Personalized multi-marker panel in the risk assessment of atopic dermatitis phenotypes in children
Bibliographic record
Abstract
Introduction This paper reports the study of a combined genetic and biomarker panel for assessing the risk of development of different phenotypes of atopic dermatitis (AD) in children: alone and combined with other atopic disorders (AtD) – allergic rhinitis/rhino-conjunctivitis (AR/ARC) and bronchial asthma (BA). The aim was to establish a personalized diagnostic multi-marker panel for assessing the developmental risk of different AD phenotypes in children combining single nucleotide polymorphism (SNP) rs_7927894 filaggrin (FLG) genotype variants, serum levels of total immune globulin E (IgE), cutaneous T-cell attracting chemokine (CTACK/CCL27) and thymus and activation regulated chemokine (TARC/CCL17). Material and methods The study recruited patients aged 3–18 years old: 39 atopic patients to the main group and 47 non-atopic patients to the control group. All the patients were tested for SNP variants of rs_7927894 FLG and serum concentrations of total IgE, CTACK/CCL27 and TARC/CCL17. Results Within AD alone phenotype patients we detected the following significant risk ratios: cytosinethymine (C/T) rs_7927894 FLG [odds ratio (OR) = 4.14, p < 0.05], total IgE > 173 IU/ml (OR = 8.98, p < 0.001), CTACK/ CCL27 > 3658.5 pg/ml (OR = 5.64, p < 0.01). Atopic disorders combined with other AtD phenotype: C/T rs_7927894 FLG (OR = 2.88, p < 0.05), total IgE > 213 IU/ml (OR = 136.7, p < 0.001), CTACK/CCL27 > 4308.8 pg/ml (OR = 7.40, p < 0.001). With AD combined with other AtD collated to AD alone – total IgE > 1001 IU/ml (OR = 16.0, p < 0.001). TARC/CCL17 had no significant differences among main and control groups. Conclusions Cytosinethymine rs_7927894 FLG variant combined with cut-off serum IgE and CTACK/CCL27 levels is a novel significant personalized multi-marker panel for assessing the risk of development of the different AD phenotypes in children.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".