{"id":"W2944382010","doi":"10.1039/c9nr01967g","title":"Super-resolution microscopy can identify specific protein distribution patterns in platelets incubated with cancer cells","year":2019,"lang":"en","type":"article","venue":"Nanoscale","topic":"Blood properties and coagulation","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Stockholms Läns Landsting; Kungliga Tekniska Högskolan; Uppsala Universitet; Cancerfonden","keywords":"Resolution (logic); Microscopy; Platelet; Distribution (mathematics); Nanotechnology; Cancer cell; Cancer; Materials science; Biophysics; Computational biology; Biology; Computer science; Pathology; Immunology; Artificial intelligence; Medicine; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000815356,0.0001417181,0.0002216666,0.00005654056,0.00004513701,0.00002830836,0.00005887066,0.000131993,0.000371779],"category_scores_gemma":[0.000003153602,0.0001107957,0.00003409076,0.0002254876,0.00002940316,0.00009484291,0.00002411756,0.0001912731,0.00006582787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000226508,"about_ca_system_score_gemma":0.00007875851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002863155,"about_ca_topic_score_gemma":0.00112117,"domain_scores_codex":[0.9989333,0.00003627741,0.0002222034,0.0002924181,0.0002371875,0.0002786313],"domain_scores_gemma":[0.9995586,0.000006720417,0.00006126851,0.0002234359,0.0000820705,0.00006787391],"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":[0.0004594348,0.0001292087,0.1560779,0.0001090921,0.00001446639,0.00001543329,0.000197565,0.00005207399,0.8412111,0.000009908349,0.0004181901,0.001305574],"study_design_scores_gemma":[0.002202149,0.0003037506,0.2863208,0.0005395433,0.00002172175,0.000009500584,0.0001028723,0.0003694458,0.7059239,0.000005726132,0.00403968,0.0001608682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973657,0.0003053482,0.000138797,0.000663215,0.0001960986,0.00110075,0.0001017716,0.00004241249,0.0000859646],"genre_scores_gemma":[0.997784,0.00005724596,0.00005449152,0.00008515191,0.00007688295,0.00007761794,0.0003359126,0.00002292704,0.00150576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1352872,"threshold_uncertainty_score":0.4518118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133022170233829,"score_gpt":0.2460924389469865,"score_spread":0.2347622172446482,"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."}}