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Record W2089566239 · doi:10.1098/rspb.2004.2966

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

2005· article· en· W2089566239 on OpenAlexaff
Helen D. Donoghue, Antónia Marcsik, Carney Matheson, Kim Vernon, Emilia Nuorala, José Moltó, Charles L. Greenblatt, Mark Spigelman

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2005
Typearticle
Languageen
FieldMedicine
TopicLeprosy Research and Treatment
Canadian institutionsLakehead University
Fundersnot available
KeywordsLeprosyMycobacterium lepraeTuberculosisMycobacterium tuberculosisMycobacteriumMycobacterium tuberculosis complexDiseaseImmunologyBiologyVirologyMedicinePathology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.316
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations188
Published2005
Admission routes1
Has abstractyes

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