ArticleCurrent microbiology2025
Accuracy of SAM-TB Platform as a Diagnostic Assay for Detection of Mycobacterium tuberculosis Drug Resistance Versus TB-Proflier, PhyResSE, and Mykrobe: A Head-to-Head Comparison.
Article in Current microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Open-access genomic drug resistance prediction tools forJournal of clinical microbiology · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Accurate detection of drug-resistant Mycobacterium tuberculosis (MTB) is essential for tuberculosis control, particularly with increasing multidrug-resistant strains. Whole-genome sequencing (WGS), as a supplement to phenotypic drug susceptibility testing (DST), holds great potential for resistance detection, but comparative analyses of multiple WGS tools remain limited. This study assessed four WGS-based tools-SAM-TB, TB-Profiler, Mykrobe predictor, and PhyResSE-for predicting resistance to four first-line drugs (isoniazid, rifampicin, ethambutol, and streptomycin) using 113 clinical MTB isolates from Ganzhou, China, with phenotypic DST as reference. All tools showed similar sensitivity (85.04-88.19%) and high specificity (95.69-96.31%). Several known resistance-associated mutations were identified, along with seven novel mutations absent from the WHO catalogue and thirteen of uncertain clinical significance. Discrepancies between WGS and DST occurred across all drugs, potentially due to disputed mutations, outdated databases, or limitations of phenotypic methods. These results highlight the complementary roles of WGS and phenotypic DST in drug resistance surveillance and emphasize the need for continuously updated mutation databases, including the WHO catalogue, and standardized interpretation criteria to enhance the clinical utility of WGS.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.