Novel microglia-mediated mechanisms underlying synaptic loss and cognitive impairment after traumatic brain injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) is one of the leading causes of long-term neurological disability in the world. Currently, there are no therapeutics for treating the deleterious consequences of brain trauma; this is in part due to a lack of complete understanding of cellular processes that underlie TBI-related pathologies. Following TBI, microglia, the brain resident immune cells, turn into a "reactive" state characterized by the production of inflammatory mediators that contribute to the development of cognitive deficits. Utilizing multimodal, state-of-the-art techniques that widely span from ultrastructural analysis to optogenetic interrogation of circuit function, we investigated the reactive microglia phenotype one week after injury when learning and memory deficits are also measured. Microglia displayed increased: (i) phagocytic activity in vivo, (ii) synaptic engulfment, (iii) increased neuronal contact, including with dendrites and somata (termed 'satellite microglia'). Functionally, satellite microglia might impact somatic inhibition as demonstrated by the associated reduction in inhibitory synaptic drive. Cumulatively, here we demonstrate novel microglia-mediated mechanisms that may contribute to synaptic loss and cognitive impairment after traumatic brain injury.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".