Bibliographic record
Abstract
Objective In order to find the mechanism of tumor immunotherapy, Through observing the correlation between Th1/Th2 shift and tumors, and the influence of cytokines on Th1/Th2 paradigm. Methods Enzyme-likded Idmmunospot Assay Was used to measure Th1/Th2 paradigm, and ELISAs were used to measure cytokines. Results The ratio of Th1/Th2 positive cells of patients with gastric cancer were in the condition of Th2 predominance. Th1 type cytokines, in the patients with gastric cancer,colorectal acncer and breast cancer were lower than that of normal persons significantly (P0.05),Th2 type cytokines of patients,IL-4,IL-6 and IL-10, were significantly higher than that of normal persons (P0.05),TNF-α was higher(P0.05) and IL-12 was lower in patients of colorectal cancer than normal persons (P0.05) At the same time, cytokines of patients were observed in differentiation, the lower differentiation of the tumor had the lower Th1 type cytokines and the higher Th2 type cytokines, but there were no significance except IL-6. Conclusion All patients were in the condition of Th2 predominance,which indicated that the cellular immune response of patients was inhibited,so tumors could escape from the attack of immune response.Using IL-12 and anti-IL-4 monoclonal antibody, we could switch from Th2 response to Th1 response and enhance cellular immunity.
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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 | 0.000 |
| 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) | 0.003 | 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".