Non-Canonical Host Intracellular Niche Links to New Antimicrobial Resistance Mechanism
Bibliographic record
Abstract
Globally, infectious diseases are one of the leading causes of death among people of all ages. The development of antimicrobials to treat infectious diseases has been one of the most significant advances in medical history. Alarmingly, antimicrobial resistance is a widespread phenomenon that will, without intervention, make currently treatable infections once again deadly. In an era of widespread antimicrobial resistance, there is a constant and pressing need to develop new antibacterial drugs. Unraveling the underlying resistance mechanisms is critical to fight this crisis. In this review, we summarize some emerging evidence of the non-canonical intracellular life cycle of two priority antimicrobial-resistant bacterial pathogens: Pseudomonas aeruginosa and Staphylococcus aureus. The bacterial factors that modulate this unique intracellular niche and its implications in contributing to resistance are discussed. We then briefly discuss some recent research that focused on the promises of boosting host immunity as a combination therapy with antimicrobials to eradicate these two particular pathogens. Finally, we summarize the importance of various strategies, including surveillance and vaccines, in mitigating the impacts of antimicrobial resistance in general.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".