabolites. Regional distribution volume (VT) values were low, indicating low CB2R expression in the brain. Test-retest reliability of VT demonstrated a mean absolute variability of 9.91%. The measured effective dose of [11C]MDTC was 5.29 μSv/MBq.ConclusionThese data demonstrate the safety and pharmacokinetic behavior of [11C]MDTC with PET in healthy human brain. Future studies identifying radiometabolites of [11C]MDTC are recommended before applying [11C]MDTC PET to assess the high expression of the CB2R by activated microglia in human brain.

Environmental tobacco smoke (ETS) exposure has been shown to be associated with a variety of diseases, but evidence regarding the association between it and urinary incontinence (UI) is limited. Cotinine, a metabolite of nicotine in the human body, can more accurately quantify the level of human exposure to tobacco smoke. The study utilized data from seven survey cycles (2007-March 2020 Pre-pandemic) of the National Health and Nutrition Examination Survey (NHANES) program. Weighted multivariable logistic regression analysis, subgroup analysis, interaction tests, smooth curve fitting, and threshold effect models were used to analyze the relationship between serum cotinine and UI. Additionally, a 1:1 nearest neighbor propensity score matching (PSM) method was employed to minimize the impact of confounding factors. Before and after PSM, serum cotinine levels were higher in individuals with UI than those without ( P  < 0.05). Both before and after PSM, UI was positively correlated with serum cotinine levels, with a significantly increased risk of urinary incontinence when serum cotinine levels were in the Q3 range (before PSM: OR = 1.89, 95% CI = 1.59–2.24; after PSM: OR = 1.60, 95% CI = 1.28-2.00). Smooth curve fitting before and after PSM showed an approximate J-shaped non-linear dose-response relationship between log-transformed serum cotinine levels and UI. This study indicates that among American adults, there is a positive relationship between serum cotinine levels and UI, which is also significant in self-reported non-smoking populations. Therefore, reducing exposure to environmental tobacco smoke (e.g., avoiding second-hand smoke) in work and daily life may help alleviate the occurrence of UI, and serum cotinine levels have the potential to be a tool for predicting the degree of risk of developing UI.