{"id":"W4408213337","doi":"10.1021/acs.nanolett.5c00285","title":"A Semiconducting Polymer NanoCRISPR for Near-Infrared Photoactivatable Gene Editing and Cancer Gene Therapy","year":2025,"lang":"en","type":"article","venue":"Nano Letters","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre","funders":"Fundamental Research Funds for the Central Universities; Science and Technology Commission of Shanghai Municipality; Donghua University; Fundo para o Desenvolvimento das Ciências e da Tecnologia; Universidade de Macau; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Gene; Genetic enhancement; Polymer; Infrared; Cancer; Cancer research; Biology; Genetics; Nanotechnology; Medicine; Computational biology; Chemistry; Materials science; Physics; Optics; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008710464,0.0002022673,0.0001276416,0.0001215775,0.0001384604,0.0001536484,0.0001691288,0.0003365345,0.0007963789],"category_scores_gemma":[0.0001085914,0.0000909753,0.0001336951,0.00009978558,0.0001721111,0.000202411,0.0001229975,0.0003523891,0.0003798632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002295719,"about_ca_system_score_gemma":0.000171531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002123568,"about_ca_topic_score_gemma":0.0005247036,"domain_scores_codex":[0.9999151,0.000009682893,0.000003442159,0.00002712295,0.00003529319,0.000009296489],"domain_scores_gemma":[0.9999403,0.00001938923,0.00001690199,0.00000561216,0.00001044348,0.000007300548],"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":[0.00001397487,0.00001069867,0.00005323725,0.00005001306,0.000002915159,0.00003917537,0.000007395424,0.0001886613,0.9935299,0.0003762736,0.0001400134,0.005587741],"study_design_scores_gemma":[0.000006142244,0.0001297381,0.0003746538,0.000003944262,0.000008641944,0.00021747,0.000006239803,0.003071405,0.9900271,0.00009817184,0.006052231,0.000004152334],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8290824,0.007640468,0.1370535,0.0009550539,0.0003506134,0.0001613716,0.0002720515,0.001376384,0.0231083],"genre_scores_gemma":[0.9510419,0.001869625,0.03795352,0.0002787151,0.00003541326,0.00005986938,0.0001544661,0.00007307845,0.008533485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007963789,"threshold_uncertainty_score":0.002664149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008912498019303645,"score_gpt":0.2924127555946608,"score_spread":0.2835002575753571,"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."}}