Lactoferrin reduces mycobacterial M1-type inflammation induced with trehalose 6,6′-dimycolate and facilitates the entry of fluoroquinolone into granulomas
Bibliographic record
Abstract
Primary infection with Mycobacterium tuberculosis (Mtb) results in the formation of a densely packed granulomatous response that essentially limits the entry and efficacy of immune effector cells. Furthermore, the physical nature of the granuloma does not readily permit the entry of therapeutic agents to sites where organisms reside. The Mtb cell wall mycolic acid, trehalose 6,6′-dimycolate (TDM), is a physiologically relevant molecule for modelling macrophage-mediated events during the establishment of the tuberculosis-induced granuloma pathogenesis. At present, there are no treatments for tuberculosis that focus on modulating the host’s immune responses. Previous studies showed that lactoferrin (LF), a natural iron-binding protein proven to modulate inflammation, can ameliorate the cohesiveness of granuloma. This led to a series of studies that further examined the effects of recombinant human LF (rHLF) on the histological progression of TDM-induced pathology. Treatment with rHLF demonstrated significant reduction in size and number of inflammatory foci following injections of TDM, together with reduced levels pulmonary pro-inflammatory cytokines TNF-α and IL-1β. LF facilitated greater penetration of fluoroquinolone to the sites of pathology. Mice treated with TDM alone demonstrated exclusion of ofloxacin to regions of inflammatory response, whereas the animals treated with rHLF demonstrated increased penetration to inflammatory foci. Finally, recent findings support the hypothesis that this mycobacterial mycolic acid can specifically recruit M1-like polarized macrophages; rHLF treatment was shown to limit the level of this M1-like phenotypic recruitment, corresponding highly with decreased inflammatory response.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".