Antiestrogens sensitize human ovarian and lung carcinomas for lysis by autologous killer cells.
Bibliographic record
Abstract
BACKGROUND: The antiestrogens tamoxifen (TX) and toremifene (TO) were shown previously to enhance the lysis of target cells by natural killer cells (NK), lymphokine activated killer (LAK) cells, and by cytotoxic T lymphocytes (CTL). MATERIALS AND METHODS: CTL were cultured from lung cancer tissue and from ascites fluid of ovarian carcinoma patients with the aid of human recombinant interleukin-2 (hrIL-2). The target, effector or both cell populations were pretreated by TX, TO and/or with human recombinant interferon-alpha (IFN-alpha). RESULTS: Significant enhancement of cytotoxicity occurred when the tumor targets or both the target and effector cells were treated with TX, TO or when these drugs were used in combination with IFN-alpha. The lytic activity of CTL cultured from draining lymph nodes of lung cancer patients, was also observed after similar treatment. The lytic effect of autologous LAK cells derived from peripheral blood was increased to a lesser extent, which could be amplified by additional treatment with IFN-alpha. CONCLUSIONS: The antiestrogens TX and TO and IFN-alpha enhance the lysis of autologous tumor cells by CTL and LAK effectors.
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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".