Screening ancient tuberculosis with qPCR: challenges and opportunities
Bibliographic record
Abstract
The field of ancient DNA (aDNA) has rapidly accelerated in recent years as a result of new methods in next-generation sequencing, library preparation and targeted enrichment. Such research is restricted, however, by the highly variable DNA preservation within different tissues, especially when isolating ancient pathogens from human remains. Identifying positive candidate samples via quantitative PCR (qPCR) for downstream procedures can reduce reagent costs, increase capture efficiency and maximize the number of sequencing reads of the target. This study uses four qPCR assays designed to target regions within the Mycobacterium tuberculosis complex (MTBC) to examine 133 human skeletal samples from a wide geographical and temporal range, identified by the presence of skeletal lesions typical of chronic disseminated tuberculosis. Given the inherent challenges working with ancient mycobacteria, strict criteria must be used and primer/probe design continually re-evaluated as new data from bacteria become available. Seven samples tested positive for multiple MTBC loci, supporting them as strong candidates for downstream analyses. Using strict and conservative criteria, qPCR remains a fast and effective screening tool when compared with screening by more expensive sequencing and enrichment technologies.
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 | 0.040 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".