Using Next-Generation Sequencing Transcriptomics To Determine Markers of Post-traumatic Symptoms: Preliminary Findings from a Post-deployment Cohort of Soldiers
Bibliographic record
Abstract
Abstract Post-traumatic stress disorder is a concerning psychobehavioral disorder thought to emerge from the complex interaction between genetic and environmental factors. For soldiers exposed to combat, the risk of developing this disorder is twofold and diagnosis is often late, when much sequela has set in. To be able to identify and diagnose in advance those at “risk” of developing post-traumatic stress disorder, would greatly taper the gap between late sequelae and treatment. Therefore, this study sought to determine whether the transcriptome can be used to track the development of post-traumatic stress disorder in this unique and susceptible cohort of individuals. Gene expression levels in peripheral blood samples from 85 Canadian infantry soldiers (n = 58 participants negative for symptoms of post-traumatic stress disorder and n = 27 participants with symptoms of post-traumatic stress disorder) following return from deployment to Afghanistan were determined using RNA sequencing technology. Count-based gene expression quantification, normalization and differential analysis (with thorough correction for confounders) revealed genes associated to PTSD; LRP8 and GOLM1. These preliminary results provide a proof-of-principle for the diagnostic utility of blood-based gene expression profiles for tracking symptoms of post-traumatic stress disorder in soldiers returning from tour. It is also the first to report transcriptome-wide expression profiles alongside a post-traumatic symptom checklist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".