The correlation between the expression of ATF4 and procalcitonin combined with the detection of <i>RET</i> mutation and the pathological stage and clinical prognosis of medullary thyroid carcinoma
Bibliographic record
Abstract
To explore the correlation between the activating transcription factor 4 (ATF4) and procalcitonin (PCT) expressions combined with RET mutation and the pathological staging and clinical prognosis of sporadic medullary thyroid carcinoma (SMTC). Fifty cases (tumor tissue) of SMTC diagnosed by clinicopathology were collected and the patients with nodular goiter were selected as normal control. The RET mutation site was analyzed by detection kit and expressions of PCT and ATF4 in SMTC were analyzed by Western blot and immunohistochemistry. Multiple linear regression was used to analyze the correlation of risk factors (PCT or ATF4 expression, RET mutation, tumor differentiation, SMTC stage, lymphatic metastasis) for 5-year recurrence and survival of SMTC. The ATF4 and PCT expressions were significantly decreased and increased, respectively, with the increase of the SMTC stage. The most frequent mutation of RET gene in cancer tissue was M 22458A in exon 16. The ATF4 and PCT expressions, as well as RET mutation, were significantly associated with a 5-year recurrence, while the ATF4 expression was significantly related to better 5-year survival. ATF4 and PCT expressions combined with RET mutation are related to the clinical prognosis of SMTC and can predict SMTC staging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".