Increased susceptibility to pneumonia due to tumour necrosis factor inhibition and prospective immune system rescue via immunotherapy
Bibliographic record
Abstract
Immunomodulators such as tumour necrosis factor (TNF) inhibitors are used to treat autoimmune conditions by reducing the magnitude of the innate immune response. Dampened innate responses pose an increased risk of new infections by opportunistic pathogens and reactivation of pre-existing latent infections. The alteration in immune response predisposes to increased severity of infections. TNF inhibitors are used to treat autoimmune conditions such as rheumatoid arthritis, juvenile arthritis, psoriatic arthritis, transplant recipients, and inflammatory bowel disease. The efficacies of immunomodulators are shown to be varied, even among those that target the same pathways. Monoclonal antibody-based TNF inhibitors have been shown to induce stronger immunosuppression when compared to their receptor-based counterparts. The variability in activity also translates to differences in risk for infection, moreover, parallel, or sequential use of immunosuppressive drugs and corticosteroids makes it difficult to accurately attribute the risk of infection to a single immunomodulatory drug. Among recipients of TNF inhibitors,Mycobacterium tuberculosishas been shown to be responsible for 12.5-59% of all infections;Pneumocystis jiroveciihas been responsible for 20% of all non-viral infections; andLegionella pneumophilainfections occur at 13-21 times the rate of the general population. This review will outline the mechanism of immune modulation caused by TNF inhibitors and how they predispose to infection with a focus onMycobacterium tuberculosis,Legionella pneumophila, andPneumocystis jirovecii. This review will then explore and evaluate how other immunomodulators and host-directed treatments influence these infections and the severity of the resulting infection to mitigate or treat TNF inhibitor-associated infections alongside antibiotics.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".