{"id":"W4318693896","doi":"10.32920/21985085","title":"Optimizing metastatic-cascade-dependent Rac1 targeting in breast cancer: Guidance using optical window intravital FRET imaging","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; AstraZeneca (Canada)","funders":"Servier; Cancer Institute NSW; National Breast Cancer Foundation; National Health and Medical Research Council; Cancer Research UK","keywords":"RAC1; In vivo; Intravital microscopy; Förster resonance energy transfer; Cancer research; Fluorescence-lifetime imaging microscopy; Breast cancer; Medicine; Cancer; Pathology; Biology; Internal medicine; Fluorescence; Cell biology; Signal transduction","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":[0.0002660448,0.0003236246,0.000345588,0.0001890965,0.0002005568,0.0004721707,0.0003697038,0.0005546768,0.001405822],"category_scores_gemma":[0.0002286677,0.0002023055,0.0002109788,0.000138341,0.000306727,0.0004097537,0.0003176556,0.000617669,0.0005006266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006244655,"about_ca_system_score_gemma":0.0003357572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135924,"about_ca_topic_score_gemma":0.001818509,"domain_scores_codex":[0.999882,0.00001340229,0.000004881358,0.00003634261,0.0000321522,0.00003119277],"domain_scores_gemma":[0.9999017,0.00002780354,0.0000321334,0.00001199533,0.00001243353,0.00001390204],"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":[0.00002047512,0.00001473395,0.000109834,0.00001780785,0.000002250817,0.00003283324,0.00001640528,0.0006437125,0.9965734,0.000358368,0.0001219572,0.002088296],"study_design_scores_gemma":[0.000003431209,0.00002607886,0.0004015141,0.000002158393,0.000002847699,0.00006199873,0.00001122612,0.007536295,0.9909223,0.00007945496,0.0009474161,0.000005316839],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9222463,0.0009717325,0.07029799,0.0005345704,0.00004415349,0.00006462429,0.0004249763,0.0008438799,0.004571736],"genre_scores_gemma":[0.9435921,0.001115622,0.04824022,0.0001215132,0.00001361263,0.00008213211,0.000245607,0.0002702198,0.00631898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001405822,"threshold_uncertainty_score":0.004702985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838248610716789,"score_gpt":0.3276059925467476,"score_spread":0.3092235064395797,"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."}}