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Record W4409039997 · doi:10.1021/acsnano.4c18954

Regulating the Tumor Microbiome through Near-Infrared-III Light-Excited Photosynthesis

2025· article· en· W4409039997 on OpenAlexaff
Feiyu Li, Bingzhu Zheng, Jiafei Chen, Qilong Yan, Zijie Lu, Chao Fang, Yike Fu, Xiang Li

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersFundamental Research Funds for the Central UniversitiesChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsPhotosynthesisMicrobiomeExcited stateInfraredNanotechnologyArtificial photosynthesisMaterials scienceOptoelectronicsPhotochemistryAstrobiologyEnvironmental scienceBiologyChemistryPhotocatalysisBotanyOpticsPhysicsBioinformaticsAtomic physicsBiochemistry

Abstract

fetched live from OpenAlex

Tumor microbiomes are increasingly associated with the growth and metastasis of tumors. Exploring the regulation of the tumor microbiome through therapeutics is an area of interest in cancer therapy. In this study, the authors have investigated a biohybrid with 1550 nm light-excited photosynthetic ability to regulate the tumor microbiome. This system utilizes Er-based core–shell upconversion nanoparticles to arm microalga Chlorella, enabling the rapid evolution of Chlorella to perform oxygenic photosynthesis under 1550 nm light excitation. This biohybrid may alleviate hypoxia within the tumor microenvironment and induce significant changes in the tumor microbiome, ultimately resulting in marked inhibition of tumor growth. Benefiting from the strong tissue penetration ability of 1550 nm light, this biohybrid also exhibits clear inhibition of deep-seated tumors. The therapeutic efficacy of microbiome regulation is directly mediated by immune activation, converting “cold” tumors into “hot” tumors, which also leads to a long-lasting immune memory effect.

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 score0.177
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.207
Teacher spread0.201 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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