Neuroprotective effects of resident microglia following acute brain injury
Bibliographic record
Abstract
Microglia quickly react to various neurodegenerative processes by producing cytokines and eliminating cellular debris via phagocytosis. These events are also associated with an increased proliferation of microglia, which derive from resident progenitors and those present in the bone marrow. However, it is not clear whether the innate immune response by resident or newly differentiated microglia is beneficial or detrimental to the central nervous system. The aim of this study was to determine the impact of an altered immune response following acute excitotoxicity. Sodium nitroprusside (SNP) or kainic acid (KA) was administered in the brain of various groups of mice, and the extent of neurodegeneration, myelin damage, and inflammation was evaluated within a period of 2 weeks. We used synthetic glucocorticoid (GC), myeloid differentiation factor 88 (MyD88)-deficient mice to suppress nuclear factor kappaB (NF-kappaB) signaling and transgenic mice that express the thymidine kinase (TK) protein under the control of the CD11b promoter to determine the role of proliferating and infiltrating microglia in acute models of brain injury. Neurodegeneration was more extensive in GC-treated and MyD88-deficient mice, suggesting that NF-kappaB signaling and microglia activation are potent neuroprotective mechanisms in the presence of SNP. KA was also highly toxic to neurons of the amygdala in MyD88 knockout mice but not in their WT littermates. Although bone marrow-derived cells are clearly attracted to neurodegenerative areas, preventing their infiltration and differentiation did not affect the extent of SNP-related damage. These data indicate that MyD88/NF-kappaB signaling in resident non-proliferating microglia plays a critical role by restricting damage during acute excitotoxicity.
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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) | 0.001 | 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 | 0.001 |
| 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".