{"id":"W4409317116","doi":"10.1021/acs.jmedchem.5c00071","title":"Development of Galectin-7-Specific Nanobodies: Implications for Immunotherapy and Molecular Imaging in Cancer","year":2025,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Galectins and Cancer Biology","field":"Immunology and Microbiology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"Institute of Cancer Research; Canadian Glycomics Network; Natural Sciences and Engineering Research Council of Canada; Québec Consortium for Drug Discovery; Canadian Institutes of Health Research; Canada Foundation for Innovation; Fonds de Recherche du Québec - Santé","keywords":"Chemistry; Cancer immunotherapy; Immunotherapy; Galectin; Cancer; Molecular imaging; Computational biology; Nanotechnology; Cancer research; Biochemistry; Internal medicine","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":[0.0003586344,0.0003156887,0.0002103984,0.0002351515,0.000126581,0.0004479418,0.0003393079,0.0006093046,0.001338812],"category_scores_gemma":[0.0002185871,0.0001715367,0.0002182223,0.0001527918,0.0002788496,0.0005031722,0.0002494653,0.0006915972,0.000423207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005499439,"about_ca_system_score_gemma":0.0002095488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004205548,"about_ca_topic_score_gemma":0.0006618958,"domain_scores_codex":[0.9998988,0.00002469547,0.000005260175,0.00002049053,0.00003209383,0.00001875984],"domain_scores_gemma":[0.9999082,0.00002368986,0.00001723456,0.00000798768,0.00001993705,0.00002294791],"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":[0.00005032542,0.00001945417,0.0001048303,0.00006443445,0.000004187233,0.00002691661,0.00001123562,0.0002593412,0.9902142,0.0006391418,0.0002975452,0.008308528],"study_design_scores_gemma":[0.00002252192,0.0001943613,0.0007555443,0.00001638147,0.00001040615,0.0002076174,0.00001861936,0.003925539,0.9834999,0.0005233978,0.01081759,0.000008119105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6740384,0.03390573,0.2688975,0.006012242,0.0003470383,0.0004356097,0.0008198731,0.001356662,0.01418707],"genre_scores_gemma":[0.8418424,0.01447948,0.1322833,0.001192501,0.00007313522,0.000280586,0.0009721685,0.0001534449,0.00872304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001338812,"threshold_uncertainty_score":0.004478753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209005919878,"score_gpt":0.3018220372956767,"score_spread":0.2897319780968968,"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."}}