Comparison of radiobiological effects induced by radiolabeled antibodies in human cancer cells and fungal cells
Bibliographic record
Abstract
Purpose Acute myeloid leukemia (AML) is a deadly form of leukemia, and its treatment often leaves patients immunocompromised, making them vulnerable to fungal infections. Radioimmunotherapy (RIT) is explored for both AML and fungal infections. This study compares the radiobiological effects of alpha emitter Actinium-225 (225Ac) and beta emitter Lutetium-177 (177Lu)-labeled antibodies on AML and Cryptococcus neoformans cells.Materials and methods AML OCI-AML3 and C. neoformans Cap-67 cells were treated with anti-(1-3)-beta-glucan antibody 400-2 and anti-CD33 antibody HuM-195, conjugated to DOTA and radiolabeled with 225Ac or 177Lu. Clonogenic survival, γH2A/X staining, and micronuclei assays were conducted. Antibody internalization was assessed by flow cytometry.Results Both 225Ac- and 177Lu-enabled RIT resulted in decreased clonogenic survival in Cap-67 and OCI-AML3 cells, with Cap-67 recovering more rapidly. DNA double-strand breaks and micronuclei formation revealed DNA damage, with fewer micronuclei in OCI-AML3 cells due to radiation destruction. HuM-195 antibody internalized into OCI-AML3 cells, whereas 400-2 did not internalize into Cap-67 cells.Conclusions While both cell lines showed similar responses to 225Ac- and 177Lu-enabled RIT, variations were observed based on cellular structure, doubling times and DNA repair mechanisms. This study offers insights for future in vivo research on fungal infections in cancer setting.
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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) | 0.001 | 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".