{"id":"W2057590353","doi":"10.1016/j.actbio.2012.09.025","title":"Human insulin adsorption kinetics, conformational changes and amyloidal aggregate formation on hydrophobic surfaces","year":2012,"lang":"en","type":"article","venue":"Acta Biomaterialia","topic":"Advanced Drug Delivery Systems","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Delhi; University of Toronto; Connaught Fund; Agence Nationale de la Recherche","keywords":"Insulin; Adsorption; Amide; Kinetics; Monolayer; Surface plasmon resonance; Biophysics; Dissociation (chemistry); Protein aggregation; Chemistry; Crystallography; Hydrophobic effect; Conformational change; Thioflavin; Materials science; Stereochemistry; Organic chemistry; Biochemistry; Nanotechnology; Biology; Nanoparticle","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002593943,0.0002237184,0.0001490959,0.0001599731,0.0001968835,0.0002290597,0.000139122,0.0002637415,0.001416413],"category_scores_gemma":[0.0005421511,0.0001737488,0.0003068454,0.0001891479,0.0001994422,0.0002693941,0.0001444545,0.0003882016,0.0003295781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001804232,"about_ca_system_score_gemma":0.00008933647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001239333,"about_ca_topic_score_gemma":0.0005612372,"domain_scores_codex":[0.9997771,0.00005221575,0.00001345772,0.00003711065,0.00005670567,0.00006332149],"domain_scores_gemma":[0.9997111,0.0001528029,0.00003626307,0.00002712697,0.00004543896,0.00002727272],"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":[0.0008168095,0.00005755304,0.001074831,0.00002845354,0.00001413918,0.00008531768,0.00008639495,0.0002882129,0.9958634,0.00005321157,0.00004299972,0.001588648],"study_design_scores_gemma":[0.00001611833,0.0005496038,0.01399095,0.000003659272,0.00002435097,0.0001201458,0.0000879673,0.003423373,0.9813483,0.00004760806,0.0003760337,0.0000119325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984428,0.0002693634,0.0006151799,0.00001670734,0.000009678857,0.000005514784,0.00005694252,0.000007835853,0.0005759383],"genre_scores_gemma":[0.9984618,0.0001924148,0.0003187833,0.00001860393,0.000006817828,0.000006195484,0.00009504581,0.000005558471,0.0008946935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001416413,"threshold_uncertainty_score":0.00473839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0867892928293183,"score_gpt":0.3830327402641244,"score_spread":0.2962434474348061,"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."}}