Bibliographic record
Abstract
Painful experiences are personal, subjective, complex and do not always match the level of noxious input experienced. Instead, pain is influenced by several psychosocial factors that can enhance or inhibit the perception of pain. To effectively study pain, the role of the social context needs to be clarified. These studies aimed to deconstruct the roles of social context, sex, and hormonal mechanisms in the social modulation of pain. Social interactions result in the physiological release of hormones, which can be rewarding or stressful depending on context, hormonal release, and receptor dynamics. Here, I carried out several experiments to further understand the complex relationships between social context, sex, social interactions, and their effects on pain. In the first study, I demonstrate the role of glucocorticoid receptors (GR) in suppressing emotional contagion in mice in an unfamiliar social context. Stranger dyads exhibit significantly elevated GR activity in the prelimbic cortex of the mPFC. Acute inhibition of GR in the prelimbic cortex was sufficient to elicit pain contagion in strangers, while their activation prevented pain contagion in cagemate dyads. Together these findings provide novel evidence towards the neural mechanism underlying the prevention of pain contagion. In my second study, I examined the role of mu, delta, and kappa opioid receptors in modulating social approach behaviours towards a sibling in pain. Systemic blockade of opioid receptors (MOR, DOR and KOR) all reversed social preference for a female sibling in pain. Pharmacological inactivation of the mid cingulate cortex reversed social approach preference behaviour towards a sibling in pain. In my final study, I demonstrate that social interactions reduce pain and anxiety behaviours in reunited female sibling mice. In female sibling mice, c-fos was elevated in the oxytocinergic neurons of the PVN, cells within the PAG and reduced in the basolateral amygdala compared to mice paired with a stranger or tested alone. Together these findings point towards distinct modulation of pain-related behaviours due to social context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".