Maternal Substance Use and Early-Life Adversity: Inducing Drug Dependence in Offspring, Interactions, Mechanisms, and Treatments
Bibliographic record
Abstract
The likelihood of substance dependency in offspring is increased in cases when there is a family history of drug or alcohol use. Mothering is limited by maternal addiction because of the separation. Maternal separation (MS) leads to the development of behavioural and neuropsychiatric issues in the future. Despite the importance of this issue, empirical investigations of the influences of maternal substance use and separation on substance use problems in offspring are limited, and studies that consider both effects are rare. This study aims to review a few studies on the mechanisms, treatments, genetics, epigenetics, molecular and psychological alterations, and neuroanatomical regions involved in the dependence of offspring who underwent maternal addiction and separation. The PubMed database was used. A total of 95 articles were found, including the most related ones in the review. The brain's lateral paragigantocellularis (LPGi), nucleus accumbens (NAc), caudate-putamen (CPu), prefrontal cortex (PFC), and hippocampus, can be affected by MS. Dopamine receptor subtype genes, alcohol biomarker minor allele, and preproenkephalin mRNA may be affected by alcohol or substance use disorders. After early-life adversity, histone acetylation in the hippocampus may be linked to brain-derived neurotrophic factor (BDNF) gene epigenetics and glucocorticoid receptors (GRs). The adverse early-life experiences differ in offspring›s genders and rewire the brain›s dopamine and endocannabinoid circuits, making offspring more susceptible to dependence. Related psychological factors rooted in early-life stress (ELS) and parental substance use disorder (SUD). Treatments include antidepressants, histone deacetylase inhibitors, lamotrigine, ketamine, choline, modafinil, methadone, dopamine, cannabinoid 1 receptor agonists/antagonists, vitamins, oxytocin, tetrahydrocannabinol, SR141716A, and dronabinol. Finally, the study emphasizes the need for multifaceted strategies to prevent these outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".