CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity
Bibliographic record
Abstract
Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA-miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.
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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 | 0.001 |
| 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) | 0.000 | 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".