An automated group-housed oral fentanyl self-administration method in mice
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Social factors play a critical role in human drug addiction, and humans often consume drugs together with their peers. In contrast, in traditional animal models of addiction, rodents consume or self-administer the drug in their homecage or operant self-administration chambers while isolated from their peers. Here, we describe HOMECAGE ("Home-cage Observation and Measurement for Experimental Control and Analysis in a Group-housed Environment"), a translationally relevant method for studying oral opioid self-administration in mice. This setting reduces experimental confounds introduced by social isolation or interaction with the experimenter. METHODS: We have developed HOMECAGE, a method in which mice are group-housed and individually monitored for their consumption of a drug vs. a reference liquid. RESULTS: Mice in HOMECAGE preserve naturalistic aspects of behavior, including social interactions and circadian activity. The mice showed a preference for fentanyl and escalated their fentanyl intake over time. Mice preferred to consume fentanyl in bouts during the dark cycle. Mice entrained to the reinforcement schedule of the task, optimizing their pokes to obtain fentanyl rewards, and maintained responding for fentanyl under a progressive ratio schedule. HOMECAGE also enabled the detection of cage-specific and individual-specific behavior patterns and allowed the identification of differences in fentanyl consumption between co-housed control and experimental mice. CONCLUSIONS: HOMECAGE serves as a valuable procedure for translationally relevant studies on oral opioid intake under conditions that more closely mimic the human condition. The method enables naturalistic investigation of factors contributing to opioid addiction-related behaviors and can be used to identify novel treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".