<i>Aryl hydrocarbon receptor interacting protein</i> (<i>AIP</i>) gene mutation analysis in children and adolescents with sporadic pituitary adenomas
Bibliographic record
Abstract
OBJECTIVE: Pituitary adenomas occur rarely in childhood and adolescence. Pituitary adenoma predisposition (PAP) has been recently associated with germline mutations in the aryl hydrocarbon receptor interacting protein (AIP) gene. The aim of the study was to examine the proportion of germline AIP mutations in apparently sporadic paediatric pituitary adenomas. DESIGN: Genomic DNA was analysed for mutations in the AIP gene, by PCR amplification and direct sequencing. PATIENTS: A population-based cohort consisting of 36 apparently sporadic paediatric pituitary adenoma patients, referred to two medical centres in Italy, was included in the study. Patients were either less than 18 years at diagnosis, or showed clinical evidence of adenoma development before the age of 18 years. RESULTS: A heterozygous in-frame deletion Y248del (c.742_744delTAC) was identified in one GH-secreting adenoma patient. Loss of heterozygosity (LOH) analysis of tumour DNA revealed the loss of the wild-type allele. First degree relatives carrying the mutation were clinically unaffected. CONCLUSIONS: While mutations were absent in non-GH-secreting adenoma patients, germline AIP mutations can be found in children and adolescents with GH-secreting tumours, even in the absence of family history. The present study reports the AIP mutation analysis results on patients of a single ethnic origin. Clearly, further studies are needed to improve our knowledge on the role of AIP in paediatric pituitary adenomas.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".