Prevalence of Shigella boydii in Bangladesh: Isolation and Characterization of a Rare Phage MK-13 That Can Robustly Identify Shigellosis Caused by Shigella boydii Type 1
Bibliographic record
Abstract
Shigellosis, caused by Shigella boydii type 1 is understudied and under-reported. For three years, GEMS study identified 5.4% of all Shigella as S. boydii. We showed the prevalent serotypes of S. boydii in Bangladesh and phage based diagnosis of S. boydii type1, a rapid and low-cost approach. Previously typed 793 clinical S. boydii strains were used for serotype distribution. Twenty eight environmental water samples were collected for isolation of Shigella phages. Forty eight serotypes of Shigella and other enteric bacteria were used for testing the susceptibility to phage MK-13. Electron microscopy, restriction enzyme analysis, whole genome sequencing (WGS) and annotation were performed for extensive characterization. S. boydii type1 is the second most prevalent serotype among 20 serotypes of S. boydii in Bangladesh. We isolated a novel phage, MK-13 which specifically lyses S. boydii type 1, but doesn’t lyse other 47 serotypes of Shigella or other enteric bacteria tested. The phage belongs to Myoviridae family and distinct from other phages indicated by electron microscopy and restriction enzyme analysis respectively. MK-13 genome consists of 158 kbp of circularly permuted double-stranded DNA with G+C content of 49.45%, and encodes 211 open reading frames including 4 tRNA-coding regions. The genome has 98% identity with previously reported phage, ΦSboM-AG3 which infects most of the S. boydii and other species of Shigella tested. To our knowledge, MK-13 is the first phage reported to be used as diagnostic marker to detect S. boydii type1, especially in remote settings with limited laboratory infrastructure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".