USE OF MOLECULAR GENETIC METHODS IN THE STUDY OF HEREDITARY PREDISPOSITION TO ATOPIC DISEASES INCHILDREN
Bibliographic record
Abstract
Background. Study of the аssociations of susceptibility genes to the development of atopic diseases in children. Materials and methods. All 325 examined children reside on the territory of the European part of Russia who by according to surveys, Russian by nationality. Analysis of polymorphism in genes of receptors ADRB2, GRL, ALOX5, genes of biotransformation - CYP1A1, CYP2C9, CYP2C19, GSTT1, GSTM1, NAT2 as well as the variants of the genes MTHFR and TNFA was performed in patients suffering from atopic disease and in healthy individuals. Using Multifactor Dimentionality Reduction method (MDR) it was defined the most significant model of genegene interaction for the development of atopic disease Results. Association of the development of atopic diseases with polymorphic variants of the genes: ALOX5 (VNTR) GRL (1220A > G) TNFA (-308G > A) CYP1A1 (6235T > C) and GSTM1 was identified in surveyed children. The highrisk alleles and genotypes of developing atopic diseases in pediatric patients were determined. Using Multifactor Dimentionality Reduction method (MDR) it was defined the most significant model of gene-gene interaction for the development of atopic disease, including ADRB2 (79 C >G), (46A > G), CYP2C19 (G681A) was defined. Conclusion. There were identified polymorphic variants of genes and important gene-gene interactions associated with development of atopic diseases in children.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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 | 0.000 |
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".