Aicardi–Goutieres syndrome: from patients to genes and beyond
Bibliographic record
Abstract
Aicardi-Goutières syndrome (AGS) is a hereditary neurodegenerative disorder characterized mainly by early onset progressive encephalopathy, concomitant with an increase in interferon-α levels in the cerebrospinal fluid. Although it was initially mistaken for intrauterine viral infections, AGS has now been genetically attributed to a lack of adequate processing of cellular nucleic acid debris, which culminates in the perpetual trigger of the innate and acquired immune responses. Although the exact mechanisms governing AGS are not fully understood, significant strides have been recently achieved in better characterizing the disorder and the molecular functions of the five known proteins found mutated in AGS. Studies have now uncovered that AGS is tightly linked with the predisposition to other autoimmune disorders such as familial chilblain lupus and systemic lupus erythematosus. Moreover, at least two of the proteins mutated in AGS, namely TREX1 and SAMHD1, also seem to have antagonistic roles in safeguarding humans from human immunodeficiency virus (HIV) infections. We hereby synthesize the current developments into the greater framework of AGS and suggest that a better understanding of AGS might help usher a better treatment not only for some autoimmune disorders but also possibly for patients suffering from HIV infections, too.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".