Benzodiazepines compromise the outcome of cancer immunotherapy
Bibliographic record
Abstract
) acts on the gamma-amino butyric acid (GABA) receptor type A via a specific binding site that is shared by diazepam and other benzodiazepines. Both ACBP/DBI and benzodiazepines act as positive allosteric modulators, hence increasing GABA effects on this receptor. Recently, we found that ACBP/DBI acts as an endogenous immunosuppressor, meaning that its antibody-mediated neutralization has immunostimulatory effects and enhances the efficacy of immunotherapy and chemoimmunotherapy in mouse models. Driven by these considerations, we investigated whether diazepam administration in mice would reverse the beneficial effects of ACBP/DBI neutralization on cancer chemoimmunotherapy. Indeed, diazepam abolished the therapeutic of anti-ACBP/DBI antibodies, supporting the idea that diazepam exerts immunosuppressive properties. Of note, treatment with benzodiazepines was associated with poor clinical responses to chemoimmunotherapy in patients with non-small cell lung cancer (NSCLC) as compared to individuals not receiving any psychotropic drugs. Medication with other psychotropic drugs than benzodiazepines did not compromise the outcome of chemoimmunotherapy, indicating that this immunosuppressive effect was benzodiazepine specific. We conclude that benzodiazepines may confer systemic immunosuppression. This hypothesis requires further epidemiological and clinical confirmation.
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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 | 0.000 |
| 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 | 0.000 | 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".