Mild Isoniazid-Induced Liver Injury in Humans Is Associated with an Increase in Th17 Cells and T Cells Producing IL-10
Bibliographic record
Abstract
Isoniazid (INH) remains a mainstay for the treatment of tuberculosis despite the fact that it can cause liver failure. The mechanism of INH-induced liver injury remains controversial. It had been proposed that the mechanism involves metabolic idiosyncrasy based on the observations that liver injury is not usually associated with fever, rash, or prompt increase in alanine aminotransferase (ALT) upon rechallenge. In the present study, we found that patients who were treated with INH because of a positive tuberculosis (TB) skin test and developed a small increase in ALT had an increase in Th17 cells as well as T cells that produce interleukin (IL)-10, which suggests stimulation of an adaptive immune response. Th17 cells are considered inflammatory and could be involved in causing the liver injury. IL-10 is considered anti-inflammatory and could be the reason that more serious liver injury did not occur. These changes were not observed in patients who did not have an increase in ALT. These are the first data to show a change in the T cell profile in patients with mild INH-induced liver injury; however, it is difficult to determine whether these changes were the cause or the result of the liver injury. Nevertheless, together with other studies, the data suggest that INH-induced liver injury is immune-mediated, with mild injury resulting in immune tolerance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".