Genetic Engineering of a Recombinant Fusion Protein Possessing an Antitumor Antibody Fragment and a TNF-α Moiety
Bibliographic record
Abstract
Tumor necrosis factor-α (TNF-α) is a cytokine (CK) that possesses a wide variety of biological activities, including potent antitumor activities () and immunomodulatory properties mediated through its binding to two TNF receptors (p55 and p75) (). Signaling through the p55 receptor is primarily associated with responses such as cytotoxicity (,) and cytokine secretion () whereas the p75 receptor is responsible for lymphoproliferative signals and the activation of T-cells (). Recently, it has been found that TNF-α has profound effects on dendritic cell (DC) maturation () and activation (). In addition, it has also been reported to stimulate T-cell proliferation () and to activate cytotoxic T-cells (). Because its systemic administration was shown to mediate the regression of some mouse tumors (), TNF-α has attracted much attention as a potential antitumor reagent (). However, the problem of its dose-dependent toxicity has been particularly apparent in human trials, wherein its maximal tolerated dose was 40-fold less than that used in mice (,) Systemic administration of TNF-α in treatments of cancer patients has usually resulted in severe and limiting side effects (), whereas more local delivery (e.g., via isolated perfusion to limbs) has been more effective in mediating tumor regression, indicating that antitumor effects are possible if high local concentrations of TNF-α can be obtained (). Therefore, an important issue to be addressed is how to achieve a continuously high local concentration of TNF-α within tumors without inducing severe side effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".