MétaCan
Menu
Back to cohort
Record W2600010289 · doi:10.4236/aim.2017.73017

Mycobacteria Interspersed Repetitive Units-Variable Number of Tandem Repeat, Spoligotyping and Drug Resistance of Isolates from Pulmonary Tuberculosois Patients in Kenya

2017· article· en· W2600010289 on OpenAlex
Perpetual Ndung’u, Samuel Kariuki, Gunturu Revathi, Zipporah Ng’ang’a, Stefan Niemann

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvances in Microbiology · 2017
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsnot available
FundersAfrican Population and Health Research CenterInternational Development Research Centre
KeywordsTypingGenotypeBiologyDrug resistanceMycobacterium tuberculosisVariable number tandem repeatPyrazinamideVirologyTandem repeatTuberculosisVeterinary medicineGeneticsMedicineRifampicinAntibioticsGene

Abstract

fetched live from OpenAlex

Background: Molecular typing allows a rapid and precise species differentiation and is essential in investigating the spread of specific genotypes and any relationship with drug resistance. Methodology: To compare the discrimination power of 24-loci Mycobacteria interspersed repetitive units-variable number of tandem repeat (MIRU-VNTR) to spoligotyping in determining the circulating genotypes of Mycobacterium tuberculosis in isolates from pulmonary tuberculosis patients in Kenya, a total of 204 isolates were typed. Results: Spoligotyping identified 22 spoligo lineages; while 36(17.6%) isolates were not determined. MIRU-VNTR typing identified 12 genotypes; Kenya H37_Rv_ like, S-like that had never been reported before and which were not identified by spoligotyping were identified. Others were Uganda I and II, LAM, Beijing, TUR, EAI, Delhi/C, S and Haarlem. Only 8 (3.9%) were not defined by MIRU-VNTR. Delhi/CAS, EAI, S, S-like, LAM and Beijing had strains that showed resistance to all the five drugs tested. Two strains of EAI and 2 of S genotypes were resistant to all the five drugs tested. Beijing genotype commonly associated with drug resistance was found to be third in drug resistance (14.7%) after Delhi/CAS (28.9%) and LAM (17.6%) with the highest resistance towards isoniazid and pyrazinamide (3.9% each). MIRU-VNTR typing was more discriminative than spoligotyping; identifying 10 unique H37_Rv-like isolates designated KeniaH37_Rv_like genotype and 14 S-like genotype. Conclusion: MIRU-VNTR typing has not been reported in any other study in Kenya and its higher discrimination can help identify genotypes that cannot be determined by spoligotyping. Association of Beijing genotype drug resistance particularly isoniazid should be of concern since it may result in multidrug resistance in the patients.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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