OSCR

Circulating extracellular microRNAs as tissue-specific biomarkers of human health and disease.

Code ↔ Paper

15 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 15 matches
  1. [1] § Methods › Development of miRNA-based Tissue Signal (miR-TS) scores ↔ code/02_deconvolution_miRTS/01_signature_construction.R, lines 1–52 · score 0.92 · salivary gland, lymph node, signature matrix, bladder, esophagus, muscle
  2. [2] § Results › Associations of miR-TS with tissue health ↔ code/04_figure_generation/Suppl Fig.R, lines 419–476 · score 0.90 · lung function decline, arterial pressure, body fat, arterial stiffness, eGFR, cognitive impairment
  3. [3] § Results › Associations of miR-TS with tissue health ↔ code/04_figure_generation/Fig2_associations.R, lines 61–115 · score 0.90 · glomerular filtration rate, lymph node, arterial pressure, arterial stiffness, eGFR, lung diseases
  4. [4] § Methods › Development of miRNA-based Tissue Signal (miR-TS) scores ↔ code/01_data_prep/00_loadbasicfunctions.R, lines 1–19 · score 0.89 · salivary gland, plasma detectable, lymph node, bladder, nerve, spleen
  5. [5] § Methods › Biomarkers of tissue-specific health ↔ code/04_figure_generation/Fig2_associations.R, lines 117–166 · score 0.88 · hemoglobin A1c, Body fat, Arterial stiffness, eGFR, lung diseases, FEV1
  6. [6] § Methods › Publicly available ex-miRNA datasets ↔ code/02_deconvolution_miRTS/03_apply_miRTS_to_11_public_datasets.R, lines 405–480 · score 0.76 · traumatic brain injuries, fulminant myocarditis, GSE131695, GSE148153, patient, metadata
  7. [7] § Methods › Development of miRNA-based Tissue Signal (miR-TS) scores ↔ R/data.R, lines 151–210 · score 0.75 · MCP counter, aggregated expression, xCell2, curated, signature matrix, vector
  8. [8] § Methods › Development of miRNA-based Tissue Signal (miR-TS) scores ↔ R/miRTS_score.R, lines 260–338 · score 0.72 · MCP counter, xCell2, absolute mode, gene, CIBERSORT, signature
  9. [9] § Results › Associations of miR-TS with tissue health ↔ code/04_figure_generation/Fig2_associations.R, lines 61–115 · score 0.71 · glomerular filtration rate, lymph node, eGFR, pancreas, AST, BMI
  10. [10] § Results › Associations of miR-TS with tissue health ↔ code/02_deconvolution_miRTS/03_apply_miRTS_to_11_public_datasets.R, lines 405–480 · score 0.69 · traumatic brain injury, fulminant myocarditis, healthy controls, patient, sequencing, chronic
  11. [11] § Results › Associations of miR-TS with tissue health ↔ code/04_figure_generation/Suppl Fig.R, lines 419–476 · score 0.64 · body fat, arterial stiffness, eGFR, pressure, coronary, waist
  12. [12] § Methods › Ex-miRNA isolation and sequencing ↔ R/data.R, lines 1–86 · score 0.59 · Raw sequencing, Genomics, Quantification, Serum, libraries, miRNAs
  13. [13] § Methods › Ex-miRNA isolation and sequencing ↔ R/data.R, lines 1–86 · score 0.57 · Raw sequencing, Extracellular, Serum, libraries, miRNAs, Circulating
  14. [14] § Results › Associations of miR-TS with tissue health ↔ code/02_deconvolution_miRTS/03_apply_miRTS_to_11_public_datasets.R, lines 318–362 · score 0.56 · adipose inflammation, acute stage, Healthy control, dermatitis, miRNA, TS
  15. [15] § Methods › Biomarkers of tissue-specific health ↔ code/04_figure_generation/Fig2_associations.R, lines 168–236 · score 0.55 · lung function decline, ICD, class, FEV1, FVC, coronary

Paper

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The authors' code

R · 313 lines · 12 KB · no license · 4 matches

  1. # Reproduce Figures - Fig.2
  2. source("./code/01_data_prep/00_loadbasicfunctions.R")
  4. # Fig. 2a:####
  5. Forestplot.df <- read.csv("./data/Fig_results/Fig.2_Forest plot.csv")
  7. sort(unique(Forestplot.df$Organ_marker))
  9. Organ_marker.using <- c(
  10. "Artery miR-TS vs. Arterial Stiffness", "Artery miR-TS vs. Mean Arterial Pressure", "Heart miR-TS vs. Coronary Heart Diseases", "Brain miR-TS vs. Cognitive Impairment",
  11. "Pancreas miR-TS vs. Fasting Blood Glucose", "Pancreas miR-TS vs. Hemoglobin A1C", "Pancreas miR-TS vs. Diabetes", "Kidney miR-TS vs. eGFR",
  12. "Lung miR-TS vs. Lung Diseases", "Lung miR-TS vs. Lung Function Decline","Liver miR-TS vs. ALT","Liver miR-TS vs. AST",
  13. "Lymph Node miR-TS vs. %Lymphocytes", "Adipocyte miR-TS vs. BMI", "Adipocyte miR-TS vs. Waist/Height", "Adipocyte miR-TS vs. %Body Fat"
  14. )
  15. Organ_marker.using[!(Organ_marker.using %in% unique(Forestplot.df$Organ_marker))]
  19. Forestplot.df$Organ_marker <- factor(Forestplot.df$Organ_marker, levels = Organ_marker.using)
  22. dummy <- as.data.frame(rbind(
  23. c(0.6,"NAS","artery miR-TS vs. arterial stiffness"),
  24. c(-0.6,"NAS","artery miR-TS vs. arterial stiffness"),
  25. c(0.5,"NAS","artery miR-TS vs. mean arterial pressure"),
  26. c(-0.5,"NAS","artery miR-TS vs. mean arterial pressure"),
  27. c(0.5,"NAS","liver miR-TS vs. ALT"),
  28. c(-0.5,"NAS","liver miR-TS vs. ALT"),
  29. c(0.5,"NAS","liver miR-TS vs. AST"),
  30. c(-0.5,"NAS","liver miR-TS vs. AST"),
  32. c(0.5,"NAS","heart miR-TS vs. coronary heart diseases"),
  33. c(-0.5,"NAS","heart miR-TS vs. coronary heart diseases"),
  34. c(1,"NAS","lung miR-TS vs. lung diseases"),
  35. c(-1,"NAS","lung miR-TS vs. lung diseases"),
  36. c(1.5,"NAS","lung miR-TS vs. lung function decline"),
  37. c(-1.5,"NAS","lung miR-TS vs. lung function decline"),
  38. c(0.5,"NAS","kidney miR-TS vs. eGFR"),
  39. c(-0.5,"NAS","kidney miR-TS vs. eGFR"),
  41. c(4,"NAS","pancreas miR-TS vs. diabetes"),
  42. c(-4,"NAS","pancreas miR-TS vs. diabetes"),
  43. c(0.5,"NAS","pancreas miR-TS vs. fasting blood glucose"),
  44. c(-0.5,"NAS","pancreas miR-TS vs. fasting blood glucose"),
  45. c(2,"NAS","pancreas miR-TS vs. hemoglobin A1C"),
  46. c(-2,"NAS","pancreas miR-TS vs. hemoglobin A1C"),
  47. c(4,"NAS","brain miR-TS vs. cognitive impairment"),
  48. c(-4,"NAS","brain miR-TS vs. cognitive impairment"),
  50. c(0.4,"NAS","adipocyte miR-TS vs. Waist/Height"),
  51. c(-0.4,"NAS","adipocyte miR-TS vs. Waist/Height"),
  52. c(0.3,"NAS","adipocyte miR-TS vs. BMI"),
  53. c(-0.3,"NAS","adipocyte miR-TS vs. BMI"),
  54. c(0.08,"NAS","adipocyte miR-TS vs. %body fat"),
  55. c(-0.08,"NAS","adipocyte miR-TS vs. %body fat"),
  56. c(0.3,"NAS","lymph node miR-TS vs. %lymphocytes"),
  57. c(-0.3,"NAS","lymph node miR-TS vs. %lymphocytes")
  61. # c(0.5,"NAS","heart miR-TS vs. arterial stiffness"),
  62. # c(-0.5,"NAS","heart miR-TS vs. arterial stiffness"),
  63. # c(0.3,"NAS","heart miR-TS vs. SBP"),
  64. # c(-0.3,"NAS","heart miR-TS vs. SBP"),
  65. # c(0.5,"NAS",""),
  66. # c(-0.5,"NAS",""),
  69. # c(0.3,"NAS","lung miR-TS vs. fev1/fvc%"),
  70. # c(-0.3,"NAS","lung miR-TS vs. fev1/fvc%"),
  71. # c(0.3,"NAS","pleurae miR-TS vs. fev1/fvc"),
  72. # c(-0.3,"NAS","pleurae miR-TS vs. fev1/fvc"),
  73. # c(1,"NAS","pleurae miR-TS vs. Lung diseases"),
  74. # c(-1,"NAS","pleurae miR-TS vs. Lung diseases"),
  75. ))
  76. colnames(dummy) <- c("Est", "study", "Organ_marker")
  77. dummy$Est <- as.numeric(dummy$Est)
  78. dummy <- dummy %>%
  79. mutate(
  80. Organ_marker=stringr::str_to_title(Organ_marker),
  81. Organ_marker=gsub("Mir-Ts Vs","miR-TS vs",Organ_marker),
  82. Organ_marker=gsub("Alt","ALT",Organ_marker),
  83. Organ_marker=gsub("Ast","AST",Organ_marker),
  84. Organ_marker=gsub("Fev1/Fvc","FEV1/FVC",Organ_marker),
  85. Organ_marker=gsub("Egfr","eGFR",Organ_marker),
  86. Organ_marker=gsub("A1c","A1C",Organ_marker),
  87. Organ_marker=gsub("Bmi","BMI",Organ_marker),
  88. Organ_marker=gsub("","",Organ_marker),
  89. Organ_marker=gsub("","",Organ_marker),
  90. Organ_marker=gsub("","",Organ_marker),
  91. )
  92. dummy$Organ_marker <- To_1st_upper(dummy$Organ_marker)
  93. unique(dummy$Organ_marker)
  94. dummy$Organ_marker <- factor(dummy$Organ_marker, levels = To_1st_upper(Organ_marker.using))
  96. custom_labels <- c(
  97. "brain-cognitive decline" = "brain\ncognitive decline",
  98. "kidney-eGFR" = "kidney\nestimated glomerular filtration rate",
  99. "liver-ALT" = "liver\nALT",
  100. "lung-fev1/fvc" = "lung\nfev1/fvc",
  101. "lung-lung diseases" = "lung\nlung diseases",
  102. "lymph node- %lymphocytes" = "lymph node\n %lymphocytes",
  103. "artery-arterial stiffness" = "artery\narterial stiffness",
  104. "artery-mean arterial pressure" = "artery\nmean arterial pressure",
  105. "pancreas-diabetes" = "pancreas\ndiabetes"
  106. )
  109. Forestplot.df$Organ_marker <- factor(To_1st_upper(as.character(Forestplot.df$Organ_marker)),
  110. levels = To_1st_upper(Organ_marker.using))
  112. Forestplot.df$study <- factor(
  113. Forestplot.df$study,
  114. levels = c( "pooled", "NAS","DFTJ", "SY")
  115. )
  117. # Forestplot.df_final.ALL <- Forestplot.df
  118. p1 <-
  119. ggplot(Forestplot.df,aes(y = study, x = Est))+
  120. geom_segment(aes(x = CI_l, xend = CI_h, color=study, yend = study))+
  121. geom_point(aes(size=weights/10, shape=study, color=study, alpha=1))+
  122. theme_bw() +
  123. scale_alpha_identity()+
  124. scale_size_area()+
  125. facet_wrap(~Organ_marker,ncol=4,
  126. ,scales="free_x", labeller = label_wrap_gen(width = 28, multi_line = TRUE) #
  127. )+ #labeller(category = label_fn)
  128. scale_shape_manual(values = rev(c(15, 15, 15, 18))) +
  129. scale_color_manual(values = rev(c("#2d89c9", "#39b592", "#e6a23e", "#1e3135" )))+
  130. geom_vline(lty=2, aes(xintercept=ref_line), colour = 'red') +
  131. geom_blank(data=dummy) +
  132. theme(strip.text.x = element_text(size = 10),
  133. strip.background = element_rect(fill = "grey95", color = "black"), # Set background color
  134. axis.title = element_blank(),
  135. legend.position = "none")
  137. p1
  140. ggsave("figure/Fig.1a.png", width = 8, height =6,units = "in",scale = 1, dpi = 300)
  142. # Fig. 2b:####
  143. df_heatmap <- read.csv("./data/Fig_results/Fig.2-Assoc_Score_allOrgans.heatmap.csv")
  146. order_marker <- rev(c(
  147. "%Body Fat",
  148. "Waist/Height", #
  149. "BMI", #
  150. "Arterial Stiffness",
  151. "Mean Arterial Pressure",
  152. "Cognitive Impairment",
  153. # "Cognitive Decline",
  154. # "mmse30", #
  155. "Coronary Heart Diseases",
  156. # "Arterial Stiffness (c)", #
  157. "eGFR", "ALT", "AST",
  158. # "airflow limitation", #
  159. # "FEV1/FVC", #
  160. "Lung Function Decline",
  161. "Lung Diseases",
  162. "%Lymphocytes",
  163. "Diabetes",
  164. "Hemoglobin A1C", #
  165. "Fasting Blood Glucose"
  166. ))
  168. # df_heatmap <- Heatmap.AllOrgans %>%
  169. # filter(P!=0,
  170. # !grepl("muscle|testis|esophagus|thyroid|vein|pleurae", x),
  171. # y %in% order_marker,
  172. # ) %>%
  173. # # mutate(ICD_9=as.numeric(gsub("_benign|_malig", "", y))) %>%
  174. # # left_join(.,Cancer_class, by="ICD_9") %>%
  175. # mutate(
  176. # Padj=p.adjust(P, method="BH")) %>%
  177. # mutate(
  178. # t_adj=case_when(#P<0.001~ "***", P<0.01~ "**", P<0.05~ "*",
  179. # Padj<0.05~ "11",
  180. # Padj<0.2~ "1",
  181. # # Padj>=0.05 & P<0.05~ "1",
  182. # .default = "") )
  183. # t_adj=case_when(P<0.001 & t>0 ~ "***", P<0.01 & t>0 ~ "**", P<0.05 & t>0 ~ "*", .default = "")) %>%
  184. # filter(Cancer_type==Cancer_type_using, Trans==logTrans)
  185. # df_heatmap$y <- gsub("(.{1,40})(\\s|$)", "\\1\n", df_heatmap$y)
  186. {
  187. capitalize_first <- function(x) {
  188. paste0(toupper(substr(x, 1, 1)), tolower(substr(x, 2, nchar(x))))
  189. }
  190. df_heatmap$x <- capitalize_first(df_heatmap$x)
  191. df_heatmap$y <- capitalize_first(df_heatmap$y)
  192. df_heatmap$y <- case_when(
  193. df_heatmap$y == "Ast" ~ "AST",
  194. df_heatmap$y == "Alt" ~ "ALT",
  195. df_heatmap$y == "Egfr" ~ "eGFR",
  196. df_heatmap$y == "Bmi" ~ "BMI",
  197. df_heatmap$y == "Hemoglobin a1c" ~ "Hemoglobin A1c",
  198. df_heatmap$y == "" ~ "",
  199. T ~ df_heatmap$y
  200. )
  201. sort(unique(df_heatmap$y))
  203. }
  204. {
  205. df_heatmap_t <- as.data.frame(
  206. reshape2::dcast(df_heatmap, y~x, value.var = "t") %>%
  207. column_to_rownames("y")
  208. # rename( #UMFA=log_UMFA,
  209. # `5-mTHF`=log_5MTHF,
  210. # SAM=sam_nm, SAH=sah_nm, Homocysteine=log_HCys.wk0, Cysteine=Cys.wk0, Cystathionine=log_cystathionine_nm, Methionine=methionine_um, B12=log_pB12.wk0, Choline=choline_um, Betaine=betaine_um, Dimethylglycine=log_pDMG.wk0, TMAO=log_pTMAO.wk0)
  211. )
  212. temp_1 <- as.data.frame(
  213. reshape2::dcast(df_heatmap, y~x, value.var = "t_adj") %>%
  214. column_to_rownames("y")
  215. )
  216. temp_1[is.na(temp_1)] <- ""
  217. order_marker.new <- rev(c(
  218. "%body fat",
  219. "Waist/height",
  220. "BMI",
  221. "Arterial stiffness",
  222. "Mean arterial pressure",#
  223. "Cognitive impairment",
  224. # "Cognitive decline",
  225. "Coronary heart diseases",
  226. # "Arterial Stiffness (c)",
  227. "eGFR", "ALT", "AST",
  228. # "FEV1/FVC",
  229. "Lung function decline",
  230. "Lung diseases",
  231. "%lymphocytes",
  232. "Diabetes",
  233. "Hemoglobin A1c", #
  234. "Fasting blood glucose"
  235. ))
  236. df_heatmap_t <- df_heatmap_t[order_marker.new, ]
  238. temp_1 <- temp_1[order_marker.new, ]
  239. rownames(df_heatmap_t) <- order_marker.new
  240. rownames(temp_1) <- order_marker.new
  242. library(ComplexHeatmap)
  243. library(circlize)
  244. # dup_names <- gsub(" :.*", "", rownames(df_heatmap_t))
  245. (t_max <- max(abs(min(df_heatmap_t, na.rm = T)), 0, abs(max(df_heatmap_t, na.rm = T))))
  246. # Count the number of newline characters in each element
  247. newline_counts <- sapply(gregexpr("\n", rownames(df_heatmap_t)), function(x) ifelse(x[1] == -1, 0, length(x)))
  249. row_heights <- unit(newline_counts*3, "cm")
  250. # colnames(df_heatmap_t) <- stringr::str_to_title(colnames(df_heatmap_t))
  252. ht <-
  253. Heatmap(as.matrix(df_heatmap_t[]),
  254. col = colorRamp2(c(-t_max, 0, t_max), c("blue", "white", "red")),
  255. cluster_rows = FALSE,
  256. cluster_columns = FALSE,
  257. show_row_dend = F,
  258. # show_column_dend = T,
  259. # show_row_names = T,
  260. row_names_side = 'left',
  261. row_dend_reorder = F,
  262. column_dend_reorder = F,
  263. # top_annotation=colAnn,
  264. column_names_rot = 45,
  265. column_names_centered = F,
  266. # row_names_gp = gpar(col = ifelse(dup_names %in% dup_names[duplicated(dup_names)], "red", "black")),
  267. # left_annotation = rowAnn,
  268. # column_km = 2,
  269. border = 1,
  270. # column_title = "Arsenic exposure (bAs)", column_title_side = "bottom",
  271. column_names_gp = gpar(fontsize = 12),
  272. row_names_gp = gpar(fontsize = 12),
  273. column_title_gp = gpar(fontsize = 14, fontface = "bold"),
  274. heatmap_legend_param = list(
  275. title="t",
  276. legend_width = unit(2, "cm")),
  277. show_heatmap_legend = T,
  278. # cell_fun = function(j, i, x, y, w, h, fill) {
  279. # if(temp_1[i, j] =="***") {
  280. # grid.text("✱✱✱", x, y)
  281. # } else if(temp_1[i, j] =="**") {
  282. # grid.text("✱✱", x, y)
  283. # } else if(temp_1[i, j] =="*") {
  284. # grid.text("✱", x, y)
  285. # } else {
  286. # grid.text("", x, y)
  287. # }}
  288. # height= row_heights,
  289. cell_fun = function(j, i, x, y, w, h, fill) {
  290. if(temp_1[i, j] =="***") {
  291. grid.text("***", x, y)
  292. } else if(temp_1[i, j] =="**") {
  293. grid.text("**", x, y)
  294. } else if(temp_1[i, j] =="*") {
  295. grid.text("*", x, y)
  296. } else if(temp_1[i, j] =="11") {
  297. grid.text("**", x, y, vjust = 0.7,gp = gpar(cex=1.5))
  298. } else if(temp_1[i, j] =="1") {
  299. grid.text("*", x, y, vjust = 0.7,gp = gpar(cex=1.5))
  300. } else {
  301. grid.text("", x, y)
  302. }}
  303. )
  305. }
  306. ht
  308. png(paste("Heatmap-Score_AllOrgan_FDR_0.2 ALL_Cap", "2025Apr18 .png", sep = "_"), # As and OCM&FA metabolites__batchAdjusted.baseline_PBO.png
  309. width=8,height=4,units="in",res=300)
  311. draw(ht, padding = unit(c(2, 35, 2, 2), "mm"))
  313. dev.off()

Fig2_associations.R at commit 8a4fba4, no license · at the source

Overview

Authors: Wending Li1,2, Christina M. Eckhardt1,3,4, Vrinda Kalia1, Louise C. Laurent5, Kasey Brennan6, Wenpin Hou7, Yu Yuan2, Pinpin Long2, Huan Guo2, Joel D. Schwartz6, Tangchun Wu2, Andrea A. Baccarelli6, Haotian Wu1
ORCID iDs: Wending Li, Wenpin Hou, Pinpin Long, Joel D. Schwartz, Tangchun Wu, Andrea A. Baccarelli, Haotian Wu
  1. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University,New York, NY USA
  2. Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology,Wuhan, Hubei China
  3. Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University,New York, NY USA
  4. Merck & Co., Inc.,Rahway, NJ USA
  5. Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Diego,La Jolla, CA USA
  6. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University,Boston, MA USA
  7. Department of Biostatistics, Mailman School of Public Health, Columbia University,New York, NY USA
Institutions: Columbia University (United States); Huazhong University of Science and Technology (China); Merck & Co., Inc., Rahway, NJ, USA (United States) (United States); University of California San Diego (United States); Harvard University (United States)
Journal: Nature communications, volume 17, issue 1, article 5797
Dates: received 12 July 2025; accepted 20 April 2026; published online 28 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72578-3 · PMID 42045252 · PMCID PMC13332237 · OpenAlex W7156299089
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Spectral & time-frequency, Machine learning
Keywords: Biomarkers, miRNAs, Computational biology and bioinformatics, Diseases
MeSH: Biomarkers*, Circulating MicroRNA*, MicroRNAs*, Brain, Female, Humans, Liver, Lung, Male, Middle Aged, Myocardial Infarction, Myocardium, Organ Specificity, Prospective Studies (* major topic)
Topic: MicroRNA in disease regulation (Cancer Research, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI) (K23HL171822); U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS) (R35ES031688)
Citations: cited by 2 papers (Europe PMC); 56 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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li-wending/miRTS

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Commit: e018c472db09431cef940134f45a918bacfccbd7, 13 September 2026
Languages: R (5)
Size: 40 files, 5 scripts
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Holds: README, license file, environment (DESCRIPTION), documentation, 1 notebook
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Tools: edgeR (1 file), ggplot2 (1 file), tidyverse (1 file)
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8 files
At the source: github.com/li-wending/miRTS

li-wending/miRTS_paper

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Commit: 8a4fba4f9e75aca40ff97fb8d0348d447da1f3da, 6 April 2026
Languages: R (14)
Size: 120 files, 14 scripts
Software Heritage: not archived
Found in: “Code availability”
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Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), reshape2 (5 files), ggplot2 (4 files), circlize (3 files), ComplexHeatmap (3 files), edgeR (3 files), ggpubr (1 file), limma (1 file), pheatmap (1 file), survival (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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15 files
At the source: github.com/li-wending/miRTS_paper

Zenodo 19446986

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8 files
At the source:

Zenodo 19446881

License: CC-BY-4.0
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Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-72578-3.

Tracing map

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What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 24 scripts, each with its path and the digest of its content;
  • 15 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-72578-3.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 4 keywords, 14 MeSH terms, 2 funders, 53 references.

Cite

This paper

Li, W., Eckhardt, C. M., Kalia, V., Laurent, L. C., Brennan, K., Hou, W., Yuan, Y., Long, P., Guo, H., Schwartz, J. D., Wu, T., Baccarelli, A. A., & Wu, H. (2026). Circulating extracellular microRNAs as tissue-specific biomarkers of human health and disease. Nature communications, 17(1), 5797. https://doi.org/10.1038/s41467-026-72578-3

BibTeX

@article{li2026circulating,
author = {Li, Wending and Eckhardt, Christina M. and Kalia, Vrinda and Laurent, Louise C. and Brennan, Kasey and Hou, Wenpin and Yuan, Yu and Long, Pinpin and Guo, Huan and Schwartz, Joel D. and Wu, Tangchun and Baccarelli, Andrea A. and Wu, Haotian},
title = {{Circulating extracellular microRNAs as tissue-specific biomarkers of human health and disease}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5797},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72578-3},
url = {https://doi.org/10.1038/s41467-026-72578-3},
pmid = {42045252},
pmcid = {PMC13332237}
}

RIS

TY - JOUR
AU - Li, Wending
AU - Eckhardt, Christina M.
AU - Kalia, Vrinda
AU - Laurent, Louise C.
AU - Brennan, Kasey
AU - Hou, Wenpin
AU - Yuan, Yu
AU - Long, Pinpin
AU - Guo, Huan
AU - Schwartz, Joel D.
AU - Wu, Tangchun
AU - Baccarelli, Andrea A.
AU - Wu, Haotian
TI - Circulating extracellular microRNAs as tissue-specific biomarkers of human health and disease
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/28
VL - 17
IS - 1
SP - 5797
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72578-3
UR - https://doi.org/10.1038/s41467-026-72578-3
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-72578-3",
"type": "article-journal",
"title": "Circulating extracellular microRNAs as tissue-specific biomarkers of human health and disease",
"container-title": "Nature communications",
"author": [
{
"family": "Li",
"given": "Wending"
},
{
"family": "Eckhardt",
"given": "Christina M."
},
{
"family": "Kalia",
"given": "Vrinda"
},
{
"family": "Laurent",
"given": "Louise C."
},
{
"family": "Brennan",
"given": "Kasey"
},
{
"family": "Hou",
"given": "Wenpin"
},
{
"family": "Yuan",
"given": "Yu"
},
{
"family": "Long",
"given": "Pinpin"
},
{
"family": "Guo",
"given": "Huan"
},
{
"family": "Schwartz",
"given": "Joel D."
},
{
"family": "Wu",
"given": "Tangchun"
},
{
"family": "Baccarelli",
"given": "Andrea A."
},
{
"family": "Wu",
"given": "Haotian"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5797",
"DOI": "10.1038/s41467-026-72578-3",
"PMID": "42045252",
"PMCID": "PMC13332237",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72578-3",
"language": "en",
"issued": {
"date-parts": [
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2026,
4,
28
]
]
}
}

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