Differentiation of hair growth cycle from scalp hair roots for the diagnosis of glucose‐6‐phosphate dehydrogenase deficiency in neonates
Bibliographic record
Abstract
Hair analysis can be used as a screening tool in the diagnosis of genetic diseases. The scalp hair roots of 67 normal neonates and 39 neonates with glucose-6-phosphate dehydrogenase (G6PD) deficiency were analysed using Fourier transform infrared (FT-IR) microspectroscopy to differentiate the stages of the hair growth cycle and to diagnose the genetic disorder on the basis of spectral differences. We have demonstrated that FT-IR microspectroscopy is a rapid and effective noninvasive diagnostic method to differentiate scalp hair roots of normal neonates into the anagen, catagen or telogen phases of the hair growth cycle, using IR spectral differences within the 3000-2800 cm(-1) region and the IR peak area ratio of 2854 cm(-1)/2873 cm(-1) or 1084 cm(-1)/amide II band (p<0.001). Moreover, G6PD-deficient neonates could be accurately diagnosed from telogen phase hair roots owing to significant differences in IR peak area ratios of 2854 cm(-1)/2873(-1) or 1084 cm(-1)/amide II band compared to normal values in healthy neonates. The result suggests that the application of FT-IR microspectroscopy may be capable not only of differentiating the hair growth cycle into anagen, categen or telogen phases but also of detecting G6PD deficiency. Hair root analysis promises to be a useful complement to serum and urine analysis in the diagnosis of genetic diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".