Antimycobacterial strategies to evade antimicrobial resistance in the nontuberculous mycobacteria
Bibliographic record
Abstract
The nontuberculous mycobacteria (NTM) have recently emerged as important bacterial pathogens of both animals and humans. Of particular, concern is the high level of antimicrobial resistance (AMR) displayed by these organisms, which complicates treatment and potential successful outcomes. This review, therefore, wishes to examine novel compounds and approaches to combatting AMR in the NTMs, specifically examining antimycobacterial (NTM) compounds from plants and venoms, as well as examining synergistic and combination effects with other antimicrobials. Novel and modified drugs including new inhaled drugs are examined, as well as the repurposing of existing drugs for antimycobacterial activity. Many of these novel interventions are at various stages of development, from initial concept through to licensed intervention. The challenge remains to translate these interventions from in vitro laboratory models to effective in vivo interactions. When these are realized, then we will have the opportunity of overcoming NTM AMR, to the benefit of medicine, society, and humanity.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".