Anti-inflammatory profile of circulating immune cells after surgery for seizure.
Bibliographic record
Abstract
AIM: The central nervous system has been described as the coordinator of the inflammatory response to infection through the hypothalamo-pituitary axis and the autonomic nervous system. Brain lesions have been associated with impaired immunity and postoperative infections. We studied alterations of the inflammatory response in relation to neurohormonal patterns after neurosurgery for seizure. METHODS: Nine patients were studied before, during and immediately after operation, and then on days 1, 2 and 4 post-operatively. Monocyte HLA-DR (mHLA-DR) expression and plasma interleukin (IL)-10, IL-12 and MIF were measured ex vivo and after an in vitro 6-h our lipopolysaccharide (LPS) stimulation of whole blood. Corticotropin (ACTH), cortisol, arginine vasopressin, prolactin, epinephrine and norepinephrine were quantified in plasma. The effect of plasma mediators on LPS stimulation was studied by replacing plasma with standard culture medium. RESULTS: Surgery resulted in decreased ex vivo mHLA-DR expression, but no change in IL-10 or IL-12 plasma levels. mHLA-DR was low in LPS culture over the 4 postoperative days, whereas IL-10 release was increased and not counterbalanced by IL-12p40 production. The hormonal plasma pattern showed increased prolactin during anesthesia and peaks of cortisol, ACTH and arginine vasopressin during waking, but no alteration in catecholamine levels. mHLA-DR expression in LPS culture was not modified by plasma replacement, except immediately after surgery. CONCLUSION: Postoperatively, mHLA-DR expression was associated with an anti-inflammatory phenotype of whole blood. The anti-inflammatory profile was not related to the plasma mediators measured, suggesting that neural control might take place upstream in the circulation, at the level of progenitors in bone marrow.
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) | 0.000 | 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".