{"id":"W4411964600","doi":"10.1016/j.bios.2025.117743","title":"Ultrasensitive detection of miRNA with single-nucleotide resolution using a high-electron-mobility transistor biosensor combined with splint-ligation","year":2025,"lang":"en","type":"article","venue":"Biosensors and Bioelectronics","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"MD Precision (Canada)","funders":"National Key Research and Development Program of China; Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences; Institute of Biophysics, Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Biosensor; Transistor; Nanotechnology; Materials science; Chemistry; Optoelectronics; Analytical Chemistry (journal); Combinatorial chemistry; Chromatography; Engineering; Electrical engineering; Voltage","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":[0.0004318453,0.0005007703,0.0003000165,0.0002451039,0.0001821935,0.0003796295,0.0005381069,0.0007943329,0.0009747926],"category_scores_gemma":[0.0005418907,0.0003318536,0.0003426544,0.0001917329,0.0003425951,0.0003834802,0.0003651652,0.0006565386,0.0006415436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002743283,"about_ca_system_score_gemma":0.0001956473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001807902,"about_ca_topic_score_gemma":0.0004465039,"domain_scores_codex":[0.9996346,0.00007208644,0.00002474165,0.0001198723,0.0001194694,0.0000290855],"domain_scores_gemma":[0.9997843,0.00009638772,0.00004307853,0.00002428627,0.00002850425,0.00002350353],"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":[0.00002064762,0.000007220558,0.00005441049,0.00001766831,0.000003696037,0.00002642536,0.00001163731,0.00002048086,0.9985819,0.00006413378,0.00003437559,0.001157408],"study_design_scores_gemma":[0.000005356966,0.00008554739,0.0003328526,0.000002210785,0.000006061953,0.000233236,0.000008043337,0.00172862,0.9967731,0.00005905121,0.0007599174,0.000006083834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7741713,0.002318738,0.2142763,0.0008715965,0.000382386,0.0002031902,0.0005812905,0.001811043,0.005384201],"genre_scores_gemma":[0.8520671,0.0008861018,0.1400023,0.0003474499,0.00005121734,0.0001725152,0.0003940601,0.00008840091,0.005990844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009747926,"threshold_uncertainty_score":0.00326103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005932167226950511,"score_gpt":0.2289524314648008,"score_spread":0.2230202642378503,"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."}}