Neurodevelopmental Changes in Offspring Prenatally Exposed to Opioids in Human Children and Rodent Models: A Review
Bibliographic record
Abstract
The number of infants born to opioid-dependent pregnant women in North America is a growing problem. Studies that focus on the long-term effects of neurodevelopmental changes of prenatal opioid exposure in human infants are however limited. The use of rodent models to evaluate these changes may provide some insight. This review focuses on studies of rodent models exposed to opioids such as morphine, heroin, oxycodone, buprenorphine, methadone, and l-α-acetylmethadol in uteroand briefly discusses the neural and behavioural effects in human children. Most of the rodent studies reported the following neural effects: increases in caspase-3 and Bax/Bcl-2 ratio, altered NMDA activity, and decreases in BDNF expression in the offspring prenatally exposed to opioids. In addition, they showed decreases in synaptic plasticity, LTP, LTD, dendritic length, and dendritic branch number. The exposed rodent offspring were more inclined to perform poorly in the behavioural tests. Likewise, some of the human studies reported a significant difference between the exposed group and the control; however, other studies reported insignificant or no significant differences after correcting for covariates. Most of the studies suggest an impairment in learning and memory in the rodent offspring and deficits in behaviour and cognition in human children; however, this was not always the case. It is still not clear whether the effects of prenatal opioid exposure are due to the opioid itself being the prime factor, as various factors may also contribute to the results. Further studies of the effects of early opioid exposure on neurodevelopment in the offspring are required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".