Sequence-based diagnostics and precision medicine in bacterial and viral infections: from bench to bedside
Bibliographic record
Abstract
PURPOSE OF REVIEW: Nucleic acid sequence-based organism identification plays an important role in the diagnosis and management of transplant and cancer-associated infectious diseases. Here, we provide a high-level overview of advanced sequencing technologies, discuss test performance, and highlight unmet research needs with a focus on immunocompromised hosts. RECENT FINDINGS: Next-generation sequencing (NGS) technologies are powerful tools with a growing role in managing immunocompromised patients with suspected infection. Targeted NGS (tNGS) can identify pathogens directly from patient specimens, especially for mixed samples, and has been used to detect resistance mutations in transplant-related viruses (e.g. CMV). Whole-genome sequencing (WGS) is increasingly used for outbreak investigations and infection control. Metagenomic NGS (mNGS) is useful for hypothesis-free testing and can simultaneously assess pathogens and host response to infection. SUMMARY: NGS testing increases diagnostic yield relative to standard culture and Sanger sequencing but may be limited by high cost, turnaround times, and detection of unexpected organisms or commensals of uncertain significance. Close collaboration with the clinical microbiology laboratory and infectious diseases is recommended when NGS testing is considered. Additional research is required to understand which immunocompromised patients are most likely to benefit from NGS testing, and when testing should ideally be performed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".