Whole genome sequencing confirms reactivation of tuberculosis five years after initial infection despite adequate preventative therapy in a patient with profound immunosuppression
Bibliographic record
Abstract
Whole-genome sequencing (WGS) can distinguish Mycobacterium tuberculosis (Mtb) infection (TBI) reactivation from reinfection, offering definitive evidence of long-term bacterial persistence. We describe TBI reactivation 5 years after isoniazid prophylaxis in a patient receiving multi-agent immunosuppression post allogeneic haematopoietic stem cell transplant (allo-HSCT). WGS confirmed near-identical isolates between the patient and their household infectious tuberculosis (TB) contact, proving true reactivation. This case demonstrates the capacity for mycobacterial persistence despite prophylaxis and highlights the ongoing risk of reactivation in heavily immunosuppressed patients. The patient was exposed to a household contact with pan-sensitive pulmonary TB. Contact screening confirmed TBI via a positive Mantoux test, and the patient completed nine months of isoniazid prophylaxis. Five years later, the patient underwent matched unrelated donor allo-HSCT for treatment- refractory paroxysmal nocturnal hemoglobinuria. Post-transplant immunosuppression included tacrolimus. Over the following months, the patient developed severe, refractory graft-versus-host disease (GVHD) of the gastrointestinal tract, requiring high-dose methylprednisolone, ruxolitinib, vedolizumab, and extracorporeal photopheresis.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".