Microglial Ultrastructure in the Hippocampus of a Lipopolysaccharide-Induced Sickness Mouse Model
Bibliographic record
Abstract
Sickness behavior is a set of behavioral changes induced by infections and mediated by pro-inflammatory cytokines. It is characterized by fatigue, decreased appetite and weight loss, changes in sleep patterns, cognitive functions, and lost interest in social activity. It can expedite recovery by conserving energy to mount an immune response involving innate immunity. To provide insights into microglial implication in sickness behavior with special focus on cognitive and social impairment, we investigated changes in their ultrastructure and interactions with synapses using a toxemia mouse model. Adult mice were injected with 1 mg/kg lipopolysaccharide (LPS) or saline, and assayed for signs of sickness behavior. LPS treated mice displayed reduced activity in open-field tests 24 h post-injection, while social avoidance and weight gain/loss were not significantly different between treatment groups. Microglia were investigated using electron microscopy to describe changes in their structure and function at nanoscale resolution. Microglial cell bodies and processes were investigated in the hippocampus CA1, a region responsible for learning and memory that is often impacted after peripheral LPS administration. Microglia in LPS treated animals displayed larger cell bodies as well as less complex processes at the time point examined. Strikingly, microglial processes in LPS injected animals were also more likely to contact excitatory synapses and contained more phagocytic material compared with saline injected controls. We have identified at the ultrastructural level significant changes in microglia-synapse interactions shortly after LPS administration, which draws attention to studying the roles of microglia in synaptic rewiring after inflammatory stimuli.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".