5 results about "Weakly positive" patented technology

The invention relates to the technical field of image recognition, in particular to an intelligent interpretation method and system for an immunoassay weak positive sample, and the method comprises the following steps: obtaining original image data of a to-be-detected immunochromatography test strip, extracting a region-of-interest image containing a detection line region and a quality control line region according to the original image data, and constructing an image gray matrix based on the region-of-interest image. According to the method, traditional hard threshold binary judgment is converted into soft classification based on fuzzy logic, weak positive signal gradual change features are described, and the misjudgment rate caused by signal fuzziness is reduced. A spatial constraint mechanism is introduced, isolated noisy points are filtered out by utilizing a pixel spatial topological relation, and the continuity of a detection signal is ensured. Carrying out integral calculation on the detection line area according to the membership degree distribution matrix to obtain a target signal response integral value, and determining an interpretation result by comparing the target signal response integral value with a preset detection limit threshold, so as to improve the low-concentration sample detection rate.

This invention discloses an intelligent typing method and system for M protein in immunofixation electrophoresis images, belonging to the field of immunofixation electrophoresis image analysis technology. This invention establishes a double-blind annotation process and an expert knowledge-driven annotation standard with an embedded annotation verification algorithm based on physical constraints of electrophoretic mobility. Vahadane staining normalization and dynamic ROI extraction techniques are used to standardize the images, and intelligent noise suppression is applied to the image data to improve data quality. Through a hybrid neural network deep learning algorithm, the AI ​​model can perform standardized interpretation of electrophoresis images, reducing the influence of human factors and enabling the model to reproduce the specificity and accuracy of expert-level interpretation. This solves the problems of low accuracy due to strong subjectivity in traditional visual interpretation, such as overlapping bands and weak positives, and the low efficiency of seeking expert consultation for complex and difficult cases.

The application provides an immunochemical luminescence kit for detecting a cancer marker HPV18 E7 protein, the kit comprising biotin-labeled antibody 1 and acridinium ester-labeled antibody 2; the antibody 1 is a rabbit-derived monoclonal antibody, comprising a heavy chain and a light chain; the antibody 1 heavy chain variable region comprises VHCDR1, VHCDR2 and VHCDR3 with the amino acid sequences shown in SEQ ID NO. 1-3; the antibody 1 light chain variable region comprises VLCDR1, VLCDR2 and VLCDR3 with the amino acid sequences shown in SEQ ID NO. 9-11; the antibody 2 is a rabbit-derived monoclonal antibody, comprising a heavy chain and a light chain; the antibody 2 heavy chain variable region comprises VHCDR1, VHCDR2 and VHCDR3 with the amino acid sequences shown in SEQ ID NO. 17-19; and the antibody 2 light chain variable region comprises VLCDR1, VLCDR2 and VLCDR3 with the amino acid sequences shown in SEQ ID NO. 25-27. The application can improve the test sensitivity, improve the detection rate of weak positive samples, realize accurate quantification of HPV18 E7 protein in cervical exfoliated cells, and the detection method of the application is simple in operation, fully automatic in the detection process, and can greatly improve the detection efficiency.

The invention discloses an immunohistochemical interpretation method based on a negative and positive amnion roll multi-target microarray and weak supervised attention AI, and belongs to the field of AI pathological diagnosis. The method is characterized in that standardized contrast is provided through an amniotic roll multi-target microarray, a weak supervised attention U-Net model (only image-level annotation is needed, and the cost is reduced by 93.3%) is adopted to realize cell-level segmentation, and a'contrast-interpretation-calibration 'closed loop is combined to verify and correct deviation. 500 cases of clinical verifications show that the positive accuracy rate of PD-L1 (the clone number is 22C3) is 94.6%, the weak positive recall rate is 89%, the interpretation time of a single piece is 8 seconds, and the cross-laboratory Kappa is equal to 0.92. The system is compatible with high-throughput detection (500 pieces per day), supports docking with a hospital LIS system through an HL7 protocol, solves the problems of large subjective error, contrast deficiency, high AI labeling cost and no dynamic quality control pain point of existing IHC interpretation, and provides an accurate basis for tumor targeted therapy.

The application provides an immunochemiluminescence kit for detecting a cancer marker HPV16 E7 protein, the kit comprising biotin-labeled antibody 1 and acridinium ester-labeled antibody 2; the antibody 1 is a mouse monoclonal antibody, comprising a heavy chain and a light chain; the amino acid sequence of the antibody 1 heavy chain is shown in SEQ ID NO: 17, and the amino acid sequence of the antibody 1 light chain is shown in SEQ ID NO: 18; the antibody 2 is a rabbit-derived monoclonal antibody, comprising a heavy chain and a light chain; the antibody 2 heavy chain variable region comprises VHCDR1, VHCDR2 and VHCDR3 with the amino acid sequences shown in SEQ ID NO. 1-3; and the antibody 2 light chain variable region comprises VLCDR1, VLCDR2 and VLCDR3 with the amino acid sequences shown in SEQ ID NO. 9-11. The application can improve test sensitivity, improve the detection rate of weak positive samples, identify early cervical cancer, realize accurate quantification of HPV16 E7 protein in cervical exfoliated cells, and has the advantages of simple operation, automatic detection process and greatly improved detection efficiency.