9results about How to "Reduce screening" patented technology

The invention relates to the technical field of biology, and discloses a method for constructing immortalized thymoma primary epithelial cells. The method comprises the following steps: step 1, tissue dissociation and mixed cell population acquisition; step 2, performing differential liquid change to remove immune cells; step 3, immortalization treatment; step 4, carrying out monoclonal screening; step 5, amplification and verification; and step 6, passage and storage. The method is mainly used for establishing a stable immortalized cell line in a laboratory for subsequent scientific research. The specific application comprises tumor biology research; anti-tumor drug screening; cancer-related gene function research; and cell signal transduction mechanism research and the like. For tumors which do not have mature cell lines or are difficult to establish primary cell lines, such as thymoma, the invention is beneficial to improving the success rate of establishing immortalized tumor primary cell lines and promoting the development of subsequent basic scientific research.

The invention discloses a construction method of a tumor-associated meningeal metastasis model, and particularly relates to the technical field of biology. The method comprises the following steps: treating tumor cells according to a cell passage mode and centrifugal parameters, adding a serum-free culture medium, centrifuging again, adjusting the cell density by using a serum-free fresh culture medium, and sub-packaging to obtain a tumor cell suspension; the tumor cell suspension is injected into the L4-L5 intervertebral space of the mouse. After injection, the mouse resuscitates and normally diets; the growth of the tumor is monitored through a small animal living body imaging technology, and the dissected organ is subjected to in-vitro imaging to judge the invasion range of the tumor. The mouse tumor meningeal metastasis model is constructed in a simple, repeatable and efficient manner.

The present invention relates to a cable force monitoring device, comprising: a MEMS acceleration sensor, an acquisition circuit board, a battery and an NB-IoT wireless communication module, wherein the acquisition circuit board comprises a power management system, a signal conditioning conversion element, a threshold monitoring element, a Bluetooth element, a real-time clock element and a microcontroller, the power management system is connected to the signal conditioning conversion element, the threshold monitoring element, the Bluetooth element, the real-time clock element and the microcontroller, the signal conditioning conversion element is connected to the threshold monitoring element, the signal conditioning conversion element, the threshold monitoring element, the Bluetooth element and the real-time clock element are respectively connected to the microcontroller; the MEMS acceleration sensor is connected to the acquisition circuit board, the acquisition circuit board is connected to the NB-IoT wireless communication module, the battery is connected to the acquisition circuit board and supplies power to the MEMS acceleration sensor, the acquisition circuit board and the NB-IoT wireless communication module.

The invention belongs to the technical field of experimental animal models, biomedical research and immunology, and discloses a photocoagulation thymus ischemia animal model, a method and application of the photocoagulation thymus ischemia animal model in rapid simulation of thymus degeneration and immune senescence, and the model shows at least one or more characteristics 1-7 days after induction: thymus atrophy, thymus weight or thymus index is significantly reduced; the cortical-medullary structure of thymus is disordered or disappears; the expression distribution of the Keratin 5 / Keratin 8 is abnormal; thymic cell apoptosis is increased, and lipid deposition is enhanced; the proportion of CD4 + CD8 + double positive T cells is reduced, and the proportion of DN2 / DN3 stage T cells is increased; the initial T cell output is reduced; and the immune function is reduced. The pathological mechanism is clear; the modeling period is short, and the immune senescence phenotype can be stably obtained within several days; the injury focus is controllable, and systemic stress or hormone interference is avoided; the model has high repeatability and is suitable for mechanism research and drug screening.

The present invention discloses a method and system for improving antibody affinity based on molecular dynamics and machine learning, and belongs to the field of computer software technology. The method of the present invention uses molecular dynamics to accurately locate the binding site of the antibody, thereby screening out the best amino acid mutation site, and constructing a variant library of amino acid saturation mutation at the best amino acid mutation site, and calculating the change (value) of the binding free energy of the mutant complex as the evaluation basis; using the amino acid descriptor as a feature and the value as a label, training the machine learning model, and screening out the model with the best prediction performance; the model is used to predict and screen out mutants with lower values ​​(i.e., high affinity potential). The high-affinity 1-7 single-domain antibody obtained by the method of the present invention has a significantly improved affinity of 2.98-5.04 times, which effectively verifies the practicality and efficiency of the present invention and opens up a new path for the directed evolution of single-domain antibodies.

Provided are a HEK293T cell strain having high dispersibility and a screening method therefor. Specifically, the present application relates to a method for screening a HEK293T cell strain suitable for serum-free suspension culture, a cell strain screened by using the method, and a method for producing a viral vector by using the cell strain.

The present application relates to a kind of boron stainless steel sheet for thermal neutron absorption preparation method, comprising the following process steps: (1) induction melting; (2) atomization and deposition; (3) forging; (4) hot rolling; (5) profile processing.The present application is through innovative process route, reasonable melting, plate processing deformation and heat treatment process, significantly improve the microstructure of material, refine hard and brittle boride particles, and then improve the hot workability of material and the mechanical properties of finished plate, meet the higher requirements of boron stainless steel sheet for thermal neutron absorption.According to the process manufactured by the present application, the performance of the plate is similar to powder metallurgy boron stainless steel, also has excellent comprehensive performance, and the process flow is shorter, and the manufacturing cost is also lower.

The present application relates to the field of artificial intelligence and finance, and provides a user behavior prediction method, device, equipment and storage medium. The method screens a target label from a plurality of feature labels obtained, obtains training information and user behavior results of a plurality of training users on the target label, inputs the training information and training text into a plurality of behavior prediction models, obtains the prediction behavior results of the plurality of training users by each behavior prediction model, generates model indicators according to the user behavior results and the prediction behavior results, and screens a target model. In response to a user behavior prediction request, the target model is used to predict and process user information and test text of a test user, and the target behavior result can be accurately obtained. In addition, the present application also relates to blockchain technology, and the target behavior result can be stored in the blockchain.

The application discloses a kind of high-precision prediction method of friction coefficient based on ceramic coating, it is related to ceramic coating friction coefficient prediction technical field, comprising the following steps: step one: obtaining the experimental data and literature auxiliary data of APS spraying ceramic coating;Step two: complete field alignment, training / test set division, missing value processing and training set enhancement;Step three: construct five-level physical feature chain, convert original variable into descriptor with tribological physical meaning;Step four: descriptor input machine learning model for training and prediction;Step five: robustness verification and explainability analysis are carried out, the method constructs five-level physical feature chain, and further maps original material parameter and working condition parameter into physical descriptor related to contact stiffness, pore weakening, bearing capacity and lubrication response, so that model input no longer stays in surface experimental variable, but can more fully characterize tribological mechanism.