{"id":"W2996488494","doi":"10.1021/acs.nanolett.9b04152","title":"NanoMEA: A Tool for High-Throughput, Electrophysiological Phenotyping of Patterned Excitable Cells","year":2019,"lang":"en","type":"article","venue":"Nano Letters","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network","funders":"National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Cancer Institute; National Heart, Lung, and Blood Institute","keywords":"Multielectrode array; Electrophysiology; Induced pluripotent stem cell; Neurite; Throughput; Neuroscience; Nanotechnology; Biophysics; Chemistry; Materials science; Microelectrode; Biology; Computer science; In vitro; Biochemistry; Electrode; Embryonic stem cell","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":[0.0006804675,0.0006860707,0.000671109,0.001075588,0.0003335382,0.0007365408,0.0006682137,0.0006271678,0.00218374],"category_scores_gemma":[0.0007815399,0.0004673071,0.0003032971,0.000521875,0.0004305604,0.0006606042,0.0007756837,0.001077665,0.00104456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003129701,"about_ca_system_score_gemma":0.0001986043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002791353,"about_ca_topic_score_gemma":0.0009441889,"domain_scores_codex":[0.9994915,0.00007080226,0.0000432245,0.0001298042,0.0002324574,0.00003226583],"domain_scores_gemma":[0.9993768,0.0003040386,0.00008646624,0.0001315558,0.00006021718,0.00004091792],"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":[0.00002347542,0.00001835071,0.0001402511,0.00009188851,0.00001249834,0.0000542556,0.00002551847,0.0003046369,0.9865988,0.0004944408,0.0003964151,0.01183944],"study_design_scores_gemma":[0.00001027129,0.00007566172,0.001721814,0.00002070572,0.00001885026,0.0003886847,0.00001775759,0.007157263,0.9795559,0.0005335584,0.01047533,0.00002418665],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06689387,0.003486997,0.9163565,0.0003913105,0.0002332022,0.0002376816,0.002247115,0.006683475,0.003469758],"genre_scores_gemma":[0.1612822,0.00240161,0.8298121,0.0002483061,0.00006921803,0.001067158,0.001243027,0.0005196201,0.0033567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00218374,"threshold_uncertainty_score":0.007305324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152697279574556,"score_gpt":0.2207043404464439,"score_spread":0.2054346124889883,"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."}}