Journal: medRxiv

Article Title: The Diurnal Rhythmicity of Untargeted Metabolomic Profiles in Humans

doi: 10.64898/2026.04.28.26347027

Figure Lengend Snippet: a . Forest plot of 60 blood-draw time associated serum metabolites identified in the primary combined sex models. In blue, is the cosine time of blood draw-associated metabolites and in green the sine time of blood-draw associated metabolites. Metabolites that exhibit significance in the 2df joint test (which jointly assesses both sine and cosine effects of time) are in red font. b . Pathway enrichment analysis results (false discovery rate < 0.05) for the complete set of 84 time of blood draw-associated serum metabolites discovered across combined samples, sex or age-stratified analyses, and/or primary and sensitivity models. c . Chronotype gene-metabolite connections identified by network analysis across all 84 blood draw-time associated metabolites. Pink hexagons depict serum metabolites, and green circles depict chronotype genes. d . Scatter plot of 672 serum metabolites in five clusters identified by HDBSCAN in UMAP dimensions. e . Cluster average LOWESS trends across time of blood draw. The overlaid dashed black line represents the average trajectory of metabolites with positive cosine similarity to the cluster mean and a dashed gray line for those with negative cosine similarity.

Article Snippet: Serum metabolite concentrations from baseline fasting blood samples retrieved during the day were quantified by the Metabolon, Inc.

Techniques:

Journal: medRxiv

Article Title: The Diurnal Rhythmicity of Untargeted Metabolomic Profiles in Humans

doi: 10.64898/2026.04.28.26347027

Figure Lengend Snippet:

Article Snippet: Serum metabolite concentrations from baseline fasting blood samples retrieved during the day were quantified by the Metabolon, Inc.

Techniques:

Journal: medRxiv

Article Title: The Diurnal Rhythmicity of Untargeted Metabolomic Profiles in Humans

doi: 10.64898/2026.04.28.26347027

Figure Lengend Snippet: a . Fuji plot of 673 significant metabolite-trait associations (51 metabolites across 58 health traits) with adjustment of blood draw time. The outermost track displays health trait categories, and their corresponding legend color. The ‘Other’ trait category comprises of Dental, Sleep, Respiratory, Neuropsychological, and Quality of Life traits. The top five or top one most significant metabolite(s) (according to p-value significance) are annotated in the outermost track with their name, with the colored square indicating metabolite type. Metabolite types are separated each into a designated separate scatter plot track, with the tracks colored according to the legend in the bottom left. The bar plot in the upper right quadrant showcases total metabolite-trait associations identified, according to metabolite type. The following abbreviations are used: PUFA: polyunsaturated fatty acid, MUFA: monounsaturated fatty acid, SFA: saturated fatty acid, and FA: fatty acid. b . Natural log-scaled ratios comparing metabolite-trait association effect sizes from models adjusting for blood draw time, to models not adjusting for blood draw time. c . Non-zero counts of metabolite-trait associations which exhibit directional disagreement in effect size estimates between models.

Article Snippet: Serum metabolite concentrations from baseline fasting blood samples retrieved during the day were quantified by the Metabolon, Inc.

Techniques:

Journal: medRxiv

Article Title: The Diurnal Rhythmicity of Untargeted Metabolomic Profiles in Humans

doi: 10.64898/2026.04.28.26347027

Figure Lengend Snippet: a . Type 1 error under α=0.05 across three outcome scenarios (constant, in-phase with metabolite, and π/2 phase-shifted) and three causal metabolite compositions (circadian:non-circadian = 20:0, 10:10, 2:18) from the simulation analyses. b . Type 2 error under α=10 -4 across the same outcome scenarios and metabolite compositions.

Article Snippet: Serum metabolite concentrations from baseline fasting blood samples retrieved during the day were quantified by the Metabolon, Inc.

Techniques:

Journal: PLOS One

Article Title: In vivo mouse model of calcific myonecrosis induced by injury

doi: 10.1371/journal.pone.0346816

Figure Lengend Snippet: (a-c) Quantitative RT-PCR analysis of Nlrp3 (a) , Asc (b) , and Caspase-1 (c) mRNA expression in skeletal muscle from control and notexin-injected mice (n = 3). Data are presented as mean ± S.D. Statistical significance was assessed using unpaired t-test (** P < 0.01, *** P < 0.001). (d) Immunostaining of gastrocnemius muscle sections 3 days after notexin injection using anti-NLRP3, anti-ASC, and anti-Caspase-1 antibodies on serial sections. The upper panels show control muscles, and the lower panels show notexin-treated muscles. Scale bars, 20 μm. Asterisks indicate calcified muscle fibers. (e) Immunofluorescence staining showing the localization of NLRP3 (green), ASC or Caspase-1 (red) around calcified muscle fibers in notexin-injected skeletal muscle. Nuclei are counterstained with DAPI (blue). Scale bar, 20 μm. Asterisks indicate calcified muscle fibers. (f, g) Quantitative RT-PCR analysis of Il-1β (f) and Il-18 (g) mRNA expression in skeletal muscle from control and notexin-injected mice (n = 3). Data are presented as mean ± S.D. Statistical significance was assessed using unpaired t-test (*** P < 0.001). (h) Serum IL-18 levels measured by ELISA in control and notexin-injected mice (n = 6 (control), n = 9 (notexin)). Data are presented as mean ± S.D. Statistical significance was assessed using unpaired t-test (* P < 0.05). (i) Quantitative RT-PCR analysis of Runx2 mRNA expression in skeletal muscle from control and notexin-injected mice (n = 3). Data are presented as mean ± S.D. Statistical significance was assessed using unpaired t-test (* P < 0.05). (j) Western blot analysis of Runx2 and tubulin (loading control) protein expression in skeletal muscle from control and notexin-injected mice.

Article Snippet: Serum IL-18 concentrations were measured using the Mouse IL-18 DuoSet ELISA Kit (DY7625-05; R&D Systems) according to the manufacturer’s instructions.

Techniques: Quantitative RT-PCR, Expressing, Control, Injection, Immunostaining, Muscles, Immunofluorescence, Staining, Enzyme-linked Immunosorbent Assay, Western Blot

Journal: medRxiv

Article Title: T-cell activation and fibroblastic BMP4-Gremlin dysregulation indicate disease severity in acute myocarditis

doi: 10.64898/2026.04.10.26350598

Figure Lengend Snippet: (A-C) Serum levels of BMP4 (A) , GREM1 (B) and GREM2 (C) in AM patients (n = 103) compared to healthy controls (n = 47). (D) ROC curve for BMP4, GREM1, GREM2 and the GREM2/BMP4 ratio to discriminate AM patients from healthy controls. (E) Multivariable correlation matrix showing the relationship between inflammatory cytokines, components of the BMP4-Gremlin axis and clinical parameters (NT-proBNP, LVEF) in AM patients (F-G). Correlation analyses of GREM2 levels and NT-proBNP (F) and HGF (G) . (A-C, F-G) Dots represent individual patients, (A-C) box and whiskers indicate minimum to maximum values, median, and interquartile range. (E) Bubble size in the lower triangle reflects the magnitude of correlation, bubble size in the upper triangle indicates the magnitude of significance. Bubble colour in the lower triangle denotes positive (red) and negative (blue) correlation, in the upper triangle, colour from blue to yellow represents increasing statistical significance. (F-G) Linear regression lines with 95% confidence interval (grey shading). Statistical analyses were performed using the Mann-Whitney U test for (A-C) and non-parametric Spearman correlation for (E-G) . P-values were corrected for multiple testing using the Benjamini-Hochberg method. AM, acute myocarditis, BMP4, bone morphogenic protein 4, GREM1, Gremlin-1, GREM2, Gremlin-2, ROC, Receiver operating characteristic, AUC, area under the curve, NT-proBNP, N-terminal pro-B-type natriuretic peptide, LVEF, left ventricular ejection fraction, HGF, hepatocyte-growth factor, IL-2R, interleukin-2 receptor

Article Snippet: GREM2 serum concentrations were detected using the Mouse PRDC/GREM2 DuoSet ELISA (R&D Systems).

Techniques: MANN-WHITNEY

Journal: medRxiv

Article Title: T-cell activation and fibroblastic BMP4-Gremlin dysregulation indicate disease severity in acute myocarditis

doi: 10.64898/2026.04.10.26350598

Figure Lengend Snippet: (A) UMAP showing clustering of immuno-clinical phenotypes in patients with AM (n = 103). (B) UMAP illustrating the cohort distribution of AM patients across identified clusters (Cohort 1 n = 79, Cohort 2 n = 24) (C) Variable importance score of clinical and immunological parameters contributing to the discrimination of immuno-clinical phenotypes as determined by random forest analysis. (D) Forest plot displaying direction and magnitude of associations between preselected parameters with immuno-clinical phenotypes, expressed as OR for a 2-fold increase in the respective covariate in a multivariate ridge regression model. (E) Correlation analysis between GREM2 and LVEF, visually stratified by immuno-clinical phenotype. (F) ROC curve for CXCL10, GREM2, LVEF and a combined model incorporating CXCL10, GREM2 and LVEF parameters to distinguish between mild and severe immuno-clinical phenotype. (A-B, E) Dots represent individual patients. (E) Linear regression line with 95% confidence interval (grey shading), bubble size reflects CXCL10 levels, and bubble colour denotes immuno-clinical phenotypes and healthy controls. Statistical analyses were performed using unsupervised spiral clustering for (A-B) , random forest analysis was applied in (C) , multivariate ridge regression in (D) and non-parametric Spearman correlation for (E). P-values were derived from bootstrap resampling in (D) . UMAP, Uniform Manifold Approximation and Projection, GREM2, Gremlin-2, LVEF, left ventricular ejection fraction, HGF, hepatocyte-growth factor, NT-proBNP, N-terminal pro-B-type natriuretic peptide, IL-2R, interleukin-2 receptor, OR, odds ratio, AUC, area under the curve

Article Snippet: GREM2 serum concentrations were detected using the Mouse PRDC/GREM2 DuoSet ELISA (R&D Systems).

Techniques: Derivative Assay