ArticleScientific reports2019
Proton transport modeling in a realistic biological environment by using TILDA-V.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Efficient computational modeling of electronic stopping power of organic polymers for proton therapy optimization.Scientific reports · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whether it is in radiobiology to identify DNA lesions or in medicine to adapt the radiotherapeutic protocols, a detailed understanding of the radiation-induced interactions in living matter is required. Monte Carlo track-structure codes have been successfully developed to describe these interactions and predict the radiation-induced energy deposits at the nanoscale level in the medium of interest. In this work, the quantum-mechanically based Monte Carlo track-structure code TILDA-V has been used to compute the slowing-down of protons in water and DNA. Stopping power and range are then reported and compared with existing data. Then, a first application of TILDA-V to cellular irradiations is also reported in order to highlight the absolute necessity of taking into account a realistic description of the cellular environment in microdosimetry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.