Selective Killing of Leukemia and Lymphoma Cells Ectopically Expressing hCGβ by a Conjugate of Curcumin with an Antibody against hCGβ Subunit
Bibliographic record
Abstract
OBJECTIVE: A variety of cancers ectopically express human chorionic gonadotropin beta (hCGbeta). Patients harboring such cancers have poor prognosis and adverse survival. A recombinant chimeric antibody, cPiPP, exhibiting high affinity and specificity for hCGbeta/hCG was engineered. This study was designed to determine whether this antibody alone or conjugated to curcumin can selectively kill tumor cells expressing hCGbeta. EXPERIMENTAL DESIGN: The study was carried out on MOLT-4 and U-937 cells expressing hCGbeta and on peripheral blood leukocytes of acute myeloid leukemia (AML) patients. The anticancerous compound curcumin was conjugated to cPiPP. The binding of cPiPP and cPiPP-curcumin conjugate to cells was studied by flow cytometry and cytotoxicity by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), FACS with propidium iodide staining, trypan blue exclusion assay and microscopy. RESULTS: The antibody did not impair the growth of MOLT-4 and U-937 cells in culture. Its conjugate with curcumin, however, was lethal to both cell lines. The immunoconjugate killed tumor cells bearing the CD33 marker of an AML patient expressing hCGbeta but did not have a similar action on cells of another AML patient with the CD13 marker but who was negative for hCGbeta. CONCLUSION: A humanized antibody against hCGbeta linked to curcumin has potential for therapy of hCGbeta-expressing tumors.
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 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) | 0.001 | 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".