MétaCan
Menu
Back to cohort
Record W4408858711 · doi:10.1021/acsnano.4c17080

Leveraging Vitamin C to Augment Nanoenabled Photothermal Immunotherapy

2025· article· en· W4408858711 on OpenAlexaff
Wuxian Deng, Yiyuan Wang, Junyu Wang, Yi‐Tan Su, Mingyang Li, Keming Qu

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsResponse Biomedical (Canada)
FundersAnhui Provincial Key Research and Development PlanNational Key Research and Development Program of ChinaNatural Science Foundation of Anhui ProvinceMajor Science and Technology Projects in Anhui ProvinceChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsPhotothermal therapyImmunotherapyImmune systemCancer researchT cellPhotosensitizerTumor microenvironmentVitamin CImmunologyMedicineChemistryMaterials scienceInternal medicineNanotechnology

Abstract

fetched live from OpenAlex

Photothermal immunotherapy (PTI) is valuable for precise tumor targeting and immune activation. However, its efficacy is hindered by insufficient immune response, elevated antioxidant levels within tumor, and intrinsic tumor resistance mechanisms. This study introduces Vitamin C (VC), a widely available dietary nutrient, as an effective enhancer for PTI. High-dose VC induces oxidative imbalance in tumor cells, making them more susceptible to nanoenabled near-infrared-II photothermal therapy (NIR-II PTT) with the photosensitizer IR1080. The combination of VC and NIR-II PTT significantly amplifies antitumor immunity by upregulating CXCL16 expression and promoting CXCR6 + T cell infiltration. Clinical data reveal that higher CXCL16 and CXCR6 levels in human tumors correlate with improved survival and T cell infiltration, underscoring the translational potential of this approach. This study positions VC as a safe, accessible, and cost-effective dietary enhancer for PTI, reshaping the role of dietary nutrients in cancer therapy and offering a strategy for overcoming treatment resistance.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.213
Teacher spread0.208 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations8
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueACS NanoSame topicNanoplatforms for cancer theranosticsFrench-language works237,207