The BCG in situ study – novel techniques applied to a 100-year-old vaccine
Bibliographic record
Abstract
The use of Bacillus Calmette-Guérin (BCG) vaccine against tuberculosis (TB) spans more than a century. Besides protection against severe paediatric TB, randomized trials and novel advances within innate immunology documented that BCG has beneficial non-specific effects, providing protection against non-TB infections. Since paediatric intradermal BCG vaccination has proved unable to contain adult pulmonary tuberculosis, several novel TB vaccines are under development, most of which build upon BCG. BCG’s status as an essential remedy against TB will therefore be maintained, but despite many decades of near-universal intradermal use, the local responses to BCG in the skin have not been thoroughly elucidated. We therefore developed appropriate methods to capture the localised skin events at the cellular and molecular level after intradermal BCG vaccination. This work informs future studies to identify the immunological events induced following administration of BCG to accelerate development of improved or new vaccines against Mycobacterium tuberculosis (Mtb) . We employed advanced technologies such as spatial transcriptomics on skin tissue punch biopsies and cell-free plasma transcriptomics (liquid biopsies) to characterize both the local ( in situ ) skin and systemic (peripheral blood) response to BCG via: Whole blood transcriptomics and epigenetic analysis Blood immune cell characterization Plasma proteomic and metabolomic analysis
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".