Myoglobin-loaded gadolinium nanotexaphyrins for oxygen synergy and imaging-guided radiosensitization therapy
Bibliographic record
Abstract
Abstract Gadolinium (Gd3+)-coordinated texaphyrin (Gd-Tex) is a promising radiosensitizer that entered clinical trials, but temporarily fails largely due to insufficient radiosensitization efficacy. Little attention has been given to using nanovesicles to improve its efficacy. Herein, Gd-Tex is transformed into building blocks “Gd-Tex-lipids” to self-assemble nanovesicles called Gd-nanotexaphyrins (Gd-NTs), realizing high density packing of Gd-Tex in a single nanovesicle and achieving high Gd-Tex accumulation in tumors. To elucidate the impact of O2 concentration on Gd-Tex radiosensitization, myoglobin (Mb) is loaded into Gd-NTs (Mb@Gd-NTs), resulting in efficient relief of tumor hypoxia and significant enhancement of Gd-Tex radiosensitization, eventually inducing the obvious long-term antitumor immune memory to inhibit tumor recurrence. In addition to Gd3+, the versatile Mb@Gd-NTs can also chelate 177Lu3+ (Mb@177Lu/Gd-NTs), enabling SPECT/MRI dual-modality imaging for accurately monitoring drug delivery in real-time. This “one-for-all” nanoplatform with the capability of chelating various trivalent metal ions exhibits broad clinical application prospects in imaging-guided radiosensitization therapy.
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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".