Optimizing and Evaluating Nanopore-Based Targeted and Metagenomic Sequencing Workflows for Rapid Diagnosis of Acute Invasive Infections from Normally Sterile Body Fluids
Bibliographic record
Abstract
Abstract Rapid and accurate pathogen identification is critical for managing acute invasive infections. Conventional culture methods are time-consuming, delaying effective treatment. Nanopore sequencing offers real-time, long-read capabilities suitable for clinical diagnostics, yet standardized workflows remain lacking. This study developed and evaluated two optimized nanopore sequencing workflows: Nanopore Targeted Sequencing (NTS) and Nanopore Metagenomic Sequencing (NMgS), for pathogen and antimicrobial resistance (AMR) detection in 177 normally sterile body fluid samples. NTS used multiplex PCR to amplify 16S rRNA, ITS, and 21 AMR genes, while NMgS applied host DNA depletion followed by unbiased sequencing. Both workflows were benchmarked against culture-based diagnostics. Among the 304 species cultured from 177 body fluid samples, NTS identified 78.95%, with 77.30% meeting the threshold of relative abundance (T RA ) of 0.058 and 71.38% having at least 10 classified reads. In comparison, NMgS identified 39.47% of cultured species at the species level and 9.54% at the genus level. Of the 28 samples containing AMR ESKAPE pathogens, NTS detected associated AMR genes in 24 samples (85.71%), while NMgS identified AMR genes linked to 9 of the 32 ESKAPE pathogens (28.13%). The turnaround times for NTS and NMgS workflows were 10.75 and 12.82 hours, respectively. In conclusion, this study demonstrated the clinical utility of Nanopore sequencing for rapid diagnosis in clinical microbiology. The heightened sensitivity of Nanopore targeted sequencing renders it ideal for routine clinical microbiology diagnoses, whereas unbiased Nanopore metagenomic sequencing is advantageous in identifying infections of unknown etiology.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".