{"id":"W4281853452","doi":"10.1021/acsami.2c05071","title":"Hydrogel Stamping for Rapid, Multiplexed, Point-of-Care Immunostaining of Cells and Tissues","year":2022,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministry of Science and ICT, South Korea; Massachusetts General Hospital","keywords":"Immunostaining; Materials science; Biomedical engineering; Multiplexing; Staining; Stamping; Fluorescence; Pathology; Computer science; Nanotechnology; Immunohistochemistry; Medicine; Optics","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":[0.000143006,0.0001182169,0.0002077032,0.000031017,0.0001089964,0.00001183586,0.0001541718,0.00005102649,0.00001358768],"category_scores_gemma":[0.00001078838,0.0001209965,0.00001655256,0.00003482017,0.00008532358,0.000002811521,0.0003383362,0.00003320538,2.285319e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008413244,"about_ca_system_score_gemma":0.00001474071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001282808,"about_ca_topic_score_gemma":0.000001386144,"domain_scores_codex":[0.9992694,0.00001538435,0.0002777837,0.0002401224,0.00006177748,0.0001355068],"domain_scores_gemma":[0.9994506,0.00002306419,0.0002233482,0.0002333262,0.00005470335,0.00001497607],"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":[0.0002707288,0.00002153078,0.000002547936,0.00009730578,0.0000254474,8.764221e-8,0.00027628,0.00004430809,0.9956424,0.000569715,0.00009487897,0.002954771],"study_design_scores_gemma":[0.0003000785,0.0003620175,0.000002404404,0.00001734826,0.00001593059,0.000002093446,0.002025901,0.000001225938,0.9925726,0.0005129733,0.004062776,0.0001246654],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997405,0.0008040593,0.0006784163,0.00002501556,0.00005791556,0.0005084836,0.0004230386,0.00001961575,0.00007846577],"genre_scores_gemma":[0.9853692,0.0001891262,0.01399936,0.0000336804,0.0000301736,0.0001553521,0.0001733035,0.00002447432,0.00002534664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01332094,"threshold_uncertainty_score":0.4934092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009188262779992965,"score_gpt":0.2609400845429141,"score_spread":0.2517518217629211,"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."}}