{"id":"W4409039997","doi":"10.1021/acsnano.4c18954","title":"Regulating the Tumor Microbiome through Near-Infrared-III Light-Excited Photosynthesis","year":2025,"lang":"en","type":"article","venue":"ACS Nano","topic":"Nanoplatforms for cancer theranostics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Photosynthesis; Microbiome; Excited state; Infrared; Nanotechnology; Artificial photosynthesis; Materials science; Optoelectronics; Photochemistry; Astrobiology; Environmental science; Biology; Chemistry; Photocatalysis; Botany; Optics; Physics; Bioinformatics; Atomic physics; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001319293,0.0002632864,0.0002685282,0.00007943049,0.0002839335,0.0001420289,0.0004697279,0.0001117479,0.000073838],"category_scores_gemma":[0.00006025527,0.0002028053,0.0001112828,0.0008500008,0.00007725743,0.0002302856,0.00008911749,0.0001999408,0.00009252885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001699012,"about_ca_system_score_gemma":0.00005778103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004893665,"about_ca_topic_score_gemma":0.00001635314,"domain_scores_codex":[0.9988091,0.00001578781,0.0003605386,0.0002271042,0.0001516859,0.0004357977],"domain_scores_gemma":[0.9990112,0.0001969699,0.0000570911,0.0006462125,0.00005308037,0.00003542563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002560717,0.00001545163,0.00007818593,0.00009240746,0.0001881296,0.000007280343,0.0008143918,0.0008232591,0.9804966,0.0005855182,0.01425749,0.002615604],"study_design_scores_gemma":[0.0004523969,0.00001320949,0.0001081845,0.0001568076,0.00005643574,0.000008994102,0.0001164063,0.001921034,0.804097,0.001485278,0.1913429,0.0002413847],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9532994,0.002198925,0.003001843,0.0005571655,0.00180931,0.0007202054,0.00004683412,0.001110113,0.0372562],"genre_scores_gemma":[0.9948753,0.00006222138,0.001429809,0.0006433284,0.0001039512,0.00005061982,0.00001040735,0.00007204032,0.002752327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1770854,"threshold_uncertainty_score":0.827016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006394073105741443,"score_gpt":0.2074202402329134,"score_spread":0.2010261671271719,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}