A symmetric systemic challenge elicits a right-biased response mediated by vasopressin signaling.

Code ↔ Paper

4 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.

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[1] § MATERIALS AND METHODS › Statistical analysis › Processing of physiological data › Stretching force ↔ force_measurement/report_SF_preprocess.Rmd, lines 21–44 · score 0.70 · stretching force measurements, loess smoothing, hind limbs, 0–10 mm, distance, WL
[2] § MATERIALS AND METHODS › Statistical analysis › Processing of physiological data › Bayesian framework ↔ BayesianPValue.R, lines 1–50 · score 0.67 · Bayesian framework, RStan, interface, brms, emmeans
[3] § MATERIALS AND METHODS › Statistical analysis › Processing of physiological data › Stretching force ↔ force_measurement/task_2/report_SF_AI.Rmd, lines 21–49 · score 0.64 · loess smoothing, stretching force measurements, hind limbs, 0–10 mm, distance, WL
[4] § MATERIALS AND METHODS › Statistical analysis › Processing of physiological data › Bayesian framework ↔ force_measurement/task_1/report_SF_AI.Rmd, lines 95–143 · score 0.53 · student_t, Bayesian regression, brms, SD, chains, Predictors

Paper

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The authors' code

R Markdown · 466 lines · 12 KB · MIT · 1 match

---
title: 'SF: Read and preprocess raw stretch force measurements 1 and 3'
author: "Yaromir Kobikov <[email hidden]>"
date: "Date: `r format(Sys.time(), '%d/%m/%Y')`"
output:
word_document: default
html_document:
toc: true
toc_float:
collapsed: false
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
source("../BayesianPValue.R")
```
> **Stretch‑force experiments with symmetric WD groups.**
> Force–distance curves from both hind limbs were recorded simultaneously after bilateral peptide injection.
> Mechanical work for each limb (WL, WR) was obtained by integrating a loess‑smoothed force curve (span = 0.4, family = "symmetric") over 0–10 mm stretch.
> Asymmetry was quantified as the work difference ΔW = WL − WR and the log‑ratio AI<sub>L/R</sub> = log₂(WL / WR).
## Read stretch force (SF) data
Read raw sampled forces and match them with rat's descriptions
```{r config_vars, echo=FALSE}
PATH_DIR_ROOT <- "../data"
PATH_FOLDER_FORCE_MEASUREMENT <- "../data/Force-WD-20240513/"
PATH_SF_DATA_CSV <- paste0(PATH_FOLDER_FORCE_MEASUREMENT, "sf_source_data_20260125.csv")
FILE_NAME = '-ForceValues.xlsx'
PATTERN = paste0('*', FILE_NAME)
DESCRIPTION_FILE_NAME <- "WD_PA 25 12 26_1_v2.xlsx"
SHEET <- "DATA_25 12 25" # for description
TEST_NUM_FILES <- 55
```
```{r}
correctDate <- Vectorize(function(date, pattern, sep="-") {
dateSplit <- str_split(date, pattern)[[1]]
return(paste0(dateSplit[1], sep, dateSplit[2], sep, dateSplit[3]))
})
read.StretchForce <- function(RatFile, show=TRUE) {
ratName <- sub(FILE_NAME, "", basename(RatFile), fixed = TRUE)
ratNum <- str_split(ratName, "SDU")[[1]][2]
ratName <- paste0("SDU", "-", ratNum)
sheetNames <- RatFile %>%
excel_sheets() %>%
as_tibble %>%
filter(grepl(pattern = "F([0-9]+)", value))
df <-
map_df(
.x = sheetNames$value,
~read_excel(path = RatFile, sheet = .x, range = cell_cols("A:C")),
.id = "replication"
) %>%
mutate(RatID = ratName) %>%
relocate(RatID, .before = replication)
if(show) print(paste0(ratName, " -> ", toString(count(sheetNames)$n), ", ", count(df)$n))
return(df)
}
my.rwData <- function() {
# if(file.exists(PATH_SF_DATA_CSV)) {
# print(paste0("File ", " - ", PATH_SF_DATA_CSV, " - exists"))
# return(read_csv(PATH_SF_DATA_CSV, show_col_types = FALSE))
# }
print("In process...")
sf <-
map_df(
.x = list.files(path = PATH_FOLDER_FORCE_MEASUREMENT, pattern = PATTERN, full.names = TRUE),
~read.StretchForce(.x)
)
if(length(unique(sf$RatID)) == TEST_NUM_FILES) print(paste0(TEST_NUM_FILES, " - ", "OK"))
# match rat's description with raw force measurements
rats_description <-
file.path(PATH_DIR_ROOT, DESCRIPTION_FILE_NAME) %>%
read_excel(SHEET, .name_repair = "universal", na = c("", "NA", "NULL")) %>%
rename(
BW_pre = BW..pre.,
BW_post = BW..post.
# Measurement.1 = Measurement.1,
# Measurement.3 = Measurement.3
) %>%
select(
RatID,
Measurement1,
Measurement3,
BW_pre, BW_post,
MP.1.1, MP.1.2, MP.1.3,
MP.3.1, MP.3.2, MP.3.3
) %>%
rename_with(
~ sub("^MP\\.(\\d+)\\.(\\d+)$", "PA\\1.\\2", .x),
.cols = matches("^MP\\.(1|3)\\.(1|2|3)$")
) %>%
mutate(RatID = factor(RatID),
Trt1 = factor(Measurement1, c("24H WD", "Control 1")),
Trt3 = factor(Measurement3, c("Sp", "Control 1", "Control 2", "Control 3", "SSR", "Conivaptan"))
) %>%
filter(
(!is.na(Trt1) & Trt1 != "") |
(!is.na(Trt3) & Trt3 != "")
)
print(rats_description, n=100)
#
# print(rats_description %>%
# count(Measurement3, sort = TRUE) %>%
# filter(grepl("Coniv", Measurement3, ignore.case = TRUE)))
sf <-
merge(sf, rats_description, by = "RatID", all.x = TRUE, all.y = TRUE, sort = FALSE) %>%
select(-`s"Time"`) %>%
mutate(rep_i = as.integer(replication)) %>%
# Decide which reps correspond to 24H and 2_3H based on rep count per RatID
group_by(RatID) %>%
mutate(
n_rep = n_distinct(rep_i),
# 24H always uses MP1.1-1.3 => reps 1:3 (if present)
use_24H = rep_i %in% 1:3,
# 2_3H uses MP3.1-3.3, but its rep positions depend on n_rep
use_2_3H = case_when(
n_rep %in% c(3, 6, 12) ~ FALSE, # no MP3.1-3.3
n_rep %in% c(9, 14, 15) ~ rep_i %in% 7:9, # MP3.1-3.3
n_rep >= 30 ~ rep_i %in% 13:15, # MP3.1-3.3
# fallback for "other" counts (e.g., 18, 36, 37...):
# if those reps exist, use them
TRUE ~ case_when(
all(13:15 %in% unique(rep_i)) ~ rep_i %in% 13:15,
all(7:9 %in% unique(rep_i)) ~ rep_i %in% 7:9,
TRUE ~ FALSE
)
),
Period = case_when(
use_24H ~ "24H",
use_2_3H ~ "2_3H",
TRUE ~ NA_character_
),
Period = factor(Period, levels = c("24H", "2_3H")),
Trt = case_when(
use_24H ~ Trt1,
use_2_3H ~ Trt3,
TRUE ~ NA
)
) %>%
ungroup() %>%
# keep only selected reps
filter(!is.na(Period)) %>%
pivot_longer(
cols = c("Left", "Right"),
names_to = "Side",
values_to = "SF"
) %>%
mutate(SF = SF * (-1)) %>%
# keep only rows where treatment exists for that period
filter(!is.na(Trt), Trt != "")
write_csv(x = sf, PATH_SF_DATA_CSV)
return(sf)
}
```
```{r input, results='hide'}
myspan <- 0.4
myname0 <- paste0("SF loess(symmetric span=", myspan, ")")
data <- my.rwData()
sf <-
data %>%
rename(repN = replication) %>%
mutate(
RatID = factor(RatID),
# MS = factor(MS, c("Left", "Right"), c("L", "R")),
# Later = factor(OperationSide == MS, c(FALSE, TRUE), c("Contra", "Ipsi")),
# Trt = paste0(Trt, ".", OperationSide),
Trt = factor(Trt),
repN = factor(as.integer(repN))
)
# sf[!complete.cases(sf), c("RatID", "Trt", "repN", "SF")] # Any unmatched rats? Any unmeasured forces?
```
The group sizes are (distinct rats only, same rats with different stimulation locations are counted as duplicates)
```{r dataset_n, echo=FALSE}
d.sum <- sf %>%
arrange(Trt, Period) %>%
group_by(Trt, Period) %>%
summarise(rats = length(unique(RatID))) %>%
pivot_wider(names_from=c(Trt), values_from = rats)
d.sum %>%
flextable %>%
fontsize(part = "header", size = 9) %>%
autofit
```
## SF, 0-2 sec: Visual inspection of smoothing
```{r SF_combo_dta}
mydta <- sf %>%
filter(!is.na(SF)) %>%
droplevels(.) %>%
mutate(figName = paste0(RatID, ", ", Trt, ", replicate ", repN)) %>%
select(figName, Period, Time, Side, Trt, SF) %>%
# filter(figName == "SDU-115, 3h.BNI.L, replicate 12") %>%
group_by(figName)
```
Stretch force figure combining Contra, Ipsi and their difference.
```{r example_12_rep, echo=FALSE, fig.width=12, fig.height=8}
myspan <- 0.4
name_fig <- "SDU-005, 24H WD, replicate 1"
name_plt <- paste0("raw ", name_fig)
plt[[name_plt]] <-
mydta %>%
filter( figName == name_fig) %>%
# pivot_wider(names_from = Later, values_from = SF) %>%
# mutate(
# "Contra - Ipsi" = Contra - Ipsi
# ) %>%
# pivot_longer(c("Contra", "Ipsi", "Contra - Ipsi"), names_to = "Limbs", values_to = "SF") %>%
ggplot(aes(x = Time, y = SF, group = Side, col = Side)) +
geom_line() +
geom_smooth(
formula = y ~ x,
aes(col = paste0("loess ", myspan)),
method ="loess",
span = myspan,
se = FALSE,
method.args = list(family="symmetric")
) +
labs(
title = paste0(myname0, ", ", name_fig),
x = "Time (sec)",
y = "Stretching force (mg)"
)
print(plt[[name_plt]])
```
```{r}
doc <- read_pptx()
for(nm in names(plt)) {
doc <- doc %>%
add_slide(
layout = "Title and Content",
master = "Office Theme"
) %>%
ph_with(
value = rvg::dml(ggobj = plt[[nm]]),
location = ph_location_type(
type = "body"
#, width=S_width,
# height=S_height
),
bg = "transparent"
) %>%
ph_with(
value = nm,
location = ph_location_type(type = "title")
)
}
doc %>% print(target = paste0("SF loess(symmetric span0.4)", ".pptx"))
```
Visually check the smoothing by loess(), varying the "span" to see if alternatives are better than our choice.
```{r check_loess, include=FALSE}
myspan.test <- 0.4
junk <- mydta %>%
group_map( ~{
theName <- paste(.y)
ggplot(data=.x, aes(y = SF, x = Time, group = Side, col = Side)) +
geom_line() +
geom_smooth(formula = y ~ x, aes(col = paste0(myspan.test + 0.1)), method="loess", span = myspan.test + 0.1, se=FALSE, method.args=list(family="symmetric")) +
geom_smooth(formula = y ~ x, aes(col = paste0(myspan.test - 0.1)), method="loess", span = myspan.test - 0.1, se=FALSE, method.args=list(family="symmetric")) +
geom_smooth(formula = y ~ x, aes(col = paste0(myspan.test)), method="loess", span=myspan.test, se=FALSE, method.args=list(family="symmetric")) +
# theme(legend.position = "none") +
labs(title=paste(myname0, theName), x="Time (sec)", y="Stretching force (mg)")
} ) %>%
setNames(unique(sort(mydta$figName)))
mydta %>% n_groups()
```
```{r write_pptx_check_loess, echo=FALSE, fig.width=12, fig.height=8}
for(nm in unique(mydta$figName)) {
print(junk[[nm]])
}
doc <- read_pptx()
for(nm in unique(mydta$figName)) {
doc <- doc %>%
add_slide(
layout = "Title and Content",
master = "Office Theme"
) %>%
ph_with(
value = rvg::dml(ggobj = junk[[nm]]),
location = ph_location_type(
type = "body"
#, width=S_width,
# height=S_height
),
bg = "transparent"
) %>%
ph_with(
value = nm,
location = ph_location_type(type = "title")
)
}
doc %>% print(target = paste0(myname0, "_0.4_.pptx"))
```
Define smoother for all Time points.
```{r smoother_W_AI}
# 0-1 0.4-1 1-2 0-2 0.4-2
smoother_W <- function(dta, ...) {
# dtaF <- dta %>% filter(Time < 10.5, !is.na(SF))
dtaF <- dta %>% filter(!is.na(SF))
if(nrow(dtaF) < 1 | ncol(dtaF) < 2) {
print(dta)
return(data.frame())
}
my.sm <- loess(SF~Time, data=dtaF, family="symmetric", span=myspan)
i_0_1 <- integrate(function(x) predict(my.sm, newdata=x), 0, 1)
i_0_4_1 <- integrate(function(x) predict(my.sm, newdata=x), 0.4, 1)
i_1_2 <- integrate(function(x) predict(my.sm, newdata=x), 1, 2)
i_0_2 <- integrate(function(x) predict(my.sm, newdata=x), 0, 2)
i_0_4_2 <- integrate(function(x) predict(my.sm, newdata=x), 0.4, 2)
data.frame(
dT = c(
"T_0_1",
"T_0.4_1",
"T_1_2",
"T_0_2",
"T_0.4_2"
),
W = c(
i_0_1$value,
i_0_4_1$value,
i_1_2$value,
i_0_2$value,
i_0_4_2$value
)
)
}
```
Remove unneded columns from SF dataset
```{r clean_SF}
sf_ai_dw <-
sf %>%
# filter(RatID == "TBI-250", Trt %in% c("UBI.R")) %>%
select(Trt, Period, RatID, repN, Side, Time, SF) %>%
ungroup %>%
arrange(Trt, RatID, repN, Side, Time)
```
Compute work (W) as integrals (0-1 sec, 0.4-1 sec, ...) of smoothed force measurements. dW = W_Left - W_Right
```{r compute_W_LR}
sf_LR_dW <- sf_ai_dw %>%
group_by(Trt, Period, RatID, repN) %>%
pivot_wider(names_from = Side, values_from = SF) %>%
# spread(MS, SF) %>%
mutate( SF = Left - Right ) %>%
group_modify(smoother_W) %>%
ungroup %>%
droplevels(.)
```
Compute AI = log2(Left/Right) using integrals (0-1 sec, 0.4-1 sec, ...) of smoothed force measurements.
```{r compute_AI_LR}
sf_LR_AI <- sf_ai_dw %>%
group_by(Trt, Period, RatID, repN, Side) %>%
group_modify(smoother_W) %>%
ungroup %>%
group_by(RatID, Trt, repN, dT) %>%
pivot_wider(names_from = Side, values_from = W) %>%
# spread(MS, W) %>%
mutate( AI = log2( Left / Right ) ) %>%
ungroup %>%
droplevels(.)
```
Save preprocessed SF
```{r smooth}
myname <- paste0("SF Sp 0-1 0.4-1 1-2 0-2 0.4-2 loess(symmetric span", myspan, ")")
save(sf_LR_dW, sf_LR_AI, sf, file=paste0(myname, "_rats.RData"))
```

report_SF_preprocess.Rmd at commit e5adf0d, under MIT · at the source

Overview

Authors: Hiroyuki Watanabe^1,2, Yaromir Kobikov¹, Sara Yusuf Mohamed², Karen Rich², Daniil Sarkisyan³, Olga Nosova¹, Alfhild Grönbladh¹, Mathias Hallberg¹, Jens Schouenborg⁴, Georgy Bakalkin¹, Mengliang Zhang²

ORCID iDs: Sara Yusuf Mohamed, Daniil Sarkisyan, Mathias Hallberg, Georgy Bakalkin, Mengliang Zhang

Institutions: Uppsala University (Sweden); University of Southern Denmark (Denmark); Science for Life Laboratory (Sweden); Lund University (Sweden)

Journal: Journal of neuroendocrinology, volume 38, issue 9, article e70257

Dates: received 13 April 2026; accepted 26 August 2026; published online 6 September 2026; in print September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1111/jne.70257 · PMID 42702826 · PMCID PMC13547646 · OpenAlex W7133516019

Open access: hybrid, a free copy (OpenAlex)

Preprint: doi:10.64898/2026.03.02.708998

Status: code verified

Categories: rat (organism), cellular / molecular (subfield)

Methods: Statistics

Keywords: functional asymmetry, left–right balance, symmetric systemic challenge, vasopressin, water deprivation

MeSH: Arginine Vasopressin*, Functional Laterality*, Signal Transduction*, Vasopressins*, Water Deprivation*, Animals, Antidiuretic Hormone Receptor Antagonists, Dehydration, Hindlimb, Hypothalamus, Male, Posture, Rats, Rats, Sprague-Dawley, Receptors, Vasopressin (* major topic)

Topic: Hemispheric Asymmetry in Neuroscience (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Vetenskapsrådet (VR) (2022‐01182); Novo Nordisk Fonden (NNF20OC0065099)

Citations: not cited yet (Europe PMC); 72 references in the paper

Abstract

Bilaterian animals exhibit functional asymmetry—population‐level, directional left–right differences in physiology and behavior, including responses to spatially symmetric environmental challenges. Whether such symmetry‐to‐asymmetry conversion can be driven at the systems level by neurohormonal regulators remains unclear. Here we tested whether a spatially symmetric neuroendocrine challenge—water deprivation (WD)—can elicit a directional left–right physiological response in rats using hindlimb postural asymmetry (HL‐PA), a binary readout that quantifies left‐ versus right‐sided hindlimb flexion. Twenty‐four hours of WD induced robust HL‐PA with right hindlimb flexion, revealed under anesthesia. The asymmetry persisted after complete thoracic spinal cord transection, suggesting that humoral signaling, rather than descending neural commands, maintains the postural bias. Dehydration activated the hypothalamic arginine vasopressin (AVP) system. Furthermore, a V1B antagonist (SSR‐149415) and a V1A/V2 antagonist (conivaptan) abolished and partially attenuated WD‐induced HL‐PA, respectively, supporting an AVP‐dependent mechanism that likely operates at least two anatomical sites. AVP signaling may involve pituitary V1B‐dependent endocrine output and spinal V1A actions; consistent with the latter, expression of AVP V1A receptors is right‐biased in lumbar spinal cord. Together, these findings identify WD as a symmetric systemic challenge capable of unmasking a directional peripheral bias and implicate vasopressin signaling in left–right physiological regulation. More broadly, they suggest that symmetric homeostatic challenges engage neuroendocrine mechanisms that shift the balance between left‐ and right‐sided physiological functions.

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 4 matches between paragraphs and lines of code.

YaromirKo/biostatistics-wd

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: e5adf0d53cbc24f9a6c30099b0874064dcdb87ea, 31 August 2026

Languages: R (23)

Size: 86 files, 23 scripts

Software Heritage: not archived

Found in: “DATA AVAILABILITY STATEMENT”

Holds: README, license file, 22 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: tidyverse (2 files), brms (1 file), cowplot (1 file), emmeans (1 file), patchwork (1 file), Stan (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

25 files

1_task_mpa_pas_pa/report_MPA_wd_vs_control.Rmd, R, 433 lines
1_task_mpa_pas_pa/report_PAS_wd_control.Rmd, R, 417 lines
1_task_mpa_pas_pa/report_Pa_wd_control.Rmd, R, 413 lines
2_task_mpa_pas/report_MPA_wd_ssr_conivaptan.Rmd, R, 434 lines
2_task_mpa_pas/report_MPA_wd_ssr_conivaptan_control_2_3.Rmd, R, 434 lines
2_task_mpa_pas/report_PAS_wd_ssr_conivaptan.Rmd, R, 436 lines
2_task_mpa_pas/report_PAS_wd_ssr_conivaptan_control_2_3.Rmd, R, 436 lines
3_task_mpa_pas/report_MPA_wd_sp.Rmd, R, 431 lines
3_task_mpa_pas/report_MPA_wd_sp_control_2_3.Rmd, R, 431 lines
3_task_mpa_pas/report_PAS_wd_sp.Rmd, R, 433 lines
3_task_mpa_pas/report_PAS_wd_sp_control_2_3.Rmd, R, 434 lines
BayesianPValue.R, R, 149 lines, 1 match
force_measurement/report_SF_preprocess.Rmd, R, 466 lines, 1 match
force_measurement/task_1/report_SF_AI.Rmd, R, 328 lines, 1 match
force_measurement/task_1/report_SF_dw.Rmd, R, 349 lines
force_measurement/task_2/report_SF_AI.Rmd, R, 327 lines, 1 match
force_measurement/task_2/report_SF_dw.Rmd, R, 346 lines
force_measurement/task_2_2/report_SF_AI.Rmd, R, 335 lines
force_measurement/task_2_2/report_SF_dw.Rmd, R, 357 lines
force_measurement/task_3/report_SF_AI.Rmd, R, 336 lines
force_measurement/task_3/report_SF_dw.Rmd, R, 355 lines
force_measurement/task_3_2/report_SF_AI.Rmd, R, 336 lines
force_measurement/task_3_2/report_SF_dw.Rmd, R, 355 lines
LICENSE, License, 21 lines
README.md, Text, 187 lines

At the source: github.com/YaromirKo/biostatistics-wd

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

Tracing map

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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;
23 scripts, each with its path and the digest of its content;
4 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

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

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. Data supporting the findings of this study and all codes used for analysis are available within the article, its Supporting Information and on https://github.com/YaromirKo/biostatistics-wd.

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

Versions

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Version 3, 28 September 2026

Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 15 MeSH terms, 2 funders, 66 references.

Cite

This paper

Watanabe, H., Kobikov, Y., Mohamed, S. Y., Rich, K., Sarkisyan, D., Nosova, O., Grönbladh, A., Hallberg, M., Schouenborg, J., Bakalkin, G., & Zhang, M. (2026). A symmetric systemic challenge elicits a right-biased response mediated by vasopressin signaling. Journal of neuroendocrinology, 38(9), e70257. https://doi.org/10.1111/jne.70257

BibTeX

@article{watanabe2026symmetric, author = {Watanabe, Hiroyuki and Kobikov, Yaromir and Mohamed, Sara Yusuf and Rich, Karen and Sarkisyan, Daniil and Nosova, Olga and Grönbladh, Alfhild and Hallberg, Mathias and Schouenborg, Jens and Bakalkin, Georgy and Zhang, Mengliang}, title = {{A symmetric systemic challenge elicits a right-biased response mediated by vasopressin signaling}}, journal = {Journal of neuroendocrinology}, year = {2026}, month = sep, volume = {38}, number = {9}, pages = {e70257}, publisher = {Wiley}, issn = {0953-8194}, doi = {10.1111/jne.70257}, url = {https://doi.org/10.1111/jne.70257}, pmid = {42702826}, pmcid = {PMC13547646} }

RIS

TY - JOUR AU - Watanabe, Hiroyuki AU - Kobikov, Yaromir AU - Mohamed, Sara Yusuf AU - Rich, Karen AU - Sarkisyan, Daniil AU - Nosova, Olga AU - Grönbladh, Alfhild AU - Hallberg, Mathias AU - Schouenborg, Jens AU - Bakalkin, Georgy AU - Zhang, Mengliang TI - A symmetric systemic challenge elicits a right-biased response mediated by vasopressin signaling T2 - Journal of neuroendocrinology J2 - J Neuroendocrinol PY - 2026 DA - 2026/09/01 VL - 38 IS - 9 SP - e70257 SN - 0953-8194 PB - Wiley DO - 10.1111/jne.70257 UR - https://doi.org/10.1111/jne.70257 LA - en ER -

CSL-JSON

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