Targeting inflammation to influence mood following spinal cord injury: a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: The purpose of the present study was to examine the efficacy of targeting inflammation as a means of improving mood following spinal cord injury (SCI) and explore the potential mechanisms of action. METHODS: The study was a randomized, parallel-group, controlled, clinical trial (NCT02099890) whereby 20 participants with varying levels and severities of SCI were randomized (3:2) to either the treatment group, consisting of a 12-week anti-inflammatory diet, or control group. Outcome measures were assessed at baseline, 1 and 3 months, and consisted of CES-D scores of depression, markers of inflammation as assessed by various pro- and anti-inflammatory cytokines and several amino acids related to depression. RESULTS: A significant group × time interaction was found for CES-D (Center for Epidemiologic studies Depression Scale) score (p = 0.01), the TRP/LNAA (tryptophan/large neutral amino acid) ratio (p = 0.04), the composite score of pro-inflammatory cytokines (p = 0.04), IL-1β (interleukin-1 beta) (p = 0.04), and IFN-γ (interferon gamma) (p = 0.03). Pearson's r correlation showed significance between the ∆IL-1β and both the ∆CES-D score (r = 0.740, p < 0.01) and the ∆KYN/TRP (kynurenine/tryptophan) ratio (r = 0.536, p = 0.02). The ∆KYN/TRP ratio was also significantly correlated with the ∆CES-D score (r = 0.586, p = 0.01). Mediation analysis showed that the relationship between the ∆KYN/TRP ratio and the ∆CES-D score was mediated by the ∆IL-1β. Subgroup analysis showed that participants with high CES-D scores had significantly higher concentrations of IL-1β, and all correlations were maintained or strengthened within this subgroup. CONCLUSIONS: Overall, the results demonstrated the effectiveness of targeting inflammation as a means of improving mood in SCI, with potential mechanisms relating to the reduction in IL-1β and improvements in levels of neuroactive compounds related to the kynurenine pathway. Due to the limited sample size, results should be interpreted with caution; however, they are worthy of further examination due to the potential impact of inflammation on depression. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02099890 .
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".