Physiological role and potential clinical interest of mycobacterial pigments
Bibliographic record
Abstract
The production of pigments by bacterial colonies has sparked interest among bacteriologists since the 19th century, whether for taxonomy or, in the case of carotenoids for their association with antibiotics resistance. Mycobacteria have gained a very special place in the bacterial world due to their clinical importance. Alone, Mycobacterium tuberculosis is responsible for about two million deaths annually worldwide making tuberculosis one of the most influential diseases in the history of mankind. Almost half of the Nontuberculous Mycobacteria species identified are associated with opportunistic infections in animals and humans. Mycobacterial pigmentary characteristics started to be documented about 80 years ago; but to date, their main use has been only for limited taxonomic and identification purposes. While mycobacterial pigments, especially carotenoids have been clearly associated with cellular photoprotection and survival, the regulation of their production and their physiological role have been largely unstudied. Recent advances in deciphering mycobacterial genomes and characterization of carotenoid synthesis genes, combined with an urgent need for innovative approaches to understand Mycobacterium tuberculosis pathogenic properties open new avenues for exciting research opportunities that might lead to new therapeutic strategies against a devastating secular disease.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".