230 NLRP3 Inflamasome May Mediate Adverse Outcomes in Burned Elderly
Bibliographic record
Abstract
Elderly burn patients’ represent 13% to 20% of burn center admission, yet they comprise the highest death rate among the overall burn population. Older adults are more vulnerable to burn injuries due to declining physical, motor, sensory and cognitive performance. We have previously shown ageing-induced decreases in immune function early after burn mediates poor outcomes. Similarly, we have also shown that the acute phase mediator of inflammation, the NLRP3 inflammasome, increases in response to burn mediating essential inflammatory cascades. However, little is known about the role of the NLRP3 in elderly burn patients and its association with the hyperinflammatory response. Adult (<65) and elderly patients (≥65) with burns admitted to our burn centre were studied. Blood samples and subcutaneous WAT (sWAT) collected were analyzed for NLRP3 inflammasome and inflammatory cytokines via gene expression and multiplex assay. Samples were stratified based on early (0–14 days post-burn) and late (≥ 15 days post-burn). Presently, we show that the sWAT of elderly burn patients displays decreased NLRP3 inflammasome gene expression early after burn for NLRP3, IL-1β and IL-18. Specifically, adults had up to 18-fold upregulation whereas in elderly these increases were approximately 2-fold. Interestingly, at later time points NLRP3 inflammasome components returned to baseline in adults while elderly patients had significant increases (IL-1β: 30-fold increase). Lastly, when comparing systemic inflammation, similar results were observed for pro-inflammatory cytokines and chemokines. Our findings suggest that in addition to delayed and prolonged systemic inflammation, elderly burn patients have similar findings in NLRP3 gene expression in sWAT. Thus, this supports the dysfunctional host response in older burn patients and suggests a possible mediator that contributes to poor outcomes. Enhancing our understanding of the inflammatory response systemically and metabolic tissue will enhance our understanding of appropriate treatment regimes aimed at mitigating prolonged inflammation in elderly burn patients. By doing so, this may circumvent immune exhaustion and susceptibility to infection, sepsis and mortality in this vulnerable population.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".