SYMPTOMATIC REMISSION AND RECOVERY IN SCHIZOPHRENIA SPECTRUM DISORDERS: IS THERE A ROLE FOR BDNF?
Bibliographic record
Abstract
Schizophrenia is a chronic early-onset disease with a lifetime prevalence up to 1% of the population worldwide.Injuries during brain development are considered a key etiopathogenic mechanism that would lead to the dysfunctions observed in schizophrenia.In this context, a double-hit model has been proposed: a prenatal priming event (i.e.maternal infection during pregnancy) that would induce vulnerability, followed by a second hit in peripuberty (i.e drug abuse).Cannabis consumption in early adolescence, a period of increased vulnerability to its effects, increases the risk of developing schizophrenia in vulnerable subjects.However, mechanisms underlying this effect remain unknown.In this context, it has been suggested that dysfunctions in the Akt/mTOR/S6 signaling pathway may lead to a dendritic and synaptic disorganization that confers vulnerability to cannabis induced risk for schizophrenia.For further exploring this hypothesis we evaluated the status of several proteins of the Akt/mTOR/S6 pathway in a "double-hit" rodent model of schizophrenia based on i) a prenatal neurodevelopment deficit induced by maternal immune activation (MIA) during pregnancyfollowed by ii) THC administration in peripuberty ().For that, poly(I: C) (5 mg/kg i.p. or its vehicle) was administered to female mice in gestation day 9.5.After weaning (PND21), the generated pups were subjected to THC (10 mg/kg/day i.p, 30 days) or vehicle administration.After a 5-day washout period, mice were sacrificed and brains removed.The levels of Akt/phospho-(Thr 450)Akt, ERK/ phospho-(Thr202/Tyr204)ERK and mTOR/ phospho-(S448)mTOR were quantified in the brain cortex.Results showed a significant interaction between the effect of THC and MIA in the levels of Akt, ERK and mTOR and their phosphorylated forms suggesting the involvement of these signaling proteins in the molecular mechanism underlying vulnerability to cannabis for the development of schizophrenia.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".