Impact of <i>NOTCH1</i> polymorphisms on liver cancer in a Chinese Han population
Bibliographic record
Abstract
NOTCH1, a member of the Notch family, is up-expression in advanced liver cancer (LC) patients and is associated with tumor sizes, tumor stage, metastasis, and invasion. A few studies have discovered the contribution of NOTCH1 variants to LC risk. Our purpose was to assess the relationship of NOTCH1 rs10521, rs2229971, and rs4489420 to LC risk. We enrolled 709 LC patients and 708 healthy controls. Genotyping was determined through the Agena MassARRAY system. Multiple genetic models by logistic regression were useful for odds ratios (ORs) with 95% confidence intervals (CIs). Rs10521-G (p = 0.009, OR = 0.75, 95% CI: 0.61–0.93), rs2229971-A (p = 0.023, OR = 0.81, 95% CI: 0.67–0.97), and rs4489420-A (p = 0.014, OR = 0.38, 95% CI: 0.16–0.85) might be protective factors for LC occurrence in the Chinese Han population, especially rs10521 and rs2229971 (false-positive report probability (FPRP) <0.2 and statistical power >90%). Interestingly, stratified analysis displayed that the contribution of NOTCH1 polymorphisms to LC risk might be associated with gender, age, smoking, and drinking. Our data first determined that NOTCH1 rs10521-G, rs2229971-A, and rs4489420-A might be protective factors for LC susceptibility.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.084 | 0.002 |
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; both teacher heads agree on what is shown here.
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".