Co–infection of<i>Mycobacterium tuberculosis</i>and<i>Mycobacterium leprae</i>in human archaeological samples: a possible explanation for the historical decline of leprosy
Bibliographic record
Abstract
Both leprosy and tuberculosis were prevalent in Europe during the first millennium but thereafter leprosy declined. It is not known why this occurred, but one suggestion is that cross-immunity protected tuberculosis patients from leprosy. To investigate any relationship between the two diseases, selected archaeological samples, dating from the Roman period to the thirteenth century, were examined for both Mycobacterium leprae and Mycobacterium tuberculosis DNA, using PCR. The work was carried out and verified in geographically separate and independent laboratories. Several specimens with palaeopathological signs of leprosy were found to contain DNA from both pathogens, indicating that these diseases coexisted in the past. We suggest that the immunological changes found in multi-bacillary leprosy, in association with the socio-economic impact on those suffering from the disease, led to increased mortality from tuberculosis and therefore to the historical decline in leprosy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".