Transglutaminase 2 as a therapeutic target for neurological conditions
Bibliographic record
Abstract
INTRODUCTION: Transglutaminase 2 (TG2) has been implicated in numerous neurological conditions, including neurodegenerative diseases, multiple sclerosis, and CNS injury. Early studies on the role of TG2 in neurodegenerative conditions focused on its ability to 'crosslink' proteins into insoluble aggregates. However, more recent studies have suggested that this is unlikely to be the primary mechanism by which TG2 contributes to the pathogenic processes. Although the specific mechanisms by which TG2 is involved in neurological conditions have not been clearly defined, TG2 regulates numerous cellular processes through which it could contribute to a specific disease. Given the fact that TG2 is a stress-induced gene and elevated in disease or injury conditions, TG2 inhibitors may be useful neurotherapeutics. AREAS COVERED: Overview of TG2 and different TG2 inhibitors. A brief review of TG2 in neurodegenerative diseases, multiple sclerosis and CNS injury and inhibitors that have been tested in different models. Database search: https://pubmed.ncbi.nlm.nih.gov prior to 1 July 2021. EXPERT OPINION: Currently, it appears unlikely that inhibiting TG2 in the context of neurodegenerative diseases would be therapeutically advantageous. However, for multiple sclerosis and CNS injuries, TG2 inhibitors may have the potential to be therapeutically useful and thus there is rationale for their further development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".