An Automated Culture System for Use in Preclinical Testing of Host-Directed Therapies for Tuberculosis
Bibliographic record
Abstract
Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), was the most significant infectious disease killer globally until the advent of COVID-19. Mtb has evolved to persist in its intracellular environment, evade host defenses, and has developed resistance to many anti-tubercular drugs. One approach to solving resistance is identifying existing approved drugs that will boost the host immune response to Mtb. These drugs could then be repurposed as adjunctive host-directed therapies (HDT) to shorten treatment time and help overcome antibiotic resistance. Quantification of intracellular Mtb growth in macrophages is a crucial aspect of assessing potential HDT. The gold standard for measuring Mtb growth is counting colony-forming units (CFU) on agar plates. This is a slow, labor-intensive assay that does not lend itself to rapid screening of drugs. In this protocol, an automated, broth-based culture system, which is more commonly used to detect Mtb in clinical specimens, has been adapted for preclinical screening of host-directed therapies. The capacity of the liquid culture assay system to investigate intracellular Mtb growth in macrophages treated with HDT was evaluated. The HDTs tested for their ability to inhibit Mtb growth were all-trans Retinoic acid (AtRA), both in solution and encapsulated in poly(lactic-co-glycolic acid) (PLGA) microparticles and the combination of interferon-gamma and linezolid. The advantages of this automated liquid culture-based technique over the CFU method include simplicity of setup, less labor-intensive preparation, and faster time to results (5-12 days compared to 21 days or more for agar plates).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".