OSCR

Prenatal exposure to maternal steroid hormones and child neurodevelopment: evidence for sex-specific effects in a longitudinal birth cohort

Code ↔ Paper

5 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 5 matches
  1. [1] § Cross-cohort consistency analysis ↔ scripts/7_EWAS_BiSC_hormones_neuro.R, lines 1–71 · score 0.96 · linear regression models, maternal ethnicity, missForest, maternal BMI, nursery attendance, passive smoking
  2. [2] § Discovery analysis in the INMA-Sabadell cohort ↔ scripts/6_lmm_INMA_interaction_sex_exposure.R, lines 1–60 · score 0.87 · sex interaction, maternal origin, nursery attendance, passive smoking, maternal education, maternal age
  3. [3] § Discovery analysis in the INMA-Sabadell cohort ↔ scripts/7_EWAS_BiSC_hormones_neuro.R, lines 1–71 · score 0.86 · missForest, nursery attendance, passive smoking, maternal education, maternal age, covariate
  4. [4] § Cross-cohort consistency analysis ↔ scripts/6_lmm_INMA_interaction_sex_exposure.R, lines 1–60 · score 0.85 · lmerTest, maternal BMI, nursery attendance, passive smoking, maternal education, maternal age
  5. [5] § Neurodevelopmental outcome assessments in children › INMA-Sabadell cohort ↔ scripts/3_lmm_INMA_hormones_neuro_with_residuals.R, lines 140–215 · score 0.59 · early motor skills, Early cognitive skills, general cognition, Mental, residuals

Paper

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The authors' code

R · 273 lines · 12 KB · no license · 2 matches

  1. # ====================================================================================================================================================================
  2. # Secondary analysis in the BiSC cohort: Linear regression models between prenatal steroid hormone exposures and early-life neurodevelopmental outcomes at 18 months
  3. # Author: Estelle Renard-Dausset
  4. # Purpose: Generate results table for the Exposure-Wide Association Study (EWAS) in BiSC
  5. # Requirements: dplyr, readxl, broom, gridExtra, tidyr, forcats
  6. # ====================================================================================================================================================================
  8. # Load libraries
  9. library(dplyr)
  10. library(readxl)
  11. library(broom)
  12. library(gridExtra)
  13. library(tidyr)
  14. library(forcats)
  16. # Load data
  17. codebook <- read.csv("data/codebook_BiSC_hormones.csv")
  18. data <- read.csv("data/data_IGRO_BiSC.csv")
  19. imputed_data <- read.csv("data/imputed_data_IGRO_BiSC.csv") # version where the covariates are imputed with missForest
  21. # Convert some variables to factor (categories)
  22. as_factor_cols <- c("educ_level_m_3cat", "hosp_recruit_m_12w", "ethnicity_m_3cat","b_sexo", "ethnicity_m", "parity_m", "parity_m_2cat",
  23. "S17", "smoke_any_m", "smoke_sust_m", "BISC_cb_v01_12w_questMareEV12w.smokepassive_m_preg_2c",
  24. "d1_diet.breastfeeding.yn_c_18m_3c", "o5_nursery_c_18m_4c",
  25. "nursery_18m", "smoke_pregnancy", "breastfeeding_18m", "maternal_ethnicity","maternal_education")
  27. data <- data %>% mutate(across(all_of(as_factor_cols), as.factor))
  28. imputed_data <- imputed_data %>% mutate(across(all_of(as_factor_cols), as.factor))
  30. # nursery_18m, smoke_pregnancy, breastfeeding_18m, maternal_ethnicity, maternal_education --> New categorical vars
  32. # EARLY-LIFE NEURODEVELOPMENTAL OUTCOMES
  33. # Cognitive direct score: bay_pdg_c_18m
  34. # Receptive communication direct score: bay_pdcr_c_18m
  35. # Expressive communication direct score:bay_pdce_c_18m
  36. # Fine motor skills direct score: bay_pdmf_c_18m
  37. # Gross motor skills direct score: bay_pdmg_c_18m
  39. outcomes <- c("bay_pdg_c_18m", "bay_pdcr_c_18m", "bay_pdce_c_18m", "bay_pdmf_c_18m", "bay_pdmg_c_18m")
  41. # Scale scores: transform to mean of 10 and standard deviation is 3
  42. imputed_data <- imputed_data %>%
  43. mutate(
  44. bay_pdg_c_18m_scaled = 10 + 3 * (bay_pdg_c_18m - mean(bay_pdg_c_18m, na.rm = TRUE)) / sd(bay_pdg_c_18m, na.rm = TRUE),
  45. bay_pdcr_c_18m_scaled = 10 + 3 * (bay_pdcr_c_18m - mean(bay_pdcr_c_18m, na.rm = TRUE)) / sd(bay_pdcr_c_18m, na.rm = TRUE),
  46. bay_pdce_c_18m_scaled = 10 + 3 * (bay_pdce_c_18m - mean(bay_pdce_c_18m, na.rm = TRUE)) / sd(bay_pdce_c_18m, na.rm = TRUE),
  47. bay_pdmf_c_18m_scaled = 10 + 3 * (bay_pdmf_c_18m - mean(bay_pdmf_c_18m, na.rm = TRUE)) / sd(bay_pdmf_c_18m, na.rm = TRUE),
  48. bay_pdmg_c_18m_scaled = 10 + 3 * (bay_pdmg_c_18m - mean(bay_pdmg_c_18m, na.rm = TRUE)) / sd(bay_pdmg_c_18m, na.rm = TRUE)
  49. )
  51. scaled_outcomes <- c("bay_pdg_c_18m_scaled", "bay_pdcr_c_18m_scaled", "bay_pdce_c_18m_scaled", "bay_pdmf_c_18m_scaled", "bay_pdmg_c_18m_scaled")
  54. # Compute age in number of days at Bayley test:
  55. imputed_data$age_bayley_days <- imputed_data$bay_date.d_c_18m + (imputed_data$bay_date.m_c_18m * 30.4368) + (imputed_data$bay_date.y_c_18m * 365.2422)
  57. model_number <- "model_1" #"model_1" #model_2
  59. confounders <- switch(model_number,
  60. # Model 1 = Total effect: Maternal age, maternal education, maternal ethnicity, maternal BMI 12w, parity, smoking,
  61. # passive smoking, sex of the child, age at Bayley test, nursery attendance
  62. "model_5" = c("edad", "maternal_education", "maternal_ethnicity", "bmi_m_12w", "parity_m_2cat", "smoke_pregnancy",
  63. "BISC_cb_v01_12w_questMareEV12w.smokepassive_m_preg_2c", "b_sexo", "age_bayley_days", "nursery_18m"),
  65. # Model 2 = Direct effect: Maternal age, maternal education, maternal ethnicity, maternal BMI 12w, parity, smoking,
  66. # passive smoking, sex of the child, age at Bayley test, nursery attendance, breastfeeding status
  67. "model_6" = c("edad", "maternal_education", "maternal_ethnicity", "bmi_m_12w", "parity_m_2cat", "smoke_pregnancy",
  68. "BISC_cb_v01_12w_questMareEV12w.smokepassive_m_preg_2c", "b_sexo", "age_bayley_days", "nursery_18m", "breastfeeding_18m"),
  70. stop("Invalid model number")
  71. )
  74. # Formula for EWAS (Exposure-Wide Association Study)
  75. define_formula <- function(outcome, exposure, confounders, log2=FALSE) {
  76. if(log2 ==TRUE){
  77. return(as.formula(paste(outcome, "~", "log2(",exposure, ") +", paste(confounders, collapse = " + "))))
  78. }
  79. else{return(as.formula(paste(outcome, "~", exposure, " +", paste(confounders, collapse = " + "))))
  80. }
  81. }
  83. # Compute effective number of tests for specific columns of the dataset (ENT)
  84. # Input: data for which to compute ENT
  85. # Output: Effective Number of Tests
  86. compute_ENT <- function(data_ENT) {
  87. cor_matrix <- cor(data_ENT, use = "pairwise.complete.obs") #Compute the correlation matrix
  88. eigen_values <- eigen(cor_matrix)$values #Perform eigenvalue decomposition
  89. ENT <- sum(pmin(eigen_values, 1)) #Calculate the effective number of tests
  90. return(ENT)
  91. }
  93. # PREDICTORS
  94. # 1 - Hormone metabolites
  95. hormones <- imputed_data %>% select(ends_with("32w_i_SG_GA_adj")) %>% colnames()
  96. # 2 - Hormone sums
  97. sum_hormones <- grep("^sum_.*SG_GA_adj$", colnames(imputed_data), value=TRUE)
  98. # 3 - Sulfate/Glucuronide ratios
  99. ratios_SG <- grep("ratio_SG_.*_GA_adj$", colnames(imputed_data), value=T)
  100. ratios_SG <- ratios_SG[!grepl("_SG_GA_adj$", ratios_SG)]
  101. # 4 - Phase I (product/precursor) ratios
  102. ratios_phaseI <- imputed_data %>% select(starts_with("ratio")& !starts_with("ratio_SG") & ends_with("_GA_adj")) %>% colnames()
  103. ratios_phaseI <- ratios_phaseI[!grepl("_SG_GA_adj$", ratios_phaseI)]
  105. predictors <- c(hormones, sum_hormones, ratios_SG, ratios_phaseI)
  107. # Compute ENT
  108. ENT_hormones <- compute_ENT(imputed_data[,hormones])
  109. ENT_sums <- compute_ENT(imputed_data[,sum_hormones])
  110. ENT_SG_ratios <- compute_ENT(imputed_data[,ratios_SG])
  111. ENT_phase_I_ratios <- compute_ENT(imputed_data[,ratios_phaseI])
  112. ENT_outcomes <- compute_ENT(imputed_data[,scaled_outcomes])
  115. #### EWAS ####
  117. results_df_hormones <- data.frame()
  118. results_df_sums <- data.frame()
  119. results_df_SG_ratio <- data.frame()
  120. results_df_phaseI_ratio <- data.frame()
  122. for(outcome in scaled_outcomes) {
  124. #### 1 - Hormone metabolites ####
  125. for(hormone in hormones) {
  127. # Keep only participants with available outcome, hormone and confounder data:
  128. cols <- c(outcome, hormone, confounders)
  129. filtered_df <- imputed_data[complete.cases(imputed_data[,cols]),]
  130. n <- nrow(filtered_df)
  132. formula <- define_formula(outcome, hormone, confounders, log2 = TRUE)
  133. model <- lm(formula, data=filtered_df)
  134. r2_full <- summary(model)$r.squared
  136. formula_conf <- as.formula(paste(outcome, "~", paste(confounders, collapse = " + ")))
  137. model_conf <- lm(formula_conf, data = filtered_df)
  138. r2_conf <- summary(model_conf)$r.squared
  140. # Variance explained by the predictor alone
  141. r2_predictor <- r2_full - r2_conf
  143. results <- tidy(model, conf.int = TRUE, conf.level = 0.95) %>%
  144. mutate(outcome = outcome,
  145. predictor = hormone,
  146. sample_size = n,
  147. R2_predictor = r2_predictor,
  148. R2_full = r2_full,
  149. signif_thresh = 0.05/(ENT_hormones * ENT_outcomes),
  150. model = model_number)
  152. log2_hormone <- paste0("log2(",hormone,")")
  153. results <- results %>% filter(term == log2_hormone)
  155. results_df_hormones <- rbind(results_df_hormones, results)
  156. }
  158. #### 2 - Hormone sums ####
  159. for(sum in sum_hormones) {
  161. # Keep only participants with available outcome, sum hormone and confounder data
  162. # Remove rows where the sum is zero:
  163. # Due to a few missing values of Gestational Age, we have NA in GA_adj hormones for some participants,
  164. # Therefore the sum is zero and becomes infinite after log2 transform
  165. cols <- c(outcome, sum, confounders)
  166. filtered_df <- imputed_data[imputed_data[[sum]] != 0 & complete.cases(imputed_data[,cols]),]
  167. n <- nrow(filtered_df)
  169. log2_sum <- paste0("log2(",sum,")")
  171. formula <- define_formula(outcome, sum, confounders, log2 = TRUE)
  172. model <- lm(formula, data=filtered_df)
  173. r2_full <- summary(model)$r.squared
  175. formula_conf <- as.formula(paste(outcome, "~", paste(confounders, collapse = " + ")))
  176. model_conf <- lm(formula_conf, data = filtered_df)
  177. r2_conf <- summary(model_conf)$r.squared
  179. # Variance explained by the predictor alone
  180. r2_predictor <- r2_full - r2_conf
  182. results <- tidy(model, conf.int = TRUE, conf.level = 0.95) %>%
  183. mutate(outcome = outcome,
  184. predictor = sum,
  185. sample_size = n,
  186. R2_predictor = r2_predictor,
  187. R2_full = r2_full,
  188. signif_thresh = 0.05/(ENT_sums * ENT_outcomes),
  189. model = model_number)
  191. results <- results %>% filter(term == log2_sum)
  193. results_df_sums <- rbind(results_df_sums, results)
  194. }
  196. #### 3 - Sulfate/Glucuronide ratios ####
  197. for(ratio in ratios_SG) {
  199. # Keep only participants with available outcome, ratio hormone and confounder data:
  200. cols <- c(outcome, ratio, confounders)
  201. filtered_df <- imputed_data[complete.cases(imputed_data[,cols]),]
  203. n <- nrow(filtered_df)
  205. formula <- define_formula(outcome, ratio, confounders, log2 = TRUE)
  206. model <- lm(formula, data=filtered_df)
  207. r2_full <- summary(model)$r.squared
  209. formula_conf <- as.formula(paste(outcome, "~", paste(confounders, collapse = " + ")))
  210. model_conf <- lm(formula_conf, data = filtered_df)
  211. r2_conf <- summary(model_conf)$r.squared
  213. # Variance explained by the predictor alone
  214. r2_predictor <- r2_full - r2_conf
  216. results <- tidy(model, conf.int = TRUE, conf.level = 0.95) %>%
  217. mutate(outcome = outcome,
  218. predictor = ratio,
  219. sample_size = n,
  220. R2_predictor = r2_predictor,
  221. R2_full = r2_full,
  222. signif_thresh = 0.05/(ENT_SG_ratios * ENT_outcomes),
  223. model = model_number)
  225. log2_ratio <- paste0("log2(",ratio,")")
  226. results <- results %>% filter(term == log2_ratio)
  228. results_df_SG_ratio <- rbind(results_df_SG_ratio, results)
  230. }
  232. #### 4 - Phase I ratios ####
  234. for(ratio in ratios_phaseI) {
  236. # Keep only participants with available outcome, ratio hormone and confounder data:
  237. cols <- c(outcome, ratio, confounders)
  238. filtered_df <- imputed_data[complete.cases(imputed_data[,cols]),]
  240. n <- nrow(filtered_df)
  242. formula <- define_formula(outcome, ratio, confounders, log2 = TRUE)
  243. model <- lm(formula, data=filtered_df)
  244. r2_full <- summary(model)$r.squared
  246. formula_conf <- as.formula(paste(outcome, "~", paste(confounders, collapse = " + ")))
  247. model_conf <- lm(formula_conf, data = filtered_df)
  248. r2_conf <- summary(model_conf)$r.squared
  250. # Variance explained by the predictor alone
  251. r2_predictor <- r2_full - r2_conf
  253. results <- tidy(model, conf.int = TRUE, conf.level = 0.95) %>%
  254. mutate(outcome = outcome,
  255. predictor = ratio,
  256. sample_size = n,
  257. R2_predictor = r2_predictor,
  258. R2_full = r2_full,
  259. signif_thresh = 0.05/(ENT_phase_I_ratios * ENT_outcomes),
  260. model = model_number)
  262. log2_ratio <- paste0("log2(",ratio,")")
  263. results <- results %>% filter(term == log2_ratio)
  265. results_df_phaseI_ratio <- rbind(results_df_phaseI_ratio, results)
  267. }
  269. }
  271. EWAS_results <- rbind(results_df_hormones, results_df_sums, results_df_SG_ratio, results_df_phaseI_ratio)
  272. EWAS_results <- merge(EWAS_results, codebook %>% select(variable, variable_group, variable_plot_name, hormone_full_name, hormone, hormone_group, sum_hormone_group), by.x= "predictor", by.y="variable", all.x=T)
  273. write.csv(EWAS_results, file= paste0("results/",model_number,"/result_files/EWAS_results_", model_number,".csv"), row.names=FALSE)

7_EWAS_BiSC_hormones_neuro.R at commit 52599f3, no license · at the source

Overview

Authors: Estelle Renard-Dausset1, Emily P. Laveriano-Santos1, Mariona Bustamante1, Muriel Ferrer1, Mireia Gascon1, Mònica Guxens1, Noemí Haro2, Jordi Julvez1, Aurélie Portefaix3, Martine Vrijheid1, Óscar J Pozo2, Léa Maitre1
  1. Barcelona Institute for Global Health
  2. Hospital del Mar Medical Research Institute
  3. Hospices Civils de Lyon
Institutions: Barcelona Institute for Global Health (Spain); Hospital Del Mar (Spain); Hospices Civils de Lyon (France)
Journal: Research Square (preprint)
Dates: published online 23 April 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.21203/rs.3.rs-9198207/v1 · OpenAlex W7155378585
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), developmental (subfield)
Methods: Statistics, Machine learning
Keywords: neonates, newborn, sepsis, hospital, unsupervised learning, Sub-Sahara Africa
Topic: Maternal Mental Health During Pregnancy and Postpartum (Public Health, Environmental and Occupational Health, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

Background: Neurodevelopmental disorders affect millions of children worldwide, yet their prenatal biological origins remain unclear. Steroid hormones regulate key processes in fetal brain development, but the role of maternal steroid metabolism during pregnancy on children's neurodevelopment remains poorly understood.

Methods: We report the first comprehensive longitudinal analysis of maternal steroid metabolism in late pregnancy in relation to offspring neurodevelopment. We quantified 50 maternal urinary steroid metabolites and derived 40 indicators of steroid metabolism in third-trimester samples from two Spanish birth cohorts (INMA-Sabadell, n = 500; BiSC, n = 556). In INMA-Sabadell, child cognition, motor development, attention and behaviour were assessed repeatedly from 15 months to 15 years, and associations were estimated using linear mixed-effects models, including sex-stratified analyses. As a secondary analysis, we evaluated cross-cohort consistency by testing whether associations with early- life cognitive and motor outcomes identified in INMA-Sabadell were also observed in BiSC at 18 months using linear regression models.

Results: In INMA-Sabadell, higher maternal levels of the cortisol metabolite 20α-dihydrocortisol-glucuronide were associated with poorer early cognitive abilities (estimate β = -2.05, 95% confidence interval (CI): [-3.08, -1.02]), whereas indicators of enhanced cortisol inactivation were associated with better attention, as reflected by lower variability in reaction time (β = -12.99, 95% CI: [-20.22, -5.76]). The association with 20α-dihydrocortisol-glucuronide was consistently observed in the BiSC cohort at 18 months (β = -0.29, 95% CI: -0.51, -0.06). Sex-stratified analyses revealed marked sexual dimorphism. Notably, indicators of greater androgen bioavailability were associated with increased externalizing behaviour in females (β = 0.23, 95% CI: [0.11, 0.35]) but better fluid intelligence in males (β = 4.76, 95% CI: [2.02, 7.52]).

Conclusions: These findings establish maternal steroid metabolism as a determinant of child neurodevelopment, highlight the relevance of including metabolic transformation indicators, and identify both shared and sex-specific in utero biomarkers of neurodevelopmental trajectories.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

EstelleRD/prenatal-steroids-neurodevelopment

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 52599f385c8f38156f2e110e254e80cee2075edc, 27 January 2026
Languages: R (7)
Size: 9 files, 7 scripts
Software Heritage: not archived
Found in: “Availability of data and materials”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (7 files), broom (6 files), lmerTest (5 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
8 files
At the source: github.com/EstelleRD/prenatal-steroids-neurodevelopment

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 7 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Availability of data and materials

The participants data reported in this study cannot be deposited in a public repository due to participant confidentiality and privacy concerns. Therefore, data is available upon written request. According to standard controlled access procedure, applications to use the BiSC and INMA-Sabadell data will be reviewed by the Steering Committee, evaluation of the fit of the data for the proposed methodology, and verification that the proposed use meets the guidelines of the Ethic and Governance Framework and of the consent that was provided by the participants. To request BiSC data follow the instructions described in the website.

All original analytical code has been deposited in a GitHub repository and is publicly available at https://github.com/EstelleRD/prenatal-steroids-neurodevelopment

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, journal, dates, 12 authors, 6 keywords, 42 references.

Cite

This paper

Renard-Dausset, E., Laveriano-Santos, E. P., Bustamante, M., Ferrer, M., Gascon, M., Guxens, M., Haro, N., Julvez, J., Portefaix, A., Vrijheid, M., Pozo, Ó. J., & Maitre, L. (2026). Prenatal exposure to maternal steroid hormones and child neurodevelopment: evidence for sex-specific effects in a longitudinal birth cohort. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-9198207/v1

BibTeX

@article{renarddausset2026prenatal,
author = {Renard-Dausset, Estelle and Laveriano-Santos, Emily P. and Bustamante, Mariona and Ferrer, Muriel and Gascon, Mireia and Guxens, Mònica and Haro, Noemí and Julvez, Jordi and Portefaix, Aurélie and Vrijheid, Martine and Pozo, Óscar J and Maitre, Léa},
title = {{Prenatal exposure to maternal steroid hormones and child neurodevelopment: evidence for sex-specific effects in a longitudinal birth cohort}},
journal = {Research Square (preprint)},
year = {2026},
month = apr,
publisher = {Research Square},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-9198207/v1},
url = {https://doi.org/10.21203/rs.3.rs-9198207/v1}
}

RIS

TY - JOUR
AU - Renard-Dausset, Estelle
AU - Laveriano-Santos, Emily P.
AU - Bustamante, Mariona
AU - Ferrer, Muriel
AU - Gascon, Mireia
AU - Guxens, Mònica
AU - Haro, Noemí
AU - Julvez, Jordi
AU - Portefaix, Aurélie
AU - Vrijheid, Martine
AU - Pozo, Óscar J
AU - Maitre, Léa
TI - Prenatal exposure to maternal steroid hormones and child neurodevelopment: evidence for sex-specific effects in a longitudinal birth cohort
T2 - Research Square (preprint)
J2 - Res Sq
PY - 2026
DA - 2026/04/23
SN - 2693-5015
PB - Research Square
DO - 10.21203/rs.3.rs-9198207/v1
UR - https://doi.org/10.21203/rs.3.rs-9198207/v1
ER -

CSL-JSON

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"title": "Prenatal exposure to maternal steroid hormones and child neurodevelopment: evidence for sex-specific effects in a longitudinal birth cohort",
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"author": [
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"family": "Renard-Dausset",
"given": "Estelle"
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"DOI": "10.21203/rs.3.rs-9198207/v1",
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[1] doi:10.1038/s41467-026-71415-x [code]
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Chronic Administration of Marinobufagenin in Mice Causes Hyperlocomotion and Decrease in Anxiety by Altering Monoamine Turnover Unaccompanied by Motor Deficits or Oxidative Stress.
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