Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.

Purpose: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs). Material and methods: The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model. Results: Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively. Conclusion: The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Synergizing economic growth and carbon emission reduction in China: A path to coupling the MFLP and PLUS models for optimizing the territorial spatial functional pattern.

Carbon emissions caused by economic growth are the main cause of global warming, but controlling economic growth to reduce carbon emissions does not meet China's conditions. Therefore, how to synergize economic growth and carbon emission reduction is not only a sustainable development issue for China, but also significant for mitigating global warming. The territorial spatial functional pattern (TSFP) is the spatial carrier for coordinating economic development and carbon emissions, but how to establish the TSFP to synergize economic growth and carbon emission reduction remains unresolved. We propose a decision framework for optimizing TSFP coupled with the multi-objective fuzzy linear programming and the patch-generating land use simulation model, to provide a new path to synergize economic growth and carbon emission reduction in China. To confirm the reliability, we took Qionglai City as the demonstration. The results found a significant spatiotemporal coupling between TSFP and the synergistic effect between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through optimizing TSFP. The urban space of Qionglai City in 2025 and 2030 obtained by the decision framework was 6497.57 hm2 and 6628.72 hm2 respectively, distributed in the central and eastern regions; the rural space was 60,132.92 hm2 and 56,084.97 hm2, concentrated in the east, with a few located in the west; and the ecological space was 71,072.52 hm2 and 74,998.31 hm2, mainly located in the western and southeastern areas. Compared with 2020, the carbon emission intensity of the TSFP that realized the synergy (decoupling index was 0.25 and 0.21, respectively) was reduced by 0.7 and 4.7 tons/million yuan, respectively. Further confirming that optimizing TSFP is an effective way to synergize economic growth and carbon emission reduction, which can provide policy implications for coordinating economic growth and carbon emissions for China and even similar developing countries.

Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease.

Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. Here, we define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. While islets from the majority of donors exhibited the expected relative response magnitudes, with glucose being highest, amino acid moderate, and fatty acid small, 9% of islets stimulated with amino acid and 8% of islets stimulated with fatty acids had larger responses compared with high glucose. We leveraged this insulin response heterogeneity and used transcriptomics and proteomics to identify molecular correlates of specific nutrient responsiveness, as well as those proteins and mRNAs altered in type 2 diabetes. We also examine nutrient-responsiveness in stem cell-derived islet clusters and observe that they have dysregulated fuel sensitivity, which is a hallmark of functionally immature cells. Our study now represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people's islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition. ONE-SENTENCE SUMMARY: Deep phenotyping and multi-omics reveal individualized nutrient-specific insulin secretion propensity.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Brain white matter microstructural alterations in patients with systemic lupus erythematosus: an automated fiber quantification study.

This study aimed to identify damaged segments of brain white matter fiber tracts in patients with systemic lupus erythematosus (SLE) using diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ), and analyze their relationship with cognitive impairment. Clinical and imaging data for 39 female patients with SLE and for 44 female healthy controls (HCs) were collected. AFQ was used to track whole-brain white matter tracts in each participant, and each tract was segmented into 100 equally spaced nodes. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated at each node. Correlations were also explored between DTI metrics in the damaged segments of white matter fiber tracts and neuropsychological test scores of patients with SLE. Compared with HCs, SLE patients exhibited significantly lower FA values, and significantly higher MD, AD, RD values in many white matter tracts (all P < 0.05, false discovery rate-corrected). FA values in nodes 97-100 of the left inferior fronto-occipital fasciculus (IFOF) positively correlated with the mini-mental state examination score. AFQ enables precise and accurate identification of damage to white matter fiber tracts in brains of patients with SLE. FA values in the left IFOF correlate with cognitive impairment in SLE.

Web-based multi-omics integration using the Analyst software suite.

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.

Hepatic Transcriptomic Responses to Ethinylestradiol in Embryonic Japanese Quail and Double-Crested Cormorant.

Understanding species differences in sensitivity to toxicants is a critical issue in ecotoxicology. We recently established that double-crested cormorant (DCCO) embryos are more sensitive than Japanese quail (JQ) to the developmental effects of ethinylestradiol (EE2). We explored how this difference in sensitivity between species is reflected at a transcriptomic level. The EE2 was dissolved in dimethyl sulfoxide and injected into the air cell of eggs prior to incubation at nominal concentrations of 0, 3.33, and 33.3 µg/g egg weight. At midincubation (JQ 9 days; DCCO 16 days), livers were collected from five embryos/treatment group for RNA sequencing. Data were processed and analyzed using EcoOmicsAnalyst and ExpressAnalyst. The EE2 exposure dysregulated 238 and 1,987 genes in JQ and DCCO, respectively, with 78 genes in common between the two species. These included classic biomarkers of estrogen exposure such as vitellogenin and apovitellenin. We also report DCCO-specific dysregulation of Phase I/II enzyme-coding genes and species-specific transcriptional ontogeny of vitellogenin-2. Twelve Kyoto Encyclopedia of Genes and Genomes pathways and two EcoToxModules were dysregulated in common in both species including the peroxisome proliferator-activated receptor (PPAR) signaling pathway and fatty acid metabolism. Similar to previously reported differences at the organismal level, DCCO were more responsive to EE2 exposure than JQ at the gene expression level. Our description of differences in transcriptional responses to EE2 in early life stage birds may contribute to a better understanding of the molecular basis for species differences. Environ Toxicol Chem 2024;43:772-783. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Implanting Iodine-125 Seed Strand Inside the Portal Vein Stent: An Improved Approach to Endovascular Brachytherapy for Treatment of Patients with Hepatocellular Carcinoma and Main Portal Vein Tumor Thrombus.

Purpose: To investigate the feasibility and efficacy of implanting an iodine-125 (125I) seed strand inside a portal vein stent (PVS) in the treatment of patients with hepatocellular carcinoma (HCC) and main portal vein tumor thrombus (mPVTT). Patients and Methods: Twenty-three patients who diagnosed with HCC and mPVTT and underwent endovascular implantation 125I seed strands and portal vein stenting were included in this study. Patients were divided into two groups. For patients in group A (n = 12), the 125I seed strand was placed outside the PVS, and for those in group B (n = 11), the strand was placed inside the PVS. Technical success, pain intensity during the procedure (numeric rating scale), procedure-related complications, changes in liver function, stent patency, and survival rates were recorded and analyzed. Results: The procedures were successful in all patients, and no serious procedure-related complications occurred in either group. Pain intensity during the procedure was significantly lower in group B than in group A (2.64 ± 1.50 vs 4.08 ± 1.78, p = 0.048), and there were no significant differences between pre- and post-procedure liver function in either group. The median duration of stent patency was 9 months (95% CI 2.21-15.79 months) in group A and 12 months (95% CI 3.63-18.37 months) in group B (p = 0.670). Median survival was 12 months (95% CI 10.30-13.70 months) in group A and 13 months (95% CI 10.03-15.97 months) in group B (p = 0.822). The cumulative stent patency and survival rates at 3, 6, and 12 months were 75%, 50%, and 41.7%, and 83.3%, 75%, and 50% in group A and 72.7%, 62.3%, and 31.2%, and 90.9%, 80.8%, and 50.5%, respectively. Conclusion: Implantation of 125I seed strand inside the PVS is effective and feasible for treating patients with HCC and mPVTT.

Comparing Transcriptomic Responses to Chemicals Across Six Species Using the EcoToxChip RNASeq Database.

The EcoToxChip project includes RNA-sequencing data from experiments involving model (Japanese quail, fathead minnow, African clawed frog) and ecological (double-crested cormorant, rainbow trout, northern leopard frog) species at multiple life stages (whole embryo and adult) exposed to eight chemicals of environmental concern known to perturb a wide range of biological systems (ethinyl estradiol, hexabromocyclododecane, lead, selenomethionine, 17ß trenbolone, chlorpyrifos, fluoxetine, and benzo[a]pyrene). The objectives of this short communication were to (1) present and make available this RNA-sequencing database (i.e., 724 samples from 49 experiments) under the FAIR principles (FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability), while also summarizing key meta-data attributes and (2) use ExpressAnalyst (including the Seq2Fun algorithm and EcoOmicsDB) to perform a comparative transcriptomics analysis of this database focusing on baseline and differential transcriptomic changes across species-life stage-chemical combinations. The database is available in NCBI GEO under accession number GSE239776. Across all species, the number of raw reads per sample ranged between 13 and 58 million, with 30% to 79% of clean reads mapped to the "vertebrate" subgroup database in EcoOmicsDB. Principal component analyses of the reads illustrated separation across the three taxonomic groups as well as some between tissue types. The most common differentially expressed gene was CYP1A1 followed by CTSE, FAM20CL, MYC, ST1S3, RIPK4, VTG1, and VIT2. The most common enriched pathways were metabolic pathways, biosynthesis of cofactors and biosynthesis of secondary metabolites, and chemical carcinogenesis, drug metabolism, and metabolism of xenobiotics by cytochrome P450. The RNA-sequencing database in the present study may be used by the research community for multiple purposes, including, for example, cross-species investigations, in-depth analyses of a particular test compound, and transcriptomic meta-analyses. Environ Toxicol Chem 2024;00:1-6. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Constructing a Deep Learning Radiomics Model Based on X-ray Images and Clinical Data for Predicting and Distinguishing Acute and Chronic Osteoporotic Vertebral Fractures: A Multicenter Study.

RATIONALE AND OBJECTIVES: To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs). METHODS: A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA). RESULTS: Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models. CONCLUSION: Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram.

Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms.

ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.

The value of color Doppler ultrasonography combined with serum tumor markers in differential diagnosis of gastric stromal tumor and gastric cancer.

This study aimed to explore the value of color Doppler ultrasonography combined with carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) in differential diagnosis of gastric stromal tumor (GST) and gastric cancer (GC). An analysis of the clinical data of 180 patients with clinically suspected gastric space occupying lesions. According to the postoperative pathological results, 180 suspected gastric space-occupying lesion patients were divided into GST group (n = 83) and GC group (n = 97). Color Doppler ultrasonography, serum tumor markers CEA and CA19-9 were compared. The research results showed that serum CEA and CA19-9 levels were lower in patients with GST group than those with GC group (both P < 0.001). With postoperative pathology as the gold standard, detection rates of GST and GC by combination of color Doppler ultrasound (CDUS), serum CEA, and CA19-9 were higher than those of each index alone (both P < 0.001). There was no difference between detection rates of GST and GC by combination of CDUS, serum CEA, and CA19-9 (P = 0.058). Color Doppler ultrasonography combined with serum tumor markers CEA and CA19-9 tests has a certain differential diagnostic value for GST and GC, which may provide a reliable reference basis for clinical diagnosis and treatment.

Functional Brain Network Alterations in Patients With Systemic Lupus Erythematosus With Different Cognitive Function States: A Graph Theory Analysis Study.

OBJECTIVE: The study aimed to investigate the characteristics of brain functional network disruption in patients with systemic lupus erythematosus (SLE) with different cognitive function states by using graph theory analysis and to explore their relationship with clinical data and neuropsychiatric scales. METHODS: Resting-state functional magnetic resonance imaging data were collected from 38 female SLE patients and 44 healthy controls. Based on Montreal Cognitive Assessment (MoCA) scores, SLE patients were divided into a high MoCA group (MoCA-H; MoCA score, ≥26) and a low MoCA group (MoCA-L; MoCA score, <26). The matrix of resting-state functional brain networks of subjects in the 3 groups was constructed by using the graph theory approach. The topological properties of the functional brain networks, including global and local metrics, in the 3 groups were calculated. The differences in the topological properties of networks between the 3 groups were compared. In addition, Spearman correlation analysis was used to explore the correlation between altered topological properties of brain networks and clinical indicators, as well as neuropsychiatric scales in SLE patients in the MoCA-L group. RESULTS: At the global level, in the sparsity threshold range of 0.10 to 0.34, the values of small-world properties were greater than 1 in all 3 groups, indicating that functional brain networks of both 3 groups had small-world properties. There were statistically significant differences in the characteristic path length, global, and local efficiency between 3 groups (F = 3.825, P = 0.0260; F = 3.722, P = 0.0285; and F = 3.457, P = 0.0364, respectively). Systemic lupus erythematosus patients in the MoCA-L group showed increased characteristic path length (t = 2.816, P = 0.00651), decreased global (t = -2.729, P = 0.00826), and local efficiency (t = -2.623, P = 0.0109) compared with healthy controls. No statistically significant differences in local metrics were found between the MoCA-H group and the healthy control, MoCA-L groups. At the local level, there was statistically significant difference in the node efficiency among the 3 groups (P < 0.05 after Bonferroni correction). Compared with healthy controls, SLE patients in the MoCA-L group showed decreased node efficiency in left anterior cingulate paracingulate gyrus, bilateral putamen, bilateral pallidum, and left Heschl gyrus. No statistically significant differences in the local metrics were found between the MoCA-H, MoCA-L, and healthy control groups. Correlation analysis in SLE patients in the MoCA-L group showed that the characteristic path length was positively correlated with C4 levels (r = 0.587, P = 0.007), the global and local efficiencies were negatively correlated with C4 levels (r = -0.599, P = 0.005; r = -0.599, P = 0.005, respectively), and the node efficiency in the bilateral putamen was negatively correlated with C4 levels (r = -0.611, P = 0.004; r = -0.570, P = 0.009). The node efficiency in the left pallidum was negatively correlated with disease duration (r = -0.480, P = 0.032). The node efficiency in the left Heschl gyrus was negatively correlated with IgM levels (r = -0.478, P = 0.033). No correlation was noted between other network metrics, clinical indicators, and neuropsychological scales. CONCLUSIONS: The topological properties of functional brain networks were disrupted in SLE patients with low MoCA scores, suggesting that altered topological properties of the brain networks were associated with cognitive function in SLE patients. Correlation between altered topological properties of the brain networks and clinical indicators was noted in SLE patients with low MoCA scores, suggesting that altered topological properties of brain networks in SLE patients may have clinical significance as imaging markers for monitoring disease changes in patients with SLE.

A phenome-wide association study (PheWAS) to identify the health impacts of 4-cresol sulfate in the Nagahama Study.

Gut-microbiota derived metabolites are important regulators of host biology and metabolism. To understand the impacts of the microbial metabolite 4-cresol sulfate (4-CS) on four chronic diseases [type 2 diabetes mellitus, metabolic syndrome (MetS), non-alcoholic fatty liver disease, and chronic kidney disease (CKD)], we conducted association analyses of plasma 4-CS quantified by liquid chromatography coupled to mass spectrometry (LC-MS) in 3641 participants of the Nagahama study. Our results validated the elevation of 4-CS in CKD and identified a reducing trend in MetS. To delineate the holistic effects of 4-CS, we performed a phenome-wide association analysis (PheWAS) with 937 intermediate biological and behavioral traits. We detected associations between 4-CS and 39 phenotypes related to blood pressure regulation, hepatic and renal functions, hematology, sleep quality, intraocular pressure, ion regulation, ketone and fatty acid metabolisms, disease history and dietary habits. Among them, 19 PheWAS significant traits, including fatty acids and 14 blood pressure indices, were correlated with MetS, suggesting that 4-CS is a potential biomarker for MetS. Consistent associations of this gut microbial-derived metabolite on multiple endophenotypes underlying distinct etiopathogenesis support its role in the overall host health, with prospects of probiotic-based therapeutic solutions in chronic diseases.

The Native Microbiome Member Chryseobacterium sp. CHNTR56 MYb120 Induces Trehalose Production via a Shift in Central Carbon Metabolism during Early Life in C. elegans.

Aging is the system-wide loss of homeostasis, eventually leading to death. There is growing evidence that the microbiome not only evolves with its aging host, but also directly affects aging via the modulation of metabolites involved in important cellular functions. The widely used model organism C. elegans exhibits high selectivity towards its native microbiome members which confer a range of differential phenotypes and possess varying functional capacities. The ability of one such native microbiome species, Chryseobacterium sp. CHNTR56 MYb120, to improve the lifespan of C. elegans and to promote the production of Vitamin B6 in the co-colonizing member Comamonas sp. 12022 MYb131 are some of its beneficial effects on the worm host. We hypothesize that studying its metabolic influence on the different life stages of the worm could provide further insights into mutualistic interactions. The present work applied LC-MS untargeted metabolomics and isotope labeling to study the impact of the native microbiome member Chryseobacterium sp. CHNTR56 MYb120 on the metabolism of C. elegans. In addition to the upregulation of biosynthesis and detoxification pathway intermediates, we found that Chryseobacterium sp. CHNTR56 MYb120 upregulates the glyoxylate shunt in mid-adult worms which is linked to the upregulation of trehalose, an important metabolite for desiccation tolerance in older worms.

Spectral CT - a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma.

BACKGROUND: Spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies. This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation. METHODS: There were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40 keV-equivalent image (VMI40keV), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λHU) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups. RESULTS: There were significant differences in VMI40keV, IC, Eff-Z, and λHU in AP and VP among the three groups (all p < 0.05) except for WC (p > 0.05). The VMI40keV, IC, Eff-Z, and λHU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p < 0.05). In the ROC analysis, IC performed the best in the identification of the poorly differentiated group and non-poorly differentiated group in VP (AUC = 0.729, Sensitivity = 0.829, and Specificity = 0.569 under the threshold of 21.08 mg/ml). CONCLUSIONS: Quantitative parameters of spectral CT could offer supplemental information for the preoperative differential diagnosis of ESCC with different degrees of differentiation.

mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite-Phenotype Associations.

Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites, genes and phenotypes for hypothesis generation. It has become clear that identifying potential causal relationships between metabolites and phenotypes, as well as providing deep functional insights, are crucial for further downstream applications. Here, we introduce mGWAS-Explorer 2.0 to support the causal analysis between >4000 metabolites and various phenotypes. The results can be interpreted within the context of semantic triples and molecular quantitative trait loci (QTL) data. The underlying R package is released for reproducible analysis. Using two case studies, we demonstrate that mGWAS-Explorer 2.0 is able to detect potential causal relationships between arachidonic acid and Crohn's disease, as well as between glycine and coronary heart disease.

MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data.

Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly bioinformatics tools to reveal key patterns, important biomarkers, and potential activities. Here we introduce MicrobiomeAnalyst 2.0 to support comprehensive statistics, visualization, functional interpretation, and integrative analysis of data outputs commonly generated from microbiome studies. Compared to the previous version, MicrobiomeAnalyst 2.0 features three new modules: (i) a Raw Data Processing module for amplicon data processing and taxonomy annotation that connects directly with the Marker Data Profiling module for downstream statistical analysis; (ii) a Microbiome Metabolomics Profiling module to help dissect associations between community compositions and metabolic activities through joint analysis of paired microbiome and metabolomics datasets; and (iii) a Statistical Meta-Analysis module to help identify consistent signatures by integrating datasets across multiple studies. Other important improvements include added support for multi-factor differential analysis and interactive visualizations for popular graphical outputs, updated methods for functional prediction and correlation analysis, and expanded taxon set libraries based on the latest literature. These new features are demonstrated using a multi-omics dataset from a recent type 1 diabetes study. MicrobiomeAnalyst 2.0 is freely available at microbiomeanalyst.ca.

Indole-3-Propionic Acid, a Gut Microbiota Metabolite, Protects Against the Development of Postoperative Delirium.

OBJECTIVE: The aim was to determine preoperative gut microbiota metabolites that may be associated with postoperative delirium (POD) development in patients and further study in rodents. SUMMARY BACKGROUND DATA: POD occurs in 9% to 50% of older patients undergoing anesthesia/surgery but lacks effective treatments or prevention. High-throughput metabolomics using liquid chromatography with tandem mass spectrometry has accelerated disease-related biomarkers discovery. We performed metabolomic studies in humans to identify potential metabolite biomarkers linked to POD and examined potential mechanisms in rodents. METHODS: We performed a prospective observational cohort study to examine the metabolomic changes that were associated with the development of POD. Then the gut microbiota-related metabolomic changes were recapitulated by gut microbiota perturbation in rodents. POD was assessed in mice using a battery of behavioral tests including novel objective test, Y-maze test, open-field test, and buried food test. The mechanisms through which gut microbiota-related metabolomic changes influenced POD were examined using chemogenetics. RESULTS: Indole-3-propionic acid (IPA) is a gut microbiota metabolite that belongs to the indole family. Baseline plasma levels of IPA were significantly inversely correlated with the onset of POD in 103 (17 cases) human individuals. This relationship was validated in preclinical mouse models for POD: reducing IPA levels through gut microbiota perturbation promoted POD-like behavior. More importantly, IPA administration deterred POD-like behavior. Colonization of germ-free mice with mutant Clostridium sporogenes that did not produce IPA-promoted POD-like behavior. Chemogenetic studies revealed that the protective effect of IPA in mice was mediated, in part, by peroxisome proliferator-activated receptor gamma coactivator 1-alpha in hippocampal interneurons. CONCLUSIONS: Gut microbiota-derived IPA is an important molecule implicated in the pathogenesis of POD, which could potentially be harnessed for POD prevention.