Microglial activating transcription factor 3 upregulation: An indirect target to attenuate inflammation in the nervous system
Bibliographic record
Abstract
Activating Transcription Factor 3 (ATF3) is upregulated in reaction to several cellular stressors found in a wide range of pathological conditions to coordinate a transcriptional response. ATF3 was first implicated in the transcriptional reaction to axotomy when its massive upregulation was measured in sensory and motor neuron cell bodies following peripheral nerve injury. It has since been shown to be critical for successful axon regeneration in the peripheral nervous system and a promising target to mitigate regenerative failure in the central nervous system. However, much of the research to date has focused on ATF3's function in neurons, leaving the expression, function, and therapeutic potential of ATF3 in glia largely unexplored. In the immunology literature ATF3 is seen as a master regulator of the innate immune system. Specifically, in macrophages following pathogen or damage associated molecular pattern receptor activation and subsequent cytokine release, ATF3 upregulation abrogates the inflammatory response. Importantly, ATF3 upregulation is not exclusively due to cellular stress exposure but has been achieved by the administration of several small molecules. In the central nervous system, microglia represent the resident macrophage population and are therefore of immediate interest with respect to ATF3 induction. It is our perspective that the potential of inducing ATF3 expression to dampen inflammatory microglial phenotype represents an unexplored therapeutic target and may have synergistic benefits when paired with concomitant neuronal ATF3 upregulation. This would be of particular benefit in pathologies that involve both detrimental inflammation and neuronal damage including spinal cord injury, multiple sclerosis, and stroke.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".