{"id":"W4318954096","doi":"10.1109/tmbmc.2023.3240876","title":"Molecular Communication for Quorum Sensing Inspired Cooperative Drug Delivery","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Molecular Biological and Multi-Scale Communications","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Australian Research Council; University of Melbourne","keywords":"Quorum sensing; Drug delivery; Population; Diffusion; Absorption (acoustics); Drug; Molecular communication; Molecule; Chemistry; Biological system; Nanotechnology; Materials science; Chemical physics; Biophysics; Computer science; Pharmacology; Physics; Thermodynamics; Computer network; Biochemistry; Organic chemistry; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003405832,0.0003055339,0.0003078096,0.0002092364,0.0007402612,0.00007313219,0.0007362985,0.0002380457,0.00001161529],"category_scores_gemma":[0.00002018803,0.0002986293,0.0002044314,0.0006232533,0.0003625747,0.00008670689,0.0000422694,0.000505399,0.00005007194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005962848,"about_ca_system_score_gemma":0.00002390041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002356748,"about_ca_topic_score_gemma":0.0002304314,"domain_scores_codex":[0.9983116,0.0004117994,0.0004314544,0.0003191976,0.0001233151,0.0004026468],"domain_scores_gemma":[0.9974348,0.0004194602,0.00006119567,0.001738326,0.0001703641,0.0001758554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001063735,0.0005899423,0.00004198297,0.00006487705,0.0004427039,0.000009981159,0.001187847,0.1379355,0.6911868,0.001091857,0.0006279972,0.1667142],"study_design_scores_gemma":[0.001513642,0.0001147959,0.0002413108,0.000114721,0.00008958142,0.00001221161,0.0004468307,0.9056647,0.0821011,0.0003643147,0.008660839,0.0006759847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1382007,0.002471074,0.8553398,0.001747529,0.0001171466,0.000910786,0.000114011,0.0006939939,0.0004050362],"genre_scores_gemma":[0.9469388,0.007709653,0.04425421,0.0003981687,0.000006085412,0.0002850527,0.0002782319,0.00005381491,0.00007599226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8110855,"threshold_uncertainty_score":0.9999466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0393124705606219,"score_gpt":0.2680439714609928,"score_spread":0.2287315009003709,"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."}}