{"id":"W2912561739","doi":"10.1039/c8nh00473k","title":"Detecting and targeting senescent cells using molecularly imprinted nanoparticles","year":2019,"lang":"en","type":"article","venue":"Nanoscale Horizons","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"Biotechnology and Biological Sciences Research Council; Tertiary Education Trust Fund; University of Leicester","keywords":"In vivo; Nanoparticle; Molecularly imprinted polymer; Chemistry; Nanotechnology; Biophysics; Cell biology; Molecular biology; Cancer research; Materials science; Biology; Biochemistry; Genetics; Selectivity","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":[0.0001806677,0.0003895643,0.0001509541,0.0001791398,0.00006903989,0.0002404223,0.0002034202,0.0003050399,0.0006833799],"category_scores_gemma":[0.0001869648,0.0001871357,0.0002142488,0.00009404056,0.000161672,0.0002544027,0.0002277018,0.0003685371,0.0005821494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659701,"about_ca_system_score_gemma":0.0001138629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001699999,"about_ca_topic_score_gemma":0.0002343596,"domain_scores_codex":[0.9998966,0.00001216726,0.000005984287,0.00003336023,0.00003208339,0.00001981462],"domain_scores_gemma":[0.9999382,0.00001368623,0.00002286009,0.000004889413,0.00001152172,0.000008736521],"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":[0.00003377963,0.000009800274,0.00009417279,0.00003354128,0.000003997322,0.00004552848,0.000007692103,0.00009535824,0.9963388,0.0001462579,0.0001575971,0.003033452],"study_design_scores_gemma":[0.00000307864,0.00005455574,0.0003402944,0.000002935872,0.000005854464,0.0001185251,0.000004691801,0.0008792411,0.9967043,0.0000389644,0.001844596,0.000003097926],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8569583,0.009284738,0.1204305,0.001042876,0.0003337791,0.000153787,0.0008254009,0.001584707,0.009385914],"genre_scores_gemma":[0.9055625,0.006714266,0.07316349,0.0009669671,0.0001935525,0.000166283,0.0008580022,0.0001263281,0.01224866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006833799,"threshold_uncertainty_score":0.002286077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006737279095681894,"score_gpt":0.2426562922638808,"score_spread":0.2359190131681989,"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."}}