Author response: A molecular portrait of maternal sepsis from Byzantine Troy
Bibliographic record
Abstract
Why and how have some bacteria evolved to cause illness in humans? One way to study bacterial evolution is to search for ancient samples of bacteria and use DNA sequencing technology to investigate how modern bacteria have changed from their ancestors. Understanding the evolution process may help researchers to understand how some bacteria become resistant to the antibiotics designed to kill them. Complications that occur during pregnancy, including bacterial infections, have long been a major cause of death for women. Now, Devault, Mortimer et al. have been able to sequence the DNA of bacteria found in tissue collected from a woman buried 800 years ago in a cemetery in Troy. Some of the woman’s tissues had been well preserved because they had calcified (probably as the result of infection), which preserved their structure in a mineralized layer. Two mineralized “nodules” in the body appear to be the remains of abscesses. Some of the human DNA in the nodules came from a male, suggesting that the woman was pregnant with a boy and that the abscesses formed in placental tissue. Sequencing the DNA of the bacteria in the abscess allowed Devault, Mortimer et al. to diagnose the woman’s infection, which was caused by two types of bacteria. One species, called Gardnerella vaginalis, is found in modern pregnancy-related infections. The DNA of the ancient samples was similar to that of modern bacteria. The other bacteria species was an ancient form of Staphylococcus saprophyticus, a type of bacteria that causes urinary tract infections. However, the DNA of the ancient S. saprophyticus bacteria is quite different to that of the bacteria found in modern humans. Instead, their DNA sequence appears more similar to forms of the bacteria that infect currently livestock. As humans lived closely with their livestock at the time the woman lived, her infection may be due to a type of bacteria that passed easily between humans and animals. Overall, the results suggest that the disease-causing properties of bacteria can arise from a wide range of sources. In addition, Devault, Mortimer et al. have demonstrated that certain types of tissue found in archeological remains are a potential gold mine of information about the evolution of bacteria and other microbes found in the human body.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".