{"id":"W2769965575","doi":"10.1038/s41467-017-01705-y","title":"Whole blood stabilization for the microfluidic isolation and molecular characterization of circulating tumor cells","year":2017,"lang":"en","type":"article","venue":"Nature Communications","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; National Institute of Biomedical Imaging and Bioengineering; Wellcome Trust; U.S. Department of Defense; Lustgarten Foundation; National Institutes of Health; National Science Foundation; Prostate Cancer Foundation; Howard Hughes Medical Institute","keywords":"Circulating tumor cell; Microfluidics; Isolation (microbiology); Whole blood; Characterization (materials science); Tumor cells; Computational biology; Nanotechnology; Biology; Cancer research; Cancer; Materials science; Immunology; Bioinformatics; Genetics; Metastasis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008403286,0.0005575728,0.0003727846,0.0004991752,0.0004733317,0.0007651771,0.0005697094,0.000488156,0.002769149],"category_scores_gemma":[0.001166087,0.0002532162,0.0003090117,0.0003110313,0.0004262298,0.0005254919,0.0005063016,0.0007323292,0.00104096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003935032,"about_ca_system_score_gemma":0.0006864507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005973252,"about_ca_topic_score_gemma":0.001072299,"domain_scores_codex":[0.9994062,0.0001262144,0.00004414398,0.0001774502,0.0001925841,0.00005343806],"domain_scores_gemma":[0.9994788,0.0001706952,0.0001219024,0.0001076119,0.00008358494,0.00003747623],"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":[0.0001108513,0.00002686441,0.0005145993,0.0001115276,0.00001508387,0.00004781135,0.00006764212,0.0001901957,0.9811321,0.0008945332,0.0009677784,0.01592087],"study_design_scores_gemma":[0.00001758494,0.0002050911,0.00260529,0.00002891209,0.00003369979,0.0003068444,0.00003050204,0.003042418,0.9740569,0.0004801476,0.01916938,0.00002325639],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3069335,0.0146311,0.6615393,0.002002185,0.001286843,0.0009074901,0.002677105,0.002192755,0.007829686],"genre_scores_gemma":[0.6640148,0.006814606,0.3142328,0.001262868,0.0004090023,0.001383695,0.002625795,0.0004335543,0.008822907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002769149,"threshold_uncertainty_score":0.009263694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01404837159244663,"score_gpt":0.2431472020159908,"score_spread":0.2290988304235442,"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."}}