{"id":"W3193334162","doi":"10.1039/d1lc00619c","title":"Microfluidic arrays of dermal spheroids: a screening platform for active ingredients of skincare products","year":2021,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; BASF Corporation","keywords":"Microfluidics; Spheroid; Dermis; Nanotechnology; 3d printed; In vitro; Biomedical engineering; Materials science; Chemistry; Biology; Engineering; Anatomy; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002084562,0.0001223106,0.0002325729,0.00008531695,0.00003270364,0.00001020804,0.0001950777,0.00009807282,0.0000465641],"category_scores_gemma":[0.0009323127,0.0001212136,0.00006920117,0.0003823327,0.00007030035,0.00006084306,0.00008726598,0.0002322248,0.000005220487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004664435,"about_ca_system_score_gemma":0.00007475359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009611367,"about_ca_topic_score_gemma":0.000003687302,"domain_scores_codex":[0.998842,0.00001813118,0.0002548597,0.0002112132,0.0003535149,0.000320261],"domain_scores_gemma":[0.9991753,0.0002027536,0.00005018012,0.0002691951,0.0002230944,0.00007951698],"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":[0.0001992489,0.0001566784,0.001426013,0.001924994,0.0002377502,0.00001732159,0.001762949,0.0002238213,0.9002234,0.0004378462,0.002544291,0.09084563],"study_design_scores_gemma":[0.0006988719,0.00007766148,0.004111659,0.000382131,0.00001122135,0.000003739955,0.0002524382,0.0009886023,0.9901283,0.0001187519,0.003113393,0.0001132753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932466,0.0007496147,0.003636619,0.00008810071,0.0001933242,0.0002781429,0.00007119542,0.00006470431,0.001671724],"genre_scores_gemma":[0.9913232,0.00009933738,0.008260097,0.00002151711,0.0001140648,0.00002366741,0.00003616585,0.0000370178,0.0000848959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09073236,"threshold_uncertainty_score":0.4942946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03000570781968072,"score_gpt":0.2740334241902018,"score_spread":0.2440277163705211,"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."}}