Genomic Epidemiology of Methicillin-Resistant <i>Staphylococcus aureus</i> in Two Cohorts of High-Risk Military Trainees
Bibliographic record
Abstract
Abstract Background MRSA skin and soft tissue infection (SSTI) is a significant cause of morbidity in military trainees. To guide interventions, it is critical we understand the epidemiology of MRSA in this population. Methods Two cohorts (‘companies’) of US Army Infantry trainees (N=343) at Fort Benning, GA, USA, were followed during their training cycles (Jun.-Dec. 2015). Trainees had nares, oropharynx, perianal and inguinal areas swabbed for MRSA colonization at five ∼2-4 week intervals, and monitored for SSTI throughout training. Epidemiological data were collected. Isolates were sequenced using Illumina HiSeq and NovaSeq. Single-nucleotide polymorphisms and clusters were identified. Multi-locus sequence type (MLST) and antimicrobial resistance genes were predicted from de novo assemblies. Results 87 trainees were positive at least once for MRSA (12 had SSTI, 2 without any colonization). Excluding those positive at baseline, 43.7% were colonized within the first month of training. 244/254 samples were successfully sequenced (including all SSTI). ST8 (n=135, 100% of SSTI), ST5 (n=81) and ST87 (n=21) were the most represented. Three main Clusters were identified, largely corresponding to these STs. Sub-analyses within Clusters showed multiple importations of MRSA, with transmission subsequently predominantly within, rather than between, platoons in each company. Over 50% of trainees were colonized only at other anatomical sites; restricting analyses to nares missed substantial transmission. Conclusions Serial importations of MRSA into this high-risk setting likely contribute to the ongoing burden of MRSA colonization and infection among military trainees. Sampling multiple anatomical sites is critical for comprehensive characterization of MRSA transmission Summary US Infantry trainees were followed through training for MRSA skin and soft tissue infection, swabbing for colonization at 2-4 week intervals. Sequencing suggests serial importations of diverse strains on base, followed by transmission mostly within platoons, involving multiple anatomical sites.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".