{"id":"W4410944978","doi":"10.1186/s12951-025-03467-y","title":"In situ and dynamic screening of extracellular vesicles as predictive biomarkers in immune-checkpoint inhibitor therapies","year":2025,"lang":"en","type":"article","venue":"Journal of Nanobiotechnology","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; State Key Laboratory of Mechanical Transmissions; Key Technology Research and Development Program of Shandong; National Natural Science Foundation of China; Shandong University","keywords":"Extracellular vesicles; In situ; Chemistry; Nanotechnology; Medicine; Cell biology; Biology; Materials science","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":[0.0005497066,0.0001574663,0.0003297042,0.0005915939,0.00002369349,0.000008675715,0.0002862678,0.0004411349,0.000003897811],"category_scores_gemma":[0.0003001322,0.0001524884,0.0001138843,0.0003235521,0.0003735301,0.00001541208,0.0001813085,0.0003190529,3.826867e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004209306,"about_ca_system_score_gemma":0.0001285334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001463207,"about_ca_topic_score_gemma":0.00002473433,"domain_scores_codex":[0.9986231,0.0001090649,0.0007274342,0.000226448,0.0001028564,0.0002110882],"domain_scores_gemma":[0.999204,0.00003760331,0.0003852677,0.0002567006,0.00008033527,0.00003609595],"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":[0.0007559238,0.0001639882,0.003194426,0.00004675102,0.0001740685,0.00009567176,0.00007792499,0.00003944179,0.9886063,0.0002172016,0.00002111565,0.006607199],"study_design_scores_gemma":[0.001716204,0.0005936722,0.00712422,0.0002625499,0.00003048758,0.00009400467,0.0009603485,0.0001433521,0.9859074,0.002470245,0.0005751597,0.0001222963],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9513634,0.04502375,0.002279496,0.0009941118,0.0001034159,0.000156849,0.000004317386,0.000005515559,0.00006910318],"genre_scores_gemma":[0.9959136,0.001617375,0.002341492,0.00003629394,0.00001572432,0.000004075822,0.000003010617,0.00001415839,0.0000543277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0445501,"threshold_uncertainty_score":0.6218296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003762239560011006,"score_gpt":0.2416504081117847,"score_spread":0.2378881685517737,"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."}}