{"id":"W4388296996","doi":"10.1101/2023.11.02.565001","title":"Feasibility Study Utilizing NanoString’s Digital Spatial Profiling (DSP) Technology for Characterizing the Immune Microenvironment in Barrett’s Esophagus FFPE Tissues","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal General Hospital","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute","keywords":"Tumor microenvironment; Barrett's esophagus; Immune system; Dysplasia; Esophagus; Stroma; Cancer research; Biology; Multiplex; Pathology; Medicine; Adenocarcinoma; Immunology; Cancer; Bioinformatics; Immunohistochemistry; 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":[0.000294459,0.0001749306,0.00009059983,0.0003399824,0.0001164496,0.0002032362,0.000123847,0.000212482,0.000440013],"category_scores_gemma":[0.0002603666,0.0001231803,0.0001226341,0.0001864466,0.0001367554,0.0002211904,0.0001864859,0.0001261222,0.0001707182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026157,"about_ca_system_score_gemma":0.00009274461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002754085,"about_ca_topic_score_gemma":0.0006567016,"domain_scores_codex":[0.9998949,0.00002233553,0.000006660325,0.00002919733,0.00003356761,0.00001332001],"domain_scores_gemma":[0.9998806,0.00004090876,0.00002193768,0.00001520392,0.00003137987,0.00001006245],"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":[0.00005085407,0.000009426257,0.002305822,0.00001697626,0.000003123814,0.00003688267,0.00001608837,0.0001258644,0.9954448,0.00004125836,0.00001663456,0.001932332],"study_design_scores_gemma":[0.000007569238,0.0003406836,0.04412477,0.00000648379,0.00002196406,0.0005214724,0.0001313905,0.006625629,0.9467801,0.00007228203,0.001359388,0.000008296889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800252,0.0002802868,0.01850701,0.00005958767,0.000008646509,0.00005056571,0.0002626014,0.000060915,0.0007452772],"genre_scores_gemma":[0.9548922,0.0003433507,0.04326128,0.00006031723,0.000007140065,0.00007261318,0.0006053509,0.00001650251,0.0007412614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000440013,"threshold_uncertainty_score":0.001557291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03699005602109938,"score_gpt":0.2936589289294733,"score_spread":0.2566688729083739,"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."}}