{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001693003,0.0001791913,0.0001762278,0.00005267404,0.0001248082,0.00004199864,0.00009333596,0.000152082,0.000002199113],"category_scores_gemma":[0.00004844611,0.0001659436,0.00009184013,0.0001434241,0.00006654173,0.000006472285,0.0001540143,0.0001050174,0.000004222995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002123388,"about_ca_system_score_gemma":0.00002907347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002653541,"about_ca_topic_score_gemma":0.000009901299,"domain_scores_codex":[0.9988461,0.0000615537,0.0002168049,0.0004428347,0.0001231721,0.0003096097],"domain_scores_gemma":[0.9994421,0.00001187539,0.00009757635,0.0002743813,0.00008498487,0.00008905469],"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":[0.00002228795,0.0000244634,0.002709988,0.00001230345,0.00002794759,0.000003033832,0.00001192465,0.000006810964,0.9950193,0.000007547568,0.00001328021,0.002141127],"study_design_scores_gemma":[0.0002066434,0.0001959085,0.0001899967,0.00002455669,0.00003269063,0.00001675265,0.0001125295,0.0009765354,0.9967721,0.00002131359,0.001219149,0.0002318753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913297,0.0002987578,0.007917212,0.00003173968,0.0001099205,0.0001655963,0.000006851355,0.000056178,0.00008399163],"genre_scores_gemma":[0.9751011,0.00007179327,0.02451719,0.00007168782,0.00008541816,0.000002699251,0.00001164852,0.00002758026,0.0001109042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01659998,"threshold_uncertainty_score":0.6766983,"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."}}