Ion Beam Imaging Shows Selective Thallium-201 Uptake in Cell Nuclei: Impact on Cellular Dosimetry and Radiotoxicity of Auger Electron Emitters
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Thallium-201, a promising candidate for precise targeted radionuclide therapy, emits abundant, radiotoxic, short-range Auger and other secondary electrons, but its subcellular distribution, on which the delivered absorbed radiation dose depends, remains unknown. This study investigates the subcellular localization of unbound 201 Tl +, for input into microdosimetry models. Methods: Prostate (DU145), ovarian (SKOV3), and lung (A549) cancer cells were exposed to nonradioactive TlCl and imaged using laser ablation-inductively coupled plasma-mass spectrometry, energy-dispersive X-ray spectroscopy combined with transmission electron microscopy, and ion beam analysis (IBA). Absorbed radiation doses to cell nuclei from intracellular 201 Tl were calculated for geometries based on DU145 cells, applying the standard Medical Internal Radiation Dose formalism, and compared to those from 201 Tl-labeled Prussian blue nanoparticles (PBNPs). Results: Only IBA successfully quantified thallium in both the nucleus and cytoplasm, showing selective uptake in the nucleus with nuclear:cytoplasmic concentration ratios of 1.8 ± 1.5 in DU145 cells and 1.8 ± 1.0 in SKOV3 cells. New dose calculations for 201 Tl revealed that exclusively cytoplasmic localization of 201 Tl activity, exemplified by 201 Tl bound to chitosan-coated PBNPs in A549 cells, reduces the absorbed dose to the nucleus by 82%, compared to the observed distribution of unbound 201 Tl +, providing a rationale for reduced cytotoxicity per decay for PBNPs compared to 201 Tl + observed previously. Conclusion: Thallium(I) ions show preferential accumulation in cell nuclei and this could account for the higher toxicity of [ 201 Tl]TlCl than [ 201 Tl]PBNPs per intracellular decay event.
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".