Host-directed immunotherapy to fight infectious diseases
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review provides readers with examples of refractory infections due to inborn errors of immunity, highlighting how they may be successfully treated by deducing and targeting the underlying immunodeficiency. RECENT FINDINGS: The use of host-directed immunotherapy to treat infectious disease in inborn errors of immunity is currently limited but growing. Different strategies include depleting the cellular reservoir for pathogens with restricted cell-tropism; augmenting the diminished effector response; and restoring molecular equipoise. The immunotherapies illustrated are existing drugs that have been re-purposed and rationally used, depending on the molecular or cellular impact of the mutation. As more biologic response modifiers and molecular targeted therapies are developed for other indications, they open the avenues for their use in inborn errors of immunity. Conversely, as more molecular pathways underlying defective immune responses and refractory infections are elucidated, they lend themselves to tractability with these emerging therapies. SUMMARY: Infections that fail appropriate antimicrobial therapy are a harbinger of underlying inborn errors of immunity. Dissecting the mechanism by which the immune system fails provides opportunities to target the host response and make it succeed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".