Implication of SSAT by Gene Expression and Genetic Variation in Suicide and Major Depression
Bibliographic record
Abstract
CONTEXT: A large body of evidence suggests that predisposition to suicide, an important public health problem, is mediated to a certain extent by neurobiological factors. OBJECTIVE: To investigate patterns of expression in suicide with and without major depression and to identify new molecular targets that may play a role in the neurobiology of these conditions. DESIGN: Brain gene expression analysis was performed using the Affymetrix HG-U133 chipset in the orbital cortex (Brodmann area [BA] 11), the dorsolateral prefrontal cortex (BA8/9), and motor cortex (BA4). Subsequent studies were carried out in independent samples from adjacent areas to validate positive findings, confirm their relevance at the protein level, and investigate possible effects of genetic variation. SUBJECTS: We investigated 12 psychiatrically normal control subjects and 24 suicide victims, including 16 with and 8 without major depression, in the brain gene expression analysis, validation, and protein studies. The genetic studies included 181 suicide completers and 80 psychiatrically normal controls. All subjects investigated were male and of French Canadian origin. MAIN OUTCOME MEASURES: Gene expression measures from microarray, semiquantitative reverse transcription-polymerase chain reaction, immunohistochemistry, and Western blot analyses. RESULTS: Twenty-six genes were selected because of the consistency of their expression pattern (fold change, >1.3 in either direction [P
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".