{"id":"W2145220115","doi":"10.1369/0022155415587978","title":"Using Pharmacokinetic Profiles and Digital Quantification of Stained Tissue Microarrays as a Medium-Throughput, Quantitative Method for Measuring the Kinetics of Early Signaling Changes Following Integrin-Linked Kinase Inhibition in an In Vivo Model of Cancer","year":2015,"lang":"en","type":"article","venue":"Journal of Histochemistry & Cytochemistry","topic":"Cell Adhesion Molecules Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Langara College; University of British Columbia; BC Cancer Agency; Centre for Drug Research and Development","funders":"","keywords":"Tissue microarray; Immunohistochemistry; In vivo; Protein kinase B; Cancer research; Integrin-linked kinase; Signal transduction; Breast cancer; Biology; Kinase; Pathology; Chemistry; Cancer; Cell biology; Protein kinase A; Medicine; Internal medicine","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":[0.001188695,0.0008201986,0.0005739019,0.0008913777,0.0003460615,0.0008077741,0.0005469882,0.0006773449,0.001940596],"category_scores_gemma":[0.0009013759,0.0006254897,0.0003458597,0.0009230923,0.0004149533,0.000641406,0.0002815851,0.00123125,0.0007491162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000966448,"about_ca_system_score_gemma":0.0006716205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00130979,"about_ca_topic_score_gemma":0.003259415,"domain_scores_codex":[0.9990008,0.0001746235,0.0000414458,0.0002391006,0.0004632267,0.00008076663],"domain_scores_gemma":[0.9994331,0.0001555449,0.0001592129,0.00007558115,0.0001461694,0.00003035393],"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":[0.0001238051,0.00003820816,0.0004685267,0.0000664925,0.00001335974,0.00001849958,0.00002809909,0.0005185465,0.9928513,0.0002352678,0.0002179637,0.005419889],"study_design_scores_gemma":[0.00002726713,0.000396805,0.006287999,0.000009362087,0.00004601027,0.0001966895,0.00003937146,0.02026141,0.9682946,0.0003607307,0.004041937,0.0000378117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2723565,0.00390633,0.7057717,0.0006478588,0.0002568457,0.0008820009,0.005142458,0.003404645,0.00763164],"genre_scores_gemma":[0.5741767,0.004317209,0.403324,0.0004996576,0.0001034938,0.002986047,0.00296905,0.0004644525,0.01115942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001940596,"threshold_uncertainty_score":0.007012069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1210134477358058,"score_gpt":0.4135936793173633,"score_spread":0.2925802315815574,"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."}}