Severe neurological impairment and immune function: altered neutrophils, monocytes, T lymphocytes, and inflammasome activation
Bibliographic record
Abstract
BACKGROUND: Infections cause significant morbidity and mortality in children with Severe Neurological Impairment (SNI). Alterations in immune cell numbers and function in children with neurodisability have been reported. We aimed to characterise neutrophil, monocyte and lymphocyte proportions and activation, at baseline and in response to stimulation with lipopolysaccharide, in children with SNI compared to healthy controls. METHODS: Whole blood samples of children with SNI and controls were incubated in the presence or absence of lipopolysaccharide (10 ng/ml). Monocyte and neutrophil function (Cluster of Differentiation (CD)11b, (TLR)-4 and CD66b expression) and lymphocytes were assessed by flow cytometry. Expression of genes involved in the inflammasome (NLR Family Pyrin Domain Containing(NLRP)-3, Apoptosis-Associated Speck-like protein (ASC) and Interleukin(IL)1β) were assessed by PCR. RESULTS: Monocytes and CD8+ T cells were lower in children with SNI (n = 14). CD66b, was hyporesponsive and monocyte TLR4 was hyperresponsive to lipopolysaccharide in children with SNI compared to controls (n = 14). NLRP3 expression was higher at baseline and IL1β expression was not upregulated in response to lipopolysaccharide in children with SNI in contrast to controls. CONCLUSION: We have found significant differences in immune regulation in children with SNI compared to controls which may provide a useful therapeutic target in the future. IMPACT: Children with SNI have reduced monocyte and CD8+ T cells. Neutrophils and monocytes in children with SNI show altered markers of activation in response to lipopolysaccharide. Expression of NLRP3 at the RNA level was higher at baseline in children with SNI. This study adds to the existing literature that children with neurological impairment have altered inflammatory and immune cell responses. This may provide a useful therapeutic target to reduce infection-related morbidity and mortality, and tertiary neurological injury in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".