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Record W4413286269 · doi:10.1128/mbio.01963-25

Rapid, low-cost colorimetric detection of <i>Salmonella</i> Typhi bacteriophages for environmental surveillance

2025· article· en· W4413286269 on OpenAlexaff
Késia Esther da Silva, Shiva Ram Naga, Paulo César Pereira dos Santos, Karla Fisher, Jean T. Coulibaly, Mi Yang, Eric J. Nelson, Richelle C. Charles, Katherine Shafer, Brad-Lot Igiraneza, Mohammed S. Yusuf, Kathleen M. Neuzil, Isaac I. Bogoch, Rajeev Shrestha, Dipesh Tamrakar, Jason R. Andrews

Bibliographic record

VenuemBio · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of TorontoToronto General HospitalUniversity Health Network
FundersStanford University Center for Innovation in Global HealthNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates Foundation
KeywordsSalmonella typhiTyphoid feverSanitationMicrobiologyAgarSalmonellaEnvironmental healthVeterinary medicineBiologyBacteriaMedicineEnvironmental scienceEnvironmental engineeringEscherichia coli

Abstract

fetched live from OpenAlex

ABSTRACT Typhoid fever remains a global public health challenge, especially in low- and middle-income countries where poor sanitation and limited access to clean water facilitate transmission. The lack of data on disease burden poses a significant barrier to adopting effective interventions such as vaccination programs. We developed a novel colorimetric assay for the detection of S . Typhi-specific bacteriophages (phages) in environmental water samples, providing an indirect indicator of S . Typhi contamination and insights into typhoid burden. We collected surface water samples from Brazil, Côte d’Ivoire, Nepal, and Niger, covering urban, peri-urban, and rural areas. We evaluated the colorimetric assay efficiency against the agar overlay plaque assay. Isolated phages were tested against various bacteria to assess their host range. The colorimetric assay demonstrated high sensitivity (100% concordance with double agar overlay) with a detection limit of 28 plaque-forming units per milliliter (PFU/mL), and results were obtained in 5.5 hours. Phage detection rates were highest in densely populated areas with poor sanitation, particularly in Kathmandu, Nepal (98% positivity in river samples) and Abidjan, Côte d’Ivoire (35.1% positivity in drainage samples). The detection of S . Typhi phages in Côte d’Ivoire is particularly important, as the burden of typhoid in the region was previously undocumented. Phages were not detected in rural and drinking water sources. Host range analyses demonstrated that all isolated phages were specific to S . Typhi. The novel colorimetric assay offers a rapid and sensitive method for detecting S . Typhi bacteriophages in environmental water. The scalability, low cost (~$2.40 per sample), and minimal equipment requirements suggest that this could be an effective tool for typhoid surveillance in resource-limited settings. IMPORTANCE Typhoid fever, caused by Salmonella Typhi, remains a significant global health threat, particularly in low-resource settings with inadequate sanitation. Effective control measures, such as vaccines, require precise data on where typhoid is most prevalent, yet current surveillance methods are expensive and limited in scope. This study introduces a rapid, low-cost, and scalable colorimetric assay for detecting S. Typhi bacteriophages (viruses that infect bacteria) in environmental water samples. Unlike traditional methods, this test detects the presence of S. Typhi indirectly by identifying associated phages, offering a specific and sensitive approach for monitoring typhoid fever circulation. The assay was validated in diverse settings across four countries and demonstrated high accuracy and cost efficiency. By reducing reliance on expensive laboratory equipment and complex procedures, this tool makes typhoid surveillance more accessible, especially in low-income regions, helping prioritize vaccination campaigns and improve public health interventions.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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 designBench or experimental
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

Citations3
Published2025
Admission routes1
Has abstractyes

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