Convection-enhanced delivery of [177Lu]Lu-labeled gold nanoparticles combined with anti-PD1 checkpoint immunotherapy improves the survival of immunocompetent C57BL/6J mice with orthotopic GL261 murine glioma tumors
Bibliographic record
Abstract
INTRODUCTION: Lu]Lu-MCP-AuNP) alone or combined with anti-PD1 immune checkpoint inhibition (ICI) for improving the survival of immunocompetent C57BL/6J mice with orthotopic GL261 murine glioma tumors. METHODS: AuNP) alone or combined with anti-PD1 antibodies (200 μg i.p. every 2 d × 3 doses). Control mice received normal saline, non-radioactive MCP-AuNP or anti-PD1 antibodies. Kaplan-Meier median survival was estimated. T-cell infiltration into the brain was probed by flow cytometry. Toxicity was assessed by monitoring body weight and cognitive function tests [Object Location Test (OLT) and Novel Object Recognition Test (NORT)] and T2-weighted MRI of the brain, overall health and ex vivo histopathological examination of the brain. RESULTS: Lu]Lu-MCP-AuNP combined with anti-PD1 antibodies did not demonstrate overall deleterious health effects. CONCLUSIONS: Lu]Lu-MCP-AuNP (2.7 MBq vs. 0.8 MBq) were most effective and yielded long-term survival. ADVANCES IN KNOWLEDGE AND IMPLICATIONS FOR PATIENT CARE: Lu]Lu-MCP-AuNP and anti-PD1 checkpoint immunotherapy improved the survival of mice with glioma tumors in the brain. In the future, this treatment may be useful to treat residual tumor at the surgical margins in patients with GBM to prevent local recurrence and improve survival.
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 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 | 0.001 |
| 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".