Sobre la salud mental: algunas notas sobre la salud mental, los lazos sociales y otras yerbas
Bibliographic record
Abstract
Prolonged inflammation after spinal cord injury is detrimental to recovery. To find pharmacological modulators of the inflammation response, we designed a rapid drug screening paradigm in larval zebrafish followed by testing of hit compounds in a mouse spinal cord injury model. <b>Methods:</b> We used reduced <i>il-1β</i> linked green fluorescent protein (GFP) reporter gene expression as a read-out for reduced inflammation in a screen of 1081 compounds in larval zebrafish. Hit drugs were tested in a moderate contusion model in mice for cytokine regulation, and improved tissue preservation and locomotor recovery. <b>Results:</b> Three compounds robustly reduced <i>il-1β</i> expression in zebrafish. Cimetidine, an over-the-counter H2 receptor antagonist, also reduced the number of pro-inflammatory neutrophils and rescued recovery after injury in a zebrafish mutant with prolonged inflammation. Cimetidine action on <i>il-1β</i> expression levels was abolished by somatic mutation of H2 receptor <i>hrh2b</i>, suggesting specific action. In mice, systemic treatment with Cimetidine led to significantly improved recovery of locomotor behavior as compared to controls, accompanied by decreased neuronal tissue loss and a shift towards a pro-regenerative profile of cytokine gene expression. <b>Conclusion:</b> Our screen revealed H2 receptor signaling as a promising target for future therapeutic interventions in spinal cord injury. This work highlights the usefulness of the zebrafish model for rapid screening of drug libraries to identify therapeutics to treat mammalian spinal cord injury.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".