No Association of Polymorphisms in Nav1.7 or Nerve Growth Factor Receptor Genes with Trigeminal Neuralgia
Bibliographic record
Abstract
OBJECTIVE: Trigeminal neuralgia is defined as a sudden severe shock-like pain within the distribution of the trigeminal nerve. Pain is a subjective experience that is influenced by gender, culture, environment, psychological traits, and genes. Sodium channels and nerve growth factor play important roles in the transmission of nociceptive signals and pain. The aim of this study was to investigate the occurrence of Nav1.7 sodium channel and nerve growth factor receptor TrkA gene polymorphisms (SCN9A/rs6746030 and NTRK1/rs633, respectively) in trigeminal neuralgia patients. METHODS: Ninety-six subjects from pain specialty centers in the southeastern region of Brazil were divided into 2 groups: 48 with classical trigeminal neuralgia diagnosis and 48 controls. Pain was evaluated using the visual analog scale and multidimensional McGill Pain Questionnaire. Genomic DNA was obtained from oral swabs in all individuals and was analyzed by real-time polymerase chain reaction. RESULTS: No association was observed between evaluated polymorphisms and trigeminal neuralgia. For allele analyses, patients and controls had similar frequencies for both genes. Genotype distribution or allele frequencies of polymorphisms analyzed here did not correlate to pain scores. CONCLUSIONS: Although no association of evaluated polymorphisms and trigeminal neuralgia diagnosis or pain severity was observed, our data do not exclude the possibility that other genotypes affecting the expression of Nav1.7 or TrkA are associated with the disease. Further studies should investigate distinct genetic polymorphisms and epigenetic factors that may be important in expression of these molecules.
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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 | 0.000 | 0.002 |
| 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) | 0.004 | 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".