{"id":"W4390507365","doi":"10.1039/d3ra06323b","title":"High-throughput light sheet imaging of adult and larval <i>C. elegans</i> Parkinson's disease model using a low-cost optofluidic device and a fluorescent microscope","year":2024,"lang":"en","type":"article","venue":"RSC Advances","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Colleges and Universities; CMC Microsystems","keywords":"Throughput; Fluorescence; Parkinson's disease; Microscope; Fluorescent light; Fluorescence microscope; Larva; Nanotechnology; Biomedical engineering; Materials science; Disease; Computer science; Optics; Biology; Pathology; Medicine; Physics; Ecology","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":[0.0002061102,0.0003531877,0.0003119864,0.0004685985,0.0003616499,0.0003321179,0.0004867832,0.0004099747,0.000839216],"category_scores_gemma":[0.00008168624,0.000215851,0.0003893427,0.0001815925,0.0001618373,0.0002915234,0.0003353172,0.0005939054,0.0003698254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003256785,"about_ca_system_score_gemma":0.0003360145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002004834,"about_ca_topic_score_gemma":0.00482837,"domain_scores_codex":[0.9999013,0.000005827698,0.00000657592,0.00002896483,0.00004201116,0.00001542983],"domain_scores_gemma":[0.9998699,0.00002963741,0.00002835023,0.00001859834,0.00002804748,0.00002544019],"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":[0.00001973886,0.00001189493,0.0001163132,0.00001383793,0.000002647239,0.00002984985,0.000011378,0.0001638456,0.9982029,0.00007582868,0.0001194409,0.001232235],"study_design_scores_gemma":[0.00001067592,0.000090057,0.003920301,0.000007569668,0.00001453159,0.0001688434,0.00002180126,0.009868008,0.9836252,0.0001273623,0.002126214,0.00001944086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8068953,0.0007866044,0.1760135,0.0003190345,0.00007954619,0.0003059757,0.004997407,0.0025286,0.00807407],"genre_scores_gemma":[0.6265976,0.001094917,0.3607163,0.0001771969,0.00004233928,0.0006508388,0.002923293,0.000307193,0.007490206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002004834,"threshold_uncertainty_score":0.003986359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006350945700797188,"score_gpt":0.2476384694587364,"score_spread":0.2412875237579392,"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."}}