Dual activity of low-molecular-weight polymeric β-cyclodextrins against tuberculosis
Bibliographic record
Abstract
Tuberculosis remains the leading cause of death from infectious diseases. While effective therapies are available, their prolonged duration and severe adverse effects compromise patient adherence and promote antimicrobial resistance, highlighting the urgent need for innovative therapeutic approaches. We previously identified polymeric β-cyclodextrin (pβCD) as a promising drug delivery platform, possessing intrinsic antibacterial activity and serving as a carrier for the second-line drug ethionamide. Here, we investigated the biological properties of pβCD of different molecular weights. Strikingly, only the low-molecular-weight pβCD (LMW-pβCD) displayed antibacterial activity, in contrast to the high-molecular-weight one. Mechanistically, this activity was linked to the inhibition of Mycobacterium tuberculosis entry into macrophages through disruption of lipid rafts. Transcriptomic profiling further revealed that the LMW-pβCD also enhanced pro-inflammatory cytokine secretion, suggesting dual antimicrobial and immunomodulatory functions. Notably, linezolid, an important second-line anti-tuberculosis drug, was efficiently incorporated in pβCD, regardless of its molecular weight. These findings emphasize the critical importance of molecular weight in dictating pβCD bioactivity. Only LMW-pβCD combines antimicrobial and immunomodulatory activities with effective drug delivery, making it a promising candidate for the development of novel anti-tuberculosis therapies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".