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Record W4416589933 · doi:10.1093/bjro/tzaf030

Exploring the therapeutic potential of localized alpha irradiation for cancer: from DNA damage to immune activation

2024· review· en· W4416589933 on OpenAlexaff
Saskia Hazout, Daniel R. Zwahlen, Christoph Oehler, Ambroise Champion, David Benzaquen, Daniel Taussky

Bibliographic record

VenueBJR|Open · 2024
Typereview
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsDNA damageImmune systemLinear energy transferRadiation therapyAlpha (finance)Cancer treatmentCancerDNAAlpha particle

Abstract

fetched live from OpenAlex

Alpha radiation has emerged as a promising modality in cancer treatment due to its unique physical and biological properties. Among these, diffusing alpha-emitters radiation therapy (DaRT) delivers alpha radiation directly into solid tumours using inserted seeds. This review synthesizes both the biological mechanisms and therapeutic implications of alpha irradiation, with a focus on DaRT. We explore how alpha particles induce complex DNA damage, modulate the tumour microenvironment, and interact with immune therapies. Emphasis is placed on preclinical and early clinical findings that suggest DaRT's potential to improve outcomes, especially in difficult-to-treat malignancies. The high linear energy transfer (LET) radiation induces complex DNA damage in tumour cells, leading to increased cell death compared to conventional radiotherapy. Alpha particles have a short range in tissue, allowing for highly localized treatment with minimal damage to surrounding healthy tissue. Recent studies have demonstrated that alpha radiation can stimulate antitumor immune responses, potentially enhancing treatment efficacy. Clinical trials utilizing alpha-emitting radioisotopes have shown encouraging results in various cancer types, particularly for metastatic disease.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.180
GPT teacher head0.420
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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