Measurement of lactate levels in postmortem brain, iPSCs, and animal models of schizophrenia
Bibliographic record
Abstract
Converging evidence suggests bioenergetic defects contribute to the pathophysiology of schizophrenia and may underlie cognitive dysfunction. The transport and metabolism of lactate energetically couples astrocytes and neurons and supports brain bioenergetics. We examined the concentration of lactate in postmortem brain (dorsolateral prefrontal cortex) in subjects with schizophrenia, in two animal models of schizophrenia, the GluN1 knockdown mouse model and mutant disrupted in schizophrenia 1 (DISC1) mouse model, as well as inducible pluripotent stem cells (iPSCs) from a schizophrenia subject with the DISC1 mutation. We found increased lactate in the dorsolateral prefrontal cortex (p = 0.043, n = 16/group) in schizophrenia, as well as in frontal cortical neurons differentiated from a subject with schizophrenia with the DISC1 mutation (p = 0.032). We also found a decrease in lactate in mice with induced expression of mutant human DISC1 specifically in astrocytes (p = 0.049). These results build upon the body of evidence supporting bioenergetic dysfunction in schizophrenia, and suggests changes in lactate are a key feature of this often devastating severe mental illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".