Toward Glioblastoma Treatment with High Spatiotemporal Precision: Controlling Temozolomide Release from Upconverting Nanoparticles Using Near-Infrared Light
Bibliographic record
Abstract
As the first-line chemotherapeutic for glioblastoma multiforme (GBM), Temozolomide (TMZ) suffers from rapid degradation in physiological fluid, making it difficult to deliver sufficient doses of active TMZ to GBM tumors without inducing severe side effects. By protecting TMZ and then controlling its release using an external stimulus, we can prevent its premature degradation, thereby increasing its active concentration at the tumor site. Here, we present a near-infrared (NIR) controlled system in which TMZ is protected within a polymer before its on-demand release. NIR light is preferable, given its high penetration depth and noncarcinogenicity, but ultraviolet (UV) light is required to cleave chemical bonds. Upconverting nanoparticles (UCNPs) overcome this challenge as they convert NIR to UV, a property of the rare-earth lanthanides of which they are comprised. In this work, we coat UCNPs with poly(acrylic acid) (PAA) conjugated to a UV-cleavable nitrobenzyl photolinker (PhL) covalently linked to TMZ and show that this UCNP@PAA-PhL-TMZ system releases TMZ under low laser power (1 W/cm 2 ) NIR, with 100% release after 30 min irradiation. When a 5 min NIR pulse was used to trigger TMZ release after NP internalization into U87MG GBM cells, we observed significant cell death after 24 h, even at concentrations an order of magnitude lower than current TMZ tumor concentrations, with multiple pulses inducing a larger effect. This work highlights the importance of drug release after cellular internalization and is a key step toward enhancing TMZ effectiveness as well as developing a tool to administer time-sensitive treatments personalized to individual GBM patients.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".