{"id":"W4385904643","doi":"10.1038/s41392-023-01539-9","title":"VEGF-B prevents excessive angiogenesis by inhibiting FGF2/FGFR1 pathway","year":2023,"lang":"en","type":"article","venue":"Signal Transduction and Targeted Therapy","topic":"Fibroblast Growth Factor Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre","funders":"State Key Laboratory of Ophthalmology; Novo Nordisk Fonden; National Natural Science Foundation of China-Yunnan Joint Fund; Sun Yat-sen University; National Natural Science Foundation of China","keywords":"Angiogenesis; Fibroblast growth factor receptor 1; Cancer research; MAPK/ERK pathway; In vivo; Fibroblast growth factor; Function (biology); VEGF receptors; Vascular endothelial growth factor; Biology; Medicine; Pharmacology; Chemistry; Cell biology; Signal transduction; Internal medicine; Receptor; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001629373,0.0002300992,0.0001869636,0.0001767991,0.0001248826,0.0001708356,0.0001866994,0.0002205687,0.0009975729],"category_scores_gemma":[0.0001183505,0.00007395088,0.0001488107,0.00007927649,0.0001791398,0.0001284955,0.0001246303,0.0004144324,0.0002791188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001967296,"about_ca_system_score_gemma":0.0003125085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006209962,"about_ca_topic_score_gemma":0.0007770653,"domain_scores_codex":[0.9998926,0.00002899208,0.000009886622,0.00001706023,0.00002664538,0.00002474086],"domain_scores_gemma":[0.9999593,0.000006697111,0.00001265301,0.000004912466,0.000004927424,0.00001155056],"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":[0.0001012813,0.00003909124,0.00008410445,0.00006324371,0.000004587397,0.00003345223,0.000006843654,0.00007495986,0.9963469,0.0002376141,0.0001097575,0.00289813],"study_design_scores_gemma":[0.00002530137,0.0004116374,0.001021172,0.000009639461,0.000008111537,0.0002471801,0.000009500945,0.0005534479,0.9923657,0.0000840907,0.005260692,0.000003490679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732649,0.01057125,0.009916767,0.0005736408,0.0001298205,0.00007359798,0.0002519781,0.000249505,0.00496853],"genre_scores_gemma":[0.9910615,0.002475453,0.004229391,0.0001003569,0.00002144625,0.00003551807,0.0001562779,0.00001745641,0.00190258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009975729,"threshold_uncertainty_score":0.003337204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602937425335377,"score_gpt":0.258292156353639,"score_spread":0.2422627821002852,"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."}}