{"id":"W3209891406","doi":"10.3389/fbioe.2021.756399","title":"Adapting the Scar-in-a-Jar to Skin Fibrosis and Screening Traditional and Contemporary Anti-Fibrotic Therapies","year":2021,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"Dermatologic Treatments and Research","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"H2020 European Research Council; Biocenter Finland; European Commission; European Regional Development Fund; Opetushallitus; Finnish National Board of Education; Tays; Academy of Finland; Pirkanmaan Sairaanhoitopiiri; Tampereen Tuberkuloosisäätiö; Science Foundation Ireland; Päivikki ja Sakari Sohlbergin Säätiö","keywords":"Myofibroblast; Fibrosis; Extracellular matrix; Cancer research; Medicine; Macromolecular crowding; Fibroblast; Transforming growth factor; In vitro; Cell biology; Chemistry; Pathology; Biology; Internal medicine; Biochemistry","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":[0.0001213011,0.000114968,0.0002598999,0.0002636116,0.00006299271,0.00002447396,0.00004334154,0.0001712732,0.000002007899],"category_scores_gemma":[0.00004673325,0.00007788298,0.00001879716,0.0002747183,0.0001857477,0.00003425304,0.00005629655,0.0002388429,2.015243e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001686095,"about_ca_system_score_gemma":0.00002028426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002913451,"about_ca_topic_score_gemma":0.000003699446,"domain_scores_codex":[0.9992937,0.00002386678,0.0001441001,0.0002489204,0.00007677422,0.0002126561],"domain_scores_gemma":[0.999757,0.00004804719,0.00001563367,0.0001149327,0.00001170146,0.00005269114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000206491,0.0002195937,0.7612339,0.0004331572,0.0004049125,0.001248766,0.00134097,0.0001728012,0.04541681,0.002201648,0.002218632,0.1849023],"study_design_scores_gemma":[0.003798989,0.0005751384,0.9434534,0.0009197242,0.0000370748,0.001687999,0.007971385,0.014543,0.01829967,0.0005177417,0.007794266,0.0004015943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639573,0.01800592,0.002935602,0.01472208,0.00008030931,0.0001983928,0.000007979676,0.00004734082,0.00004509851],"genre_scores_gemma":[0.9744916,0.001398311,0.02391638,0.0001044911,0.00001766931,0.00001431432,0.000006515555,0.000009315885,0.00004140117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1845007,"threshold_uncertainty_score":0.3175976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02748421775250761,"score_gpt":0.2436054718414169,"score_spread":0.2161212540889093,"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."}}