http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112986107-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N15-14 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N15-14 |
| filingDate | 2021-02-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-03-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-03-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-112986107-B |
| titleOfInvention | Cell flow type electrical impedance detection method based on asymmetric sinusoidal flow channel |
| abstract | The invention relates to a cell flow type electrical impedance detection method based on an asymmetric sinusoidal flow channel, which adopts a hyaluronic acid solution to adjust the focusing position of a cell to be detected in the flow channel and adopts a multi-frequency mixed alternating current signal to carry out electrical impedance detection on the cell. The cells suspended in the hyaluronic acid solution are subjected to the combined action of the inertia force and the elastic force in the asymmetric sinusoidal flow channel, so that single-row focusing of the cells can be realized in a wider flow rate range, and the impedance detection precision is improved. And applying a multi-frequency alternating current signal to the cell detection area, and processing the current signal to obtain current changes caused by the cells under different frequency signals. Extracting current change signal peak values caused by cells under different frequencies, and training a classification model of cell types by using a machine learning method for cell identification and counting in a cell mixed sample. The invention is suitable for detecting and counting biological particles in various cells in blood and other biological samples. |
| priorityDate | 2021-02-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 15.