The molecular basis and downstream immune consequences of mycobacteria–host cell interactions
Bibliographic record
Abstract
Pathogenic mycobacteria gain entry to their hosts by inhalation or ingestion where they adhere to different cell types and are subsequently internalized by professional phagocytic cells, such as macrophages or dendritic cells. Multiple pathogen-associated molecular patterns present on the mycobacterial surface are recognized by and interact with a diverse panel of phagocytic pattern recognition receptors, representing the first step of the infection process. This review summarizes the current knowledge on the numerous host cell receptors and their associated mycobacterial ligands or adhesins. It further discusses the downstream molecular and cellular events resulting from the engagement of the various receptor-mediated pathways, leading to either intracellular survival of mycobacteria or to activation of host immune defenses. The content presented herein on adhesins and host receptors may serve as a resource for those developing novel therapeutic approaches, e.g. in the design of antiadhesin molecules to prevent bacterial attachment and infection. The collection of mycobacterial surface molecules highlighted in this review may also provide potential new therapeutic targets, diagnostic markers, or vaccine candidates to combat these notoriously challenging and persistent pathogens.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".