{"id":"W2430048569","doi":"10.3390/nano6060116","title":"Human Serum Albumin Nanoparticles for Use in Cancer Drug Delivery: Process Optimization and In Vitro Characterization","year":2016,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":187,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Victoria Hospital; Université de Montréal","funders":"","keywords":"Drug delivery; Drug; Cancer; In vitro; Nanoparticle; Human serum albumin; Albumin; Characterization (materials science); Process (computing); Chemistry; Pharmacology; Nanotechnology; Materials science; Medicine; Biochemistry; Computer science; Internal medicine","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":[0.0006967296,0.0002054913,0.0003450498,0.0001822012,0.00009242789,0.0001900605,0.0001563797,0.00009075314,0.0002160329],"category_scores_gemma":[0.00009671096,0.0001644539,0.00002287786,0.0001923364,0.00008542436,0.001314383,0.00006087152,0.00002032609,0.00001665875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001564744,"about_ca_system_score_gemma":0.00008595887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001635949,"about_ca_topic_score_gemma":0.0002608784,"domain_scores_codex":[0.9980677,0.0001851159,0.0005940853,0.0004893492,0.0001717137,0.0004920153],"domain_scores_gemma":[0.9992751,0.0001140239,0.000222032,0.0002047488,0.0001124985,0.00007161132],"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":[0.0003881904,0.0001019753,0.005169874,0.00007450892,0.000002430874,0.000004602607,0.000234235,0.0003304014,0.9931194,0.00001470899,0.00001094406,0.0005487204],"study_design_scores_gemma":[0.001983643,0.00003588513,0.008753264,0.0001657852,0.00001209471,0.000001695179,0.00002394636,0.0006524046,0.9880151,0.00008972996,0.00002071686,0.0002457749],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978541,0.00003463681,0.0001138862,0.0002742517,0.0003360864,0.0008542197,0.0004496289,0.00008221476,0.000001047058],"genre_scores_gemma":[0.9984179,0.00003080688,0.0007454377,0.000113785,0.00008356569,0.000480231,0.00003043237,0.00003812306,0.00005977504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005104348,"threshold_uncertainty_score":0.6706237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0199838046915035,"score_gpt":0.2657087234089422,"score_spread":0.2457249187174388,"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."}}