Aerosolization Studies of Zn-DTPA Using Nebulizers for Decorporation of Internal Radioactive Contamination
Bibliographic record
Abstract
After individuals inhale radioactive particulate matter in nuclear disasters, pharmaceuticals will be essential to treat the contamination.Zn-DTPA can chelate to radionuclides, helping to accelerate the elimination of the radioactive components from the body.Although Zn-DTPA can be administered intravenously, medication in aerosol form could also be delivered directly to the lungs using commercial inhalers.This thesis will study the feasibility of aerosolizing Zn-DTPA solution by two inhalers (jet nebulizer and ultrasonic nebulizer), normally used in the treatment of asthma or bronchitis.Two evaluation parameters, the mass median aerodynamic diameter (MMAD) and geometric standard deviation (GSD), were used to assess whether aerosols can reach the lower respiratory tract (particle size should be within 1 to 5 microns for ideal delivery).Inhalable Zn-DTPA aerosols were successfully generated by both of the tested nebulizers.The particle size distribution was measured by a commercial particle size analyzer using time-offlight methods as well as a cascade impactor.Additionally, it was found that increasing the filling volume of Zn-DTPA solution in the jet nebulizer would decrease the MMAD and GSD, but this volume would not affect the MMAD of aerosols from the ultrasonic nebulizer.Zn-DTPA concentration also affected the MMAD in both nebulizers.MMAD significantly increased in the jet nebulizer along with the rising concentration.Small MMAD fluctuations were found in the ultrasonic nebulizer when the Zn-DTPA concentration was varied.Though more comprehensive work is necessary, this work could be used by health agencies around the world regarding nuclear disaster preparedness.
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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".