Human Brain Chemokine and Cytokine Expression in Sepsis: A Report of Three Cases
Bibliographic record
Abstract
BACKGROUND: Sepsis is a systemic response to infection that can affect brain function by inducing resident cells (including astrocytes and microglia) to generate brain chemokines and cytokines. However, there are few studies on the human brain. Since this information may shed further light on pathogenesis, our study objective was to measure the expression of 36 chemokines and cytokines in autopsied brain from 3 cases of sepsis and 10 controls, and to relate this to astrocyte and microglial activation. METHODS: The right frontal pole was removed at autopsy and chemokine and cytokine expression measured by multiplexed enzyme-linked immunosorbent assay and real-time quantitative polymerase chain reaction (qPCR). Immunohistochemistry and image analysis were carried out to determine the expression of glial fibrillary acidic protein (GFAP), a marker of activated astrocytes, and CD68 and CD45, markers of activated microglial cells. RESULTS: Concentrations of the chemokines CXCL8, CXCL10, CXCL12, CCL13 and CCL22 were increased in pooled data from the three cases of sepsis (p<0.05); however, their messenger RNA (mRNA) expression was unaltered. CXCL13, CXCL1, CXCL2, CCL1, CCL2, CCL8, CCL20, (interleukin) IL-16, IL-1β and (tumour necrosis factor) TNF concentrations showed increases in two of three sepsis cases. Additionally, individual sepsis cases showed increases in mRNA expression for HDAC (histone deacetylase) 6 and EIF (eukaryotic translation initiation factor) 4A2. Brain GFAP expression was significantly increased (p<0.05) in pooled data from the three sepsis cases. Individual sepsis cases showed increases in CD68 or CD45 expression. CONCLUSIONS: These expression patterns add to our understanding of the pathogenesis of sepsis and its effects on the brain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".