Mycolic acid nanoparticle vaccination leads to antigen persistence and unique differentiation of mycobacterial lipid antigen-specific T cells
Bibliographic record
Abstract
Abstract Tuberculosis remains a serious global epidemic and with the rise of multi-drug resistant strains, an efficacious vaccine solution is imperative. Vaccines currently being developed for Mtb utilize protein antigens, which target MHC-restricted conventional T cells, overlooking the potential of Mycobacterium tuberculosis (Mtb) lipid antigens such as mycolic acid (MA), a key lipid found in Mtb cell wall. Mycobacterial lipids are presented by group 1 CD1 molecules (CD1a, b, c) to cognate T cells. Group 1 CD1-restricted T cells can be identified in patients with TB and have been shown to provide protection in Mtb infection. Using biocontinuous nanospheres (BCNs), a type of self-assembled nanostructure able to load both hydrophobic and hydrophilic molecules, we have created a vaccine containing MA. We found that MA BCN is able to effectively activate CD1b-restricted MA-specific T cells in vitro and in vivo. Interestingly, we discovered that MA persists within lung alveolar macrophages for at least 6 weeks after intratracheal vaccination with MA BCN. Antigen archiving was in part due to the encapsulation of MA within BCN. Nanoparticle vaccinations carrying lipid antigens may thus lead to persistent depots of antigen that could offer long-lasting immune response and protection. Supported by grants from NIH (5R01AI145345-03, 1F30AI157314-01, 5T32GM008152-35)
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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) | 0.001 | 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".