Schizophrenia: an integrative approach to modelling a complex disorder
Bibliographic record
Abstract
The discovery of candidate susceptibility genes for schizophrenia and the generation of mice lacking proteins that reproduce biochemical processes that are disrupted in this mental illness offer unprecedented opportunities for improved modelling of this complex disorder. Several lines of evidence indicate that obstetrical complications, as well as fetal or neonatal exposure to viral infection, are predisposing events for some forms of schizophrenia. These environmental events can be modelled in animals, resulting in some of the characteristic features of schizophrenia; however, animal models have yet to be developed that encompass both environmental and genetic aspects of this mental illness. A large number of candidate schizophrenia susceptibility genes have been identified that encode proteins implicated in the regulation of synaptic plasticity, neurotransmission, neuronal migration, cell adherence, signal transduction, energy metabolism and neurite outgrowth. In support of the importance of these processes in schizophrenia, mice that have reduced levels or completely lack proteins that control glutamatergic neurotransmission, neuronal migration, cell adherence, signal transduction, neurite outgrowth and synaptic plasticity display many features reminiscent of schizophrenia. In the present review, we discuss strategies for modelling schizophrenia that involve treating mice that bear these mutations in a variety of ways to better model both environmental and genetic factors responsible for this complex mental illness according to a “two-hit hypothesis.” Because rodents are able to perform complex cognitive tasks using odour but not visual or auditory cues, we hypothesize that olfactory-based tests of cognitive performance should be used to search for novel therapeutics that ameliorate the cognitive deficits that are a feature of this devastating mental disorder.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".