{"id":"W2566502464","doi":"10.1073/pnas.1609569114","title":"Clarifying intact 3D tissues on a microfluidic chip for high-throughput structural analysis","year":2016,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Microscale chemistry; Microfluidics; Microfluidic chip; Throughput; Nanotechnology; Organ-on-a-chip; Chip; Regenerative medicine; Computer science; Materials science; Cell biology; Biology; Stem cell","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003477729,0.0004539997,0.0003276155,0.0004338403,0.000339687,0.0006261464,0.0004988998,0.0005152129,0.001078778],"category_scores_gemma":[0.0005160683,0.0003445905,0.0003735233,0.0002414079,0.0003861526,0.0004711843,0.000431515,0.0005211331,0.0004282552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004368573,"about_ca_system_score_gemma":0.0006022642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006791494,"about_ca_topic_score_gemma":0.001976599,"domain_scores_codex":[0.9998105,0.00001944608,0.00001344219,0.00005770647,0.00007043459,0.00002850339],"domain_scores_gemma":[0.9996495,0.0001779281,0.0000521447,0.0000578801,0.00004079277,0.00002167721],"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":[0.00002187067,0.00002183362,0.0003775002,0.00005070552,0.00001048074,0.00006254569,0.00003308927,0.003164696,0.9836012,0.0009006017,0.0002691131,0.01148636],"study_design_scores_gemma":[0.00001557469,0.00009017797,0.002050904,0.000009818537,0.00001607641,0.0001807895,0.00002854817,0.07042811,0.9198817,0.0009194671,0.006348021,0.00003079501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2480726,0.001123562,0.7455007,0.0003607979,0.0001640707,0.0001409313,0.0005848948,0.002018118,0.00203429],"genre_scores_gemma":[0.3636165,0.0008707074,0.6328738,0.000210526,0.00004499643,0.0002713266,0.0004999589,0.0001380647,0.001474096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001078778,"threshold_uncertainty_score":0.003608882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04779214790042804,"score_gpt":0.3406383193489251,"score_spread":0.2928461714484971,"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."}}