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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Mu and delta opioid receptor antagonists increase the expression of social conditioned place preference in early adolescent mice

Authors: Harda, Zofia; Rodriguez Parkitna, Jan; Misiołek, Klaudia; Ziółkowska, Barbara; Szumiec, Lukasz; Klimczak, Marta; Chrószcz, Magdalena; +3 Authors

Mu and delta opioid receptor antagonists increase the expression of social conditioned place preference in early adolescent mice

Abstract

Here we present data concerning the role of endogenous opioid system in the rewarding effects of housing with siblings (as opposed to housing in isolation) in early- and late- adolescent male mice using selective opioid receptor antagonists. Social reward was assessed using the social conditioned place preference task in early-adolescent (~34 days old) and late-adolescent (~41 old) male mice that received a single dose of cyprodime (1 mg/kg, i.p.), naltrindole (1 mg/kg, i.p) or norbinaltorphimine (10 mg/kg, i.p.) before the post test. The behavioral test was performed as previously described: Harda, Z., Chrószcz, M., Misiołek, K., Klimczak, M., Szumiec, Ł., Kaczmarczyk-Jarosz, M., & Rodriguez Parkitna, J. (2022). Establishment of a social conditioned place preference paradigm for the study of social reward in female mice. Scientific Reports, 12(1), 11271. https://doi.org/10.1038/s41598-022-15427-9 Two datasets are uploaded. First contains all data, the second contains data after outlier removal and trimming. The procedures of outlier identification and trimming are described below. Outlier identification. The outlier test (Grubbs’) was performed on the following parameters: score, index 1, index 2, distance moved during the posttest. Outlier test was performed before the trimming of the data. The test was performed separately for the context A and B data, as the social score for context A (beech) was moderately higher than for context B (cellulose), which confirmed our previous results. sCPP data trimming Python script written by Dr. Jakub Dzik (Nencki Institute of Experimental Biology, PAS) was used to trim the sCPP data in order to preserve an unbiased design (https://zenodo.org/record/8100281). The algorithm operates as follows: 1. Segregates animals using the information provided in the column “Group”. 2. Segregates animals in each group into two subgroups using the information provided in the column “Social context”. 3. Counts animals in each subgroup. Excludes animals from a larger subgroup until the subgroups are equal in a following way (loop): - Checks if the mean “Pretest. Time in social context [%]” for a given group is lower than 50% - If yes, mice from the larger subgroup that have “Pretest. Time in social context [%]” lower than 50% are identified - If not, mice from the larger subgroup that have “Pretest. Time in social context [%]” equal or higher than 50% are identified - Random animal from the identified animals is excluded 4. A column is added to the data table containing the information about the version of the trimming script used

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average