Lung inflammation induced by silica particles triggers hippocampal inflammation, synapse damage and memory impairment in mice
Bibliographic record
Abstract
Abstract Background Considerable evidence indicates that a signaling crosstalk between the brain and periphery plays important roles in neurological disorders, and that both acute and chronic peripheral inflammation can produce brain changes leading to cognitive impairments. Recent clinical and epidemiological studies have revealed an increased risk of cognitive impairment and dementia in individuals with impaired pulmonary function. However, the mechanistic underpinnings of this association remain unknown. Exposure to SiO2 (silica) particles triggers lung inflammation, including infiltration by peripheral immune cells and upregulation of pro-inflammatory cytokines. We here utilized a mouse model of lung silicosis to investigate the crosstalk between lung inflammation and memory. Methods Silicosis was induced by intratracheal administration of a single dose of 2.5 mg SiO2/kg in mice. Molecular and behavioral measurements were conducted 24 h and 15 days after silica administration. Lung and hippocampal inflammation were investigated by histological analysis and by determination of pro-inflammatory cytokines. Hippocampal synapse damage, amyloid-β (Aβ) peptide content and phosphorylation of Akt, a proxy of hippocampal insulin signaling, were investigated by Western blotting and ELISA. Memory was assessed using the open field and novel object recognition tests. Results Administration of silica induced alveolar collapse, lung infiltration by polymorphonuclear (PMN) cells, and increased lung pro-inflammatory cytokines. Lung inflammation was followed by upregulation of hippocampal pro-inflammatory cytokines, synapse damage, accumulation of the Aβ peptide, and memory impairment in mice. Conclusion The current study identified a crosstalk between lung and brain inflammatory responses leading to hippocampal synapse damage and memory impairment after exposure to a single low dose of silica in mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".