Changes in Galanin Systems in a Rat Model of Post-Traumatic Stress Disorder (PTSD)
Bibliographic record
Abstract
Post-traumatic stress disorder (PTSD) is a chronic syndrome triggered by exposure to trauma and a failure to recover from a normal negative emotional reaction to traumatic stress. The neurobiology of PTSD and the participation of neuropeptides in the neural systems and circuits that control fear and anxiety are not fully understood. The long-term dysregulation of neuropeptide systems contributes to the development of anxiety disorders, including PTSD. The neuropeptide galanin (Gal) and its receptors participate in anxiety-like and depression-related behaviors via the modulation of neuroendocrine and monoaminergic systems. The objective of this research was to investigate how Gal expression changes in the brain of rats 2 weeks after exposure to footshock. Rats exposed to footshocks were subdivided into high responders (HR; immobility>60%) and low responders (LR; immobility<40%) based on immobility elicited by a novel tone one day after exposure. On day 14, rats were anesthetized, and the amygdala, hypothalamus, pituitary and adrenal glands were removed for analysis using real-time polymerase chain reaction (RT-PCR). Gal mRNA levels were increased in the amygdala and hypothalamus of HR compared with the control and LR. In contrast, Gal mRNA levels were decreased in the adrenal and pituitary glands of HR compared with the control and LR. Thus, the differential regulation (dysregulation) of the neuropeptide Gal in these tissues may contribute to anxiety and PTSD development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".