Operational nuclear research reactors in the Asia-Pacific with potential for medical radionuclide production
Bibliographic record
Abstract
Personalised cancer treatment is of growing importance and can be achieved via targeted radionuclide therapy. Radionuclides with theranostic properties are proving to be clinically effective and are widely used because diagnostic imaging and therapy can be accomplished using a single formulation that avoids additional procedures and unnecessary radiation burden to the patient. For diagnostic imaging, single photon emission computed tomography (SPECT) or positron emission tomography (PET) is used to obtain functional information noninvasively by detecting the gamma (γ) rays emitted from the radionuclide. For therapeutics, high linear energy transfer (LET) radiations such as alpha (α), beta (β - ) or Auger electrons are used to kill cancerous cells in close proximity, whereas sparing the normal tissues surrounding the malignant tumour cells. One of the most important factors that lead to the sustainable development of nuclear medicine is the availability of functional radiopharmaceuticals. Nuclear research reactors play a vital role in the production of medical radionuclides for incorporation into clinical radiopharmaceuticals. The disruption of medical radionuclide supplies in recent years has highlighted the importance of ongoing research reactor operation. This article reviews the current status of operational nuclear research reactors in the Asia-Pacific region that have the potential for medical radionuclide production. It also discusses the different types of nuclear research reactors, their operating power, and the effects of thermal neutron flux in producing desirable radionuclides with high specific activity for clinical applications.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".