{"id":"W3083872481","doi":"10.1021/acsami.0c09931","title":"Aggregated Amphiphilic Antimicrobial Peptides Embedded in Bacterial Membranes","year":2020,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Innovate UK; Science and Technology Facilities Council; Biotechnology and Biological Sciences Research Council; University of Manchester; Diamond Light Source; China Scholarship Council; Syngenta Canada; Unilever; AstraZeneca","keywords":"Membrane; Antimicrobial peptides; Zeta potential; Antimicrobial; Amphiphile; Peptide; Biophysics; Cytotoxicity; Hemolysis; Dynamic light scattering; Materials science; Biochemistry; Chemistry; Nanotechnology; Microbiology; Biology; Nanoparticle; In vitro; Polymer","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002003814,0.0004658483,0.0008426511,0.0001201754,0.0001500709,0.0001760947,0.0005065703,0.0003751722,0.00222494],"category_scores_gemma":[0.00005234782,0.0004278849,0.00004109238,0.0001751601,0.0003757785,0.0002384785,0.0003165543,0.000269605,0.001371596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003320261,"about_ca_system_score_gemma":0.00005232299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002772984,"about_ca_topic_score_gemma":0.0000543836,"domain_scores_codex":[0.9977921,0.0001506697,0.0007137295,0.0006506481,0.00004901759,0.0006437993],"domain_scores_gemma":[0.9992844,0.00008815075,0.000287277,0.0002622718,0.00003964688,0.00003826394],"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":[0.001476475,0.00009503242,0.00002517584,0.000149678,0.0001159722,0.00001009824,0.00167716,0.00002072319,0.9907625,0.0003501991,0.004868285,0.000448746],"study_design_scores_gemma":[0.001788237,0.0001117774,0.0001005949,0.00009970265,0.00003697969,0.00002117343,0.0005649787,1.406839e-7,0.9951459,0.0000972782,0.001563784,0.0004695052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99598,0.0001807777,0.000004154042,0.001147667,0.001028123,0.0005111601,0.0002565999,0.0002398448,0.0006516676],"genre_scores_gemma":[0.997925,0.0001643626,0.00007696904,0.0009026339,0.0002582802,0.00003934846,0.0003326398,0.00006200996,0.0002387902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004383393,"threshold_uncertainty_score":0.9998173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563886467682221,"score_gpt":0.2194446413755478,"score_spread":0.2038057766987256,"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."}}