Human hair follicle transcriptome profiling: a minimally invasive tool to assess molecular adaptations upon low‐volume, high‐intensity interval training
Bibliographic record
Abstract
High-intensity interval training (HIIT) has become a popular fitness training approach under both civilian and military settings. Consisting of brief and intense exercise intervals, HIIT requires less time commitment yet is able to produce the consistent targeted physical adaptations as conventional endurance training. To effectively characterize and monitor HIIT-induced cellular and molecular responses, a highly accessible yet comprehensive biomarker discovery source is desirable. Both gene differential expression (DE) and gene set (GS) analyses were conducted using hair follicle transcriptome established from pre and postexercise subjects upon a 10-day HIIT program by RNA-Seq, Comparing between pre and posttraining groups, differentially expressed protein coding genes were identified. To interpret the functional significance of the DE results, a comprehensive GS analysis approach featuring multiple algorithms was used to enrich gene ontology (GO) terms and KEGG pathways. The GS analysis revealed enriched themes such as energy metabolism, cell proliferation/growth/survival, muscle adaptations, and cytokine–cytokine interaction, all of which have been previously proposed as HIIT responses. Moreover, related cell signaling pathways were also measured. Specifically, G-protein-mediated signal transduction, phosphatidylinositide 3-kinases (PI3K) – protein kinase B (PKB) and Janus kinase (JAK) – Signal Transducer and Activator of Transcription (STAT) signaling cascades were over-represented. Additionally, the RNA-Seq analysis also identified several HIIT-responsive microRNAs (miRNAs) that were involved in regulating hair follicle-specific processes, such as miR-99a. For the first time, this study demonstrated that both existing and new biomarkers like miRNA can be explored for HIIT using the transcriptomic responses exhibited by the hair follicle.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 0.001 |
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".