{"id":"W3209524127","doi":"10.1016/j.xphs.2021.10.037","title":"Aggregation and Size Attributes Analysis of Unadsorbed and Adjuvant-adsorbed Antigens using a Multispectral Imaging Flow Cytometer Platform","year":2021,"lang":"en","type":"article","venue":"Journal of Pharmaceutical Sciences","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Scarborough Hospital; University of Toronto; Toronto Metropolitan University; Sanofi (Canada)","funders":"Mitacs","keywords":"Adjuvant; Flow cytometry; Microscopy; Antigen; Dynamic light scattering; Particle size; Fluorescence microscope; Fluorescence; Light scattering; Multispectral image; Biophysics; Biomedical engineering; Chemistry; Chromatography; Biology; Materials science; Biological system; Nanotechnology; Optics; Medicine; Nanoparticle; Pathology; Immunology; Computer science; Scattering; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004105091,0.0003188821,0.0002730013,0.0005238134,0.000255993,0.0003628909,0.0002168302,0.0002902577,0.0007427715],"category_scores_gemma":[0.0003702964,0.0001472222,0.0002761669,0.0003636298,0.0001935165,0.0002942162,0.0001993233,0.0006067985,0.0002516017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002607755,"about_ca_system_score_gemma":0.0002157206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000441626,"about_ca_topic_score_gemma":0.0006691221,"domain_scores_codex":[0.9996735,0.00003284817,0.00002973681,0.00008036301,0.0001263804,0.00005712068],"domain_scores_gemma":[0.9996454,0.0001091871,0.00007151626,0.00002599635,0.00011149,0.00003645649],"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":[0.00002275612,0.00001463918,0.0001105104,0.000008703856,0.000002012983,0.000004372889,0.0000159379,0.00003599795,0.9991006,0.00002778304,0.00001536255,0.000641359],"study_design_scores_gemma":[0.0000038827,0.00008066857,0.003465713,0.000002392833,0.00001395087,0.00003503887,0.00001776088,0.003084352,0.9927451,0.00003600703,0.0005063231,0.000008813658],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753541,0.0008479009,0.02218851,0.00006792334,0.00004745704,0.00005558114,0.0002911674,0.0001518721,0.0009955168],"genre_scores_gemma":[0.9521846,0.001073071,0.04144425,0.0002028736,0.00006195372,0.000283675,0.000960346,0.00007024638,0.003719036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007427715,"threshold_uncertainty_score":0.002484798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09510074144223574,"score_gpt":0.4185259236061386,"score_spread":0.3234251821639029,"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."}}