399 results about "Drugs sensitivity" patented technology

The invention belongs to the technical field of medicines, and particularly relates to application of sesquiterpenoids and / or pharmaceutically acceptable salts thereof in preparation of medicines for treating or preventing infectious diseases caused by staphylococcus aureus. The invention relates to the field of medicines, in particular to application in preparation of medicines for treating or preventing infectious diseases caused by drug-sensitive staphylococcus aureus or drug-resistant staphylococcus aureus. The structure of the sesquiterpenoids is shown as a formula I in the specification. The sesquiterpenoids applied in the invention are derived from rattan chrysanthemum, the source is wide, and the production cost is low. The sesquiterpenoids shown in the formula I have a very good activity inhibition effect on staphylococcus aureus for the first time, and the sesquiterpenoids have a very good activity inhibition effect on staphylococcus aureus with drug sensitivity, drug medium or drug resistance.

The invention relates to the technical field of knowledge maps, in particular to a multi-task hepatitis B drug screening method and system based on knowledge map assistance, and the method comprises the following steps: based on hepatitis B virus genotype sequence data recorded along with time, patient drug use records and drug sensitivity. According to the method, virus genotype sequence data, patient drug use records, drug sensitivity and other multi-dimensional dynamic time sequence data are integrated, interaction events between entities are marked through timestamps, and virus variation tracks, drug curative effect changes and other dynamic characteristics are embedded into atlas node attributes; and the knowledge representation can reflect the time dependence in a real scene. A hyperedge connection multi-entity set is defined based on biological pathway annotation information and protein interaction data, the limitation that only a binary relation is supported in a traditional knowledge graph is expanded, a drug combination and multi-target synergistic effect mechanism is explicitly modeled, and misjudgment of the combination effect caused by simplification of the interaction relation is avoided.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

The invention discloses a spatio-temporal trajectory fusion-based closed-loop monitoring method and system for drug-resistant bacteria in a hospital. The method comprises the following steps: collecting and processing strain drug sensitive test results and patient clinical index data in a hospital information system, and screening out key features through an improved genetic algorithm; wherein the clinical index data of the patient comprises original spatio-temporal trajectory data and nursing operation records; analyzing spatio-temporal trajectory data of the patient according to the key features, combining with drug sensitivity spectrum consistency, and constructing a propagation network model by using a graph convolutional network to identify infection source nodes and corresponding topological influence; and generating an early warning signal based on the topological influence of the infection source node, and continuously optimizing prevention and control measures by dynamically adjusting model parameters and a real-time feedback mechanism. By implementing the method provided by the invention, accurate analysis and real-time prevention and control of a drug-resistant bacterium transmission link can be realized.

The invention discloses a circular RNA drug sensitivity correlation identification method based on integrated multi-instance learning. The circular RNA drug sensitivity correlation identification method comprises the following steps: collecting experimental verification circular RNA and drug sensitivity correlation data; establishing a feature representation model based on a heterogeneous network and vertexes; embedding a heterogeneous graph node into the model, and extracting deep feature representation of the node; designing a meta-path instance embedding projector, and generating a plurality of meta-path instances; a circular RNA and drug sensitivity association predictor is constructed and completed; constructing an integrated heterogeneous graph network deep learning model; and outputting the meta-path instance of the circular RNA and drug pair and the attention coefficient, and carrying out interpretable analysis. According to the method, integrated learning and a deep learning model are combined, so that the reliability of the model is improved; according to the invention, interpretable analysis is carried out by utilizing the meta-path and the attention coefficient, the potential action mechanism of the circular RNA associated with the drug sensitivity can be explained, and the guidance of medical research is facilitated.

According to the device and the reagent for extracting the pathogen, the pathogen product separated from the uncultured sample extracted by using the device and the reagent can be subjected to a drug sensitivity test after simple culture and bacteria collection treatment, so that the culture process is shortened compared with pathogen diagnosis and identification of a clinical infected sample, and a drug sensitivity result can be obtained more quickly; and a more timely diagnosis and treatment reference is provided for clinicians.

The invention discloses application of tannic acid combined triazole medicines in preparation of medicines for treating diseases caused by fungi, and belongs to the technical field of antifungal. A series of drug sensitive tests show that tannic acid lower than 3 [mu] g / ml has no influence on the activity of human vaginal epithelial cells, and after the tannic acid is combined with fluconazole, the tannic acid can significantly inhibit the growth activity of drug-resistant candida (including drug-resistant candida albicans and drug-resistant candida glabrata). In view of the natural property, wide source and low toxicity of tannic acid, the invention provides a new antifungal treatment strategy, and the tannic acid has important clinical application value for treating infection (such as vaginal candidiasis) caused by drug-resistant fungi, especially under the condition that the existing antifungal drugs are poor in effect. Therefore, the tannic acid combined triazole medicine can be used for preparing the medicine for treating diseases caused by fungi.

The invention belongs to the technical field of intelligent medical treatment, and provides a drug recommendation method and system based on feature fusion and graph construction, and the method comprises the steps: carrying out the local feature extraction and global feature extraction of a diagnosis sequence, an operation sequence and a drug sequence, fusing the local feature extraction and the global feature extraction, and carrying out the global feature extraction; obtaining a diagnostic representation, a surgical representation and a drug representation; by fusing local feature extraction and global feature extraction, the health condition of the patient can be represented more comprehensively; according to the electronic health record data set, establishing an electronic health record graph, a drug interaction graph and a drug sensitivity graph, obtaining a drug representation by using the established electronic health record graph, drug interaction graph and drug sensitivity graph and a preset graph attention network, and fusing the drug representation with the drug representation obtained by feature extraction to obtain a drug representation; weight contributions of different medical events in recommendation decisions can be displayed, doctors are helped to understand logic behind recommendation, and the model has remarkable result interpretation ability.

The invention discloses a lung squamous cell carcinoma drug discovery method and system based on disturbance response scanning analysis, and belongs to the field of biological medicines.The method comprises the steps that lung squamous cell carcinoma protein expression profile data is downloaded and preprocessed, a robust network module is constructed and multi-scale analysis and evaluation are conducted, and drug genomic data are obtained based on drug genomic data. Predicting the drug sensitivity of the patient by using machine learning, and generating a DRN through difference analysis of the drug sensitivity; predicting drug-target protein binding affinity in combination with deep learning, quantifying node sensitivity through a PRS technology, calculating a DPI disturbance score and ranking drug priorities; high-ranking drugs are screened through comprehensive sensitivity analysis and literature investigation; a drug for a lung squamous carcinoma cell line is screened out through a pre-experiment and is relocated. According to the framework developed by the invention, by integrating proteomics, pharmacogenomics and dynamic network analysis, systematic analysis is provided for relocation drug identification of lung squamous cell carcinoma, and good news is brought to treatment of lung squamous cell carcinoma.

An ensemble of predictive models that elucidate how cancer mutations impact the response to common replication stress-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK (receptor tyrosine kinase)-JAK-STAT assembly governing resistance. This invention defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

The invention provides a glycolipid metabolism related gene-based endometrial cancer prognosis prediction model construction method, which comprises the following steps of 1, acquiring data containing gene expression and clinical information, and preprocessing the data; 2, differential expression and prognosis gene screening; 3, constructing a prognosis model; 4, analyzing model gene enrichment; 5, evaluating the immunocompetence of the two GLRG related dangerous groups; and step 6, statistical analysis. According to the technical scheme, more accurate and reliable prognosis evaluation is provided for endometrial cancer by comprehensively analyzing multi-dimensional information such as gene expression, immune characteristics, mutation characteristics and drug sensitivity.

The invention provides a small cell lung cancer organoid culture medium and an application method thereof. The small cell lung cancer organoid culture medium is prepared from the following components: 10 to 500 [mu] g / mL of R-spondin-1, 10 to 500 [mu] g / mL of Noggin, 0.01 to 0.2 mg / mL of EGF (Epidermal Growth Factor), 0.05 to 0.2 [mu] g / mL of FGFb (Fibroblast Growth Factor b), 0.1 to 10 [mu] g / mL of FGF10, 0.02 to 0.5 mM of A83-01, 0.5 to 20 mM of Y-27632, 1 to 500 mM of SB202190, 0.1 to 200 mM of Nicotinamide, 0.1 to 500 mM of Insulin, 0.1 to 50 nM of protodioscin, 1 to 100 [mu] All the components are dissolved in a DMEM / F12 culture solution. The small cell lung cancer organoid culture medium provided by the invention is suitable for small cell lung cancer tissue organoid culture after component concentration optimization design, good in stability, high in growth speed and remarkably improved in passage frequency, and can still maintain the same tissue structure and genome characteristics of primary tissues after multiple passages; and an ideal model can be provided for research on the occurrence and development mechanism of small cell lung cancer, detection and research on drug sensitivity and the like.

The invention relates to the technical field of precision medical treatment, in particular to an interpretation system and method for tumor precision medication and a storage medium, and the system comprises a data collection module which is used for collecting gene data, clinical data and drug response data of tumor patients from a plurality of data sources; the data standardization processing module is used for carrying out formatting and standardization processing on the data, so that the data formats are consistent, and effective processing and analysis can be carried out in subsequent modules; the gene data analysis module is used for performing variation analysis according to the gene data of the patient, identifying a gene mutation site and generating a drug sensitivity analysis result; and the drug screening module is used for screening drugs matched with the gene variation sites of the patient from a drug database according to the gene data analysis result, and providing drug dosage recommendation. By adopting the technical scheme of combining a quantum optimization technology and gene data analysis, the technical effect of remarkably improving the drug screening and dose recommendation precision is achieved.

The invention discloses a dual-channel fusion cancer drug response prediction method, and belongs to the technical field of biological information. The method aims at solving the problems that an existing prediction method is only limited to intra-modal feature extraction, and a complex nonlinear cooperative relation between a drug and cancer cells is difficult to capture. Multi-omics data, drug molecular structure information and cancer cell line drug reaction data are integrated from databases such as CCLE, GDSC and PubChem, and unified input features are formed through standardization and feature construction. Introducing a hierarchical double-attention conversion network into the first channel to carry out characterization learning on the multi-modal features of the drug and the cell line, and constructing a high-order attention neighbor interaction graph convolutional network in the second channel to capture graph structure information of cancer drug response. And carrying out adaptive weighting on output results of the two channels by using a PPO-based fusion module so as to realize a dynamic optimal decision. And finally, generating a drug sensitivity prediction result of the cancer cell line through a classification predictor.

The invention provides a single-cell drug sensitivity prediction algorithm based on multi-modal reinforcement learning, and aims to solve the problem that the accuracy of a traditional drug sensitivity prediction method is limited. According to the system, multi-modal bioinformatics data including cell line gene expression data, proteome data, metabolome data, drug reaction data and single-cell RNA sequencing data are integrated, and feature extraction, fusion and selection are performed by using a deep reinforcement learning technology, so that high-precision prediction of single-cell drug sensitivity is realized. The algorithm mainly comprises five core modules: a multi-modal data integration and preprocessing module, a deep reinforcement learning feature extraction and fusion module, a dynamic feature selection and importance evaluation module, a cross-domain migration and adaptive learning module and a deep reinforcement learning prediction model and performance optimization module. The modules work cooperatively, potential features can be learned automatically from multi-modal data, a feature fusion strategy can be optimized, a key feature subset can be selected dynamically, and a model structure and parameters can be adjusted adaptively according to features of target domain data, so that prediction accuracy and stability are improved remarkably. Experimental results show that the algorithm shows significant advantages in multiple evaluation indexes, and compared with a traditional prediction method, the algorithm has higher prediction accuracy and wider applicability. Therefore, the invention provides a novel and efficient drug sensitivity prediction tool for the fields of personalized medical treatment and drug research and development, and has important practical application value.

The invention discloses a high-precision identification method for a culture medium drug sensitive paper sheet based on a mixed model, and the method comprises the steps: S1, image collection and preprocessing: carrying out the standardization processing of a collected culture medium image, including noise removal, illumination correction and contrast enhancement; s2, semantic segmentation model processing: carrying out pixel-level classification on the preprocessed image by adopting a deep learning network architecture, accurately positioning the positions and boundaries of all drug sensitive paper sheets in a culture medium, and generating a segmentation mask; and S3, classification model processing: extracting a paper sheet area based on a result of the step S2, and identifying a drug code and concentration on each paper sheet by using a CNN classification model. And S4, hybrid model fusion: intelligently fusing the data in the steps S2 and S3, and outputting a final drug sensitive paper identification result through confidence weighting and result verification. According to the method, semantic segmentation and classification models are fused, the difficulties of low recognition precision, poor environmental adaptability and the like of a traditional drug sensitive test are overcome, and high-precision automatic recognition of the drug sensitive paper sheets is realized.

The invention provides a method and system for analyzing lung cancer brain metastasis organoid cell subpopulation and functional characteristics, and the method comprises a series of steps: data quality control, batch effect removal, cell grouping, copy number variation inference, quasi-time sequence analysis, function enrichment analysis, and drug sensitivity analysis before and after radiotherapy. Single cell transcriptome sequencing data obtained after organoid culture is subjected to personalized analysis, heterogeneity of tumor cells and sensitivity of the tumor cells to drugs after radiotherapy are described, and the characteristics of all cell subgroups in the lung cancer brain metastasis organoid and the effect of the cell subgroups in the tumor microenvironment can be accurately analyzed; powerful support is provided for mechanism research and precise medicine of lung cancer brain metastasis.

The invention, which relates to the technical field of cloud data diagnosis, discloses a cloud data sharing and diagnosis platform for microbial inspection, comprising a data preprocessing module, a federal modeling module, a multi-modal fusion module, a diagnosis scoring module, a block chain evidence storage module, a trusted sharing module and a dynamic optimization module. The data preprocessing module is used for performing format unification and feature extraction on original microbiological inspection data from a plurality of medical institutions by adopting a standardized coding method, the original microbiological inspection data comprises microscopic images, mass spectrum peak maps, drug sensitivity results and clinical information, and a structured inspection feature set is output; and the federated modeling module is used for carrying out cooperative training on locally deployed initial diagnosis models of all mechanisms by adopting a federated learning method based on differential privacy protection, locally calculating gradient information by utilizing the structured test feature set, and uploading gradient parameters subjected to noise disturbance to the central server for aggregation updating.

The invention provides an organoid drug sensitivity automatic detection method and system for multi-sample parallel processing, and the method comprises the steps: obtaining the optical path length data of each hole site of a cell culture perforated plate, and determining the initial focal plane position of each hole site; matching the deformed feature point coordinates with the initial three-dimensional structure data of the similar organs, and calculating the focal plane offset caused by the deformation of the similar organs through least square fitting; adjusting the focal length of the optical imaging system according to the corrected focal plane position by adopting a fast focusing algorithm, and re-collecting an organ-like image; using a layer-by-layer stacked three-dimensional reconstruction algorithm to perform registration splicing on the recollected image sequence to obtain complete three-dimensional structure data after the organ-like body is deformed; and according to the complete three-dimensional structure data, extracting the volume, surface area and cell distribution density characteristics of the organoid, and generating a morphological characteristic report of the drug sensitivity experiment.

The invention discloses a high-throughput bacterial drug sensitivity test platform based on computer vision. The platform integrates the functions of automatic separation and identification of bacteria, drug sensitivity test and result analysis. Separation and identification of bacteria are realized through a microfluidic technology, and growth conditions of the bacteria under the action of different antibiotics are monitored in real time in combination with a computer vision technology, so that the antibiotic sensitivity of the bacteria is rapidly judged. The platform has the characteristics of high throughput, automation and intellectualization, can complete detection of a large number of samples in a short time, automatically generates a drug sensitivity test report, and provides powerful support for clinical diagnosis and treatment. By implementing the method, the speed and the accuracy of the bacterial drug sensitivity test are remarkably improved, and the method has a wide application prospect.

The invention relates to a model based on multi-modal cross attention and uncertainty integral gradient and application, and relates to the technical field of bioinformatics. The model is obtained based on multi-modal cross attention and uncertainty integral gradient construction, the problems that in an existing drug sensitivity prediction method, multi-modal data fusion is difficult, the model generalization ability is weak, and interpretability is lacked can be solved, the model captures correlation between modals through a cross attention mechanism, and the prediction accuracy of the drug sensitivity is improved. According to the method, uncertainty estimation is realized by combining Monte Carlo Dropout (MC Dropout), feature importance is analyzed by using an integral gradient algorithm, and finally, unification of high-precision prediction and biological interpretability is realized.

The present invention belongs to the technical field of rapid detection of drug sensitivity for bacteria. Disclosed are an enrichment culture medium, and a preparation method therefor and the use thereof. The enrichment culture medium comprises the following raw materials in parts by mass: 15.0-20.0 parts of tryptone, 3.5-5.5 parts of peptone, 1.5-3.5 parts of glucose, 5.0-6.5 parts of yeast extract, 2.0-2.5 parts of dipotassium phosphate, 2.0-3.5 parts of disodium hydrogen phosphate, 1.0-1.5 parts of monopotassium phosphate, 2.5-7.5 parts of sodium chloride, 12.0-18.0 parts of proteose peptone, 15.0-22.0 parts of beef heart infusion powder, 8.0-12.0 parts of soya peptone, 1.0-5.0 parts of 0.1% nicotinamide adenine dinucleotide (NAD), 30-100 parts of newborn calf serum inactivated at 56°C for 30 min, 2.0-4.0 parts of mannitol, 3.0-5.0 parts of sodium pyruvate, 0.23-0.45 parts of cycloheximide, 0.8-2.0 parts of 0.05% bromothymol blue, 2.5-4.5 parts of 0.1% methylene blue, and 1000 parts of distilled water, with the pH value of 6.8-7.5. The enrichment culture medium is used to screen effective therapeutic drugs for bacterial diseases in poultry, and is convenient to use and has a single enrichment effect superior to that of respective selective enrichment broths of target bacteria. Therefore, the culture medium can realize rapid proliferation of common pathogenic bacteria in poultry.

The invention relates to the technical field of medicines, and provides application of ibrutinib as a platinum drug sensitizer in gastric cancer treatment. Furthermore, the invention also provides an application of ibrutinib combined with platinum drugs in preparation of drugs for treating CREB1 high-expression gastric cancer, and further provides a pharmaceutical composition for treating gastric cancer. A mouse in-vivo experiment shows that the size and the growth speed of a stomach tumor overexpressed with CREB1 can be obviously reduced by combined use of ibrutinib and oxaliplatin, so that the platinum drug sensitivity of CREB1 high-expression stomach cancer cells is enhanced; through a gastric cancer organ model and an in-vitro cytology experiment, the ibrutinib can reduce the activity of CREB1 by inhibiting phosphorylation of various proteins of an EGFR-PI3K-AKT-CREB1 pathway, so that proliferation of gastric cancer cells is inhibited, the ferroptosis level of the gastric cancer cells is increased, and IC50 of the ibrutinib can be remarkably reduced by combined use of the ibrutinib and platinum drugs. The invention provides a new basis for the ibrutinib or the derivative thereof to inhibit the growth of CREB1 high-expression gastric cancer cells and enhance the sensitivity of platinum drugs.

According to the trace pathogen enrichment device and the detection method provided by the invention, pathogen products separated from uncultured samples extracted by using the device can be subjected to drug sensitivity test after simple culture and bacteria collection treatment, so that the culture process is shortened compared with pathogen diagnosis and identification of clinical infected samples, a drug sensitivity result can be obtained more quickly, and the detection efficiency is improved. And a more timely diagnosis and treatment reference is provided for clinicians.

The present specification discloses a saturated single nucleotide variant library enabling highly reliable analysis utilizing a quasi-single nucleotide variant (quasi-SNV). Also disclosed is a method for creating the saturated single nucleotide variant library in a high-throughput manner using a prime editor. In addition, disclosed is a method for confirming the effect of the single nucleotide variant on gene function and the effect thereof on drug sensitivity by using the saturated single nucleotide variant library.

The invention belongs to the technical field of biological medicine, and particularly relates to a culture method and application of a hematological malignant tumor organoid. The culture method comprises the following steps: S01, obtaining a tumor cell sample; s02, preparing a bone marrow microenvironment simulation matrix, and resuspending tumor cells; s03, a specific culture medium is added for hematoma organoid culture; s04, performing phenotype identification on the cultured hematological malignant tumor organoid; and hematological malignant tumor organs with phenotypes close to those of bone marrow cells of the patient are screened out. According to the method system disclosed by the invention, the hematoma organoid is successfully cultured and is further subjected to phenotype identification, and the screened hematoma organoid is close to the cellular immune phenotype of a patient and can be used for accurate detection of clinical patient drug sensitivity and effective test of potential effective target drugs.

The application discloses a microbial drug sensitivity detection method. The application comprises: loading a microbial database, establishing a corresponding relationship between secondary microbial names and primary categories; loading a drug breakpoint database based on the "antimicrobial drug sensitivity experiment execution standard"; placing a standard culture dish in an image acquisition device to acquire an image, and calibrating an image analysis system with a vernier caliper; inputting sample information to be detected, acquiring and uploading the image; an image analysis system extracts the position of a drug sensitivity paper sheet, identifies the edge of a bacterial inhibition zone and calculates the diameter; the system extracts corresponding drug breakpoint thresholds based on the microbial category, compares the measured diameter and determines the sensitivity classification (S / I / R); and finally, the result is uploaded to a LIS system. The method can realize the automation, standardization and high efficiency of drug sensitivity detection.

This invention provides a data storage method and system for tuberculosis detection, relating to the field of tuberculosis detection technology. The data storage method for tuberculosis detection includes the following steps: S1. Collecting whole-genome sequencing data of Mycobacterium tuberculosis, host serum IgG titer, and drug sensitivity test results; S2. Calculating genetic distance D based on a reverse evolution model to generate four-dimensional spatiotemporal coordinates (t, x, y); S3. Performing data partitioning and storage based on the drug target barrier value β; S4. Generating dynamic metadata using a host-pathogen dynamics model and compressing and storing it. This invention implements a dynamic storage entropy adjustment algorithm at the hardware and software collaborative level, continuously optimizing the matching efficiency of data compression and physical storage. This results in an intelligent data hub that can perceive the evolutionary pulse of pathogens and autonomously optimize resources, providing support for clinical tuberculosis prevention and control decisions with temporal depth, spatial correlation, and risk evolution.

The invention belongs to the technical field of strain identification and drug sensitivity detection, and discloses a strain identification and drug sensitivity detection kit and a preparation method and application thereof, the kit comprises a drug sensitivity disc, a pre-enrichment chromogenic culture medium, a chromogenic culture medium, sterile water and a diluent; the drug sensitive disc body comprises an enterobacteriaceae area, an enterococcus area, a staphylococcus area, a pseudomonas aeruginosa area and an acinetobacter baumannii area; the enterobacteriaceae area is provided with enterobacteriaceae identification culture medium micropores and drug micropores, the enterococcus area is provided with enterococcus identification culture medium micropores and drug micropores, the staphylococcus area is provided with staphylococcus identification culture medium micropores and drug micropores, and the pseudomonas aeruginosa area is provided with pseudomonas aeruginosa identification culture medium micropores and drug micropores. An acinetobacter baumannii identification culture medium micropore and a medicine micropore are matched in the acinetobacter baumannii area; the problems that an existing drug sensitivity test mode is complex in operation, high in cost and low in accuracy of identification and detection results are solved.

The application discloses a kind of full-automatic drug sensitivity equipment, it is related to the field of drug sensitivity equipment, including shell, full-automatic drug sensitivity equipment further includes incubation device, sample adding device and reading device, incubation device, sample adding device, reading device are all arranged in shell, wherein, reading device is arranged at the first side of incubation device, sample adding device is arranged at the second side of incubation device, first side is adjacent with second side;Incubation device is used to place drug sensitivity plate, and incubation device can rotate along its rotation axis, and drug sensitivity plate is provided with bar code information;Reading device is used to read the bar code information of drug sensitivity plate, and is used to reciprocate along the height direction and first direction of incubation device, first direction intersects with the height direction of incubation device;Sample adding device is used to reciprocate along second direction, second direction intersects with first direction, and second direction intersects with the height direction of incubation device.The application has the advantages of reducing floor space, reducing operation difficulty.