Advance in Research on Mycobacterium tuberculosis FabG4 and Its Inhibitor
Bibliographic record
Abstract
Increasing evidence from recent reports of drug-resistant mycobacterial strains poses a challenge worldwide. Drug-resistant strains often undergo mutations, adopt alternative pathways, and express drug efflux pumps to reduce or eliminate drug doses. Besides these intrinsic resistance mechanisms, bacteria can evade drug doses by forming biofilms. Biofilms are the concerted growth of adherent microorganisms, which can also be formed at the air-water interface. The growth is supported by the extracellular polymer matrix which is self-produced by the microorganisms. Reduced metabolic activity in a nutrient-deficient environment in the biofilm may cause the microorganisms to take alternative pathways that can make the microorganisms recalcitrant to the drug doses. Recent works have shown that Mycobacterium tuberculosis expresses several proteins during its growth in biofilm, those when deleted, did not show any effect on mycobacterial growth in normal nutrient-sufficient conditions. Studying these unconventional proteins in mycobacterial biofilms is therefore of utmost importance. In this article, I will discuss one such mycobacterial biofilm-related protein FabG4 that is recently shown to be important for mycobacterial survival in the presence of antibiotic stressors and limited nutrient condition. In an attempt to find more effective FabG4 inhibitors and its importance in biofilm forming M. tuberculosis, present knowledge about FabG4 and its known inhibitors are discussed. Based on the existing data, a putative role of FabG4 is also suggested.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".