408 results about "Tumor patient" patented technology

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

The invention provides an intelligent tumor nursing monitoring system. The intelligent tumor nursing monitoring system comprises a data acquisition module, a data processing module, a dynamic evaluation module, a multi-stage early warning module and a self-adaptive control module. The data acquisition module acquires physiological sign data, tumor local image data and body fluid biochemical data of a patient in real time through a multi-source biosensor. And the data processing module performs space-time alignment, noise suppression and cross-modal feature association on the multi-modal data to generate a comprehensive feature map. The dynamic evaluation module calculates the tumor progress rate and the complication risk coefficient based on the map, and generates a personalized risk prediction curve. And the multi-stage early warning module triggers an early warning signal according to the curve and generates a risk traceability report. And the adaptive control module generates a personalized scheme based on the early warning signal and the traceability report and controls the medical equipment to execute closed-loop feedback adjustment. The nursing quality and the treatment effect of tumor patients can be improved, and the medical risk is reduced.

The invention provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The invention relates to the technical field of tumble prediction, in particular to a tumor patient tumble prediction system based on machine learning, which comprises a stride frequency recognition module, a physiological fluctuation monitoring module, a gait coupling judgment module, a posture analysis module and a risk output regulation and control module. According to the method, by accurately positioning the wave crests and the wave troughs in the acceleration signal period and analyzing the frequency change direction of the adjacent sections, accurate marking of the mutation sections is achieved, the sensitive capture capacity of behavior state changes is effectively improved, and the synchronous extraction mode of the mutation sections and the physiological signals is improved. Through consistency comparison of attitude angle change and gravity center track direction, accuracy of motion structure abnormity and imbalance state identification is enhanced, pressure distribution and spine compression direction are extracted, corresponding pairing between structure pressure and physiological load is realized, optimization of time sequence integrity is realized, and accuracy of motion structure abnormity and imbalance state identification is improved. And multi-dimensional data fusion analysis has high pertinence and stability.

The invention discloses a tumor evolution trajectory prediction method and system based on image feature learning, and the method comprises the steps: obtaining the whole-process pathological section image data of a target type tumor patient, carrying out the analysis and screening of the image quality, constructing a pathological section screening strategy, and extracting a standard image; extracting tumor cell characteristics based on the standard image, performing grouping analysis on the cell characteristics of different time periods through a clustering algorithm, and determining tumor cell development characteristics; further constructing the cell development characteristics of multiple patients into a heterogeneity propagation network, simulating the tumor evolution process by using a random walk algorithm, and identifying the multi-branch evolution trajectory of the tumor; and finally, establishing a tumor evolution trajectory prediction model to predict the tumor development trend of the current patient. According to the method, the accuracy and interpretability of tumor evolution trajectory modeling can be improved, and reliable support is provided for clinical individualized diagnosis and treatment.

The invention provides a tumor treatment effect prediction method based on multi-modal data, and relates to the field of medical artificial intelligence. The method comprises the following steps: collecting magnetic resonance image data, histopathological section images and clinical baseline data of a tumor patient and a treatment response evaluation record after neoadjuvant chemotherapy; the collected image information is preprocessed; feature extraction is carried out based on the preprocessed image information; screening a feature subset with the highest prediction value from the extracted features; constructing a radiopathomics fusion model by using the feature subset with the highest prediction value; evaluating the radiopathomics fusion model; and predicting the tumor treatment effect by using the evaluated radiopathomics fusion model. The problems that an existing tumor neoadjuvant chemotherapy (NACT) curative effect prediction method is low in accuracy, poor in generalization ability, lack of multi-modal data fusion and insufficient in model interpretability are solved.

The invention relates to the technical field of pathological section image recognition, in particular to a system for achieving precise prognosis prediction of a gastric cancer patient based on deep learning, and aims at achieving precise prognosis prediction of the gastric cancer cell tumor patient on the pathological section level. The implementation package of the system comprises an information acquisition module, an image processing module, a wavelet parallel Mama model module, a result prediction module and an interpretability module, so that the efficiency of image feature extraction and the accuracy of model prediction are greatly improved. The core target of the invention is to solve the problems of insufficient accuracy, poorer result stability, limited model generalization ability, lack of interpretability and the like in the existing gastric cancer prognosis prediction method. Through strict verification and testing, the wavelet parallel Mama model of the research shows excellent performance in the aspect of predicting the total lifetime of gastric cancer patients in multiple centers, provides reliable support for clinical decision-making, and manifests the huge potential of the wavelet parallel Mama model in application in the field of precision medical treatment.

The invention relates to the technical field of intelligent follow-up visit, in particular to an intelligent follow-up visit system and method for daytime tumor patients, a medium and equipment, and the system comprises a physiological monitoring module, a drug interaction analysis module, a side effect mapping module, a health risk early warning module and an intelligent inquiry optimization module. According to the method, through continuous monitoring and real-time comparison of the physiological data of the patient and effective archiving of data fluctuation in a key period, instant and accurate reference is provided for clinical decision making, deep drug interaction analysis enables drug adjustment to be more targeted, unsuitable drug combinations are reduced, and therefore potential health risks of the patient are relieved; through side effect mapping, the system can predict and prevent potential side effects, the treatment safety is guaranteed, the risk early warning module enables the system to identify major health risks in the first time, the response speed of emergency medical events is improved, and the system can be used for improving the treatment efficiency by analyzing the interaction history of patients and optimizing the inquiry process. And the medical experience of the patient is more targeted and practical.

The invention relates to the technical field of tumor prognosis prediction, in particular to a chest tumor prognosis prediction method and system based on data analysis, and the method comprises the following steps: S1, data collection: collecting multi-modal data of a chest tumor patient; s2, data preprocessing: preprocessing the collected multi-modal data; s3, prediction of tumor immune cell infiltration: predicting the infiltration degree of tumor immune cells, and evaluating the influence of the infiltration degree on patient prognosis; and S4, prognosis analysis and prediction: performing comprehensive analysis through a multi-layer perceptron algorithm to generate prognosis prediction results of the tumor patient, including tumor metastasis risk, recurrence probability and immunotherapy response. According to the invention, the complexity of the tumor immune microenvironment can be better captured, so that more accurate prognosis evaluation is provided, and clinical doctors are helped to understand the potential influence of immune response on tumor metastasis, recurrence and immunotherapy response.

The invention provides application of TRMT61A and its inhibitor tetramethylthiuram disulfide in tumor immunotherapy, and relates to the technical field of biological medicine, clinical research proves that RNA m1A methyltransferase TRMT61A is highly expressed in tumor tissues and is related to poor prognosis of tumor patients, and can be used as one of tumor therapy targets. Meanwhile, high expression of the TRMT61A is related to poor prognosis of a tumor patient receiving anti-PD-1 immunotherapy, and after anti-PD-1 antibody immunotherapy is carried out on the patient with low TRMT61A expression level in the tumor of the patient, pathological complete relief is easier. Tetramethylthiuram disulfide inhibits TRMT61A, inhibits m1A modification regulation of TRMT61A on PD-L1 mRNA, increases PD-L1 protein expression, and is combined with a PD-1 antibody drug to significantly enhance the immunotherapy effect of the anti-PD-1 immune checkpoint drug in treating malignant tumors.

The invention provides a lung adenocarcinoma prognostic marker and a model, constructs a lung adenocarcinoma prognostic model based on mRNA and lncRNA related to programmed cell death (PCD), and synthesizes bioinformatics analysis and experiments to explore the biological effect of the lncRNA model in lung adenocarcinoma. The prognosis model covers all PCD pathways and various molecules, has stable prognosis prediction accuracy, and can provide a potential molecular target for personalized treatment. According to the method disclosed by the invention, the PCD-related mRNA and lncRNA are integrated to construct a lung adenocarcinoma diagnosis model suitable for Chinese population, so that the identification capability of tumor patient classification is enhanced from multiple aspects, the accuracy and reliability of prognosis prediction are improved, powerful support can be provided for promoting personalized treatment strategies of lung adenocarcinoma patients, and the method is suitable for popularization and application.

The invention discloses an application of the number of neutrophils in predicting the curative effect of oncolytic viruses in treating tumors. In the application, the neutrophile granulocyte number level in the blood of a tumor patient is closely associated with the tumor treatment effect, and the applicable tumor syndromes are common, including melanoma, colorectal cancer, esophageal cancer, head and neck tumors, gastric cancer, biliary tract system tumors, sarcoma and other tumors. In the application, the critical value of the number of neutrophils for predicting the tumor treatment effect is 2 * 10 < 9 >-5.5 * 10 < 9 > / L, and the most preferable critical value is 4.0 * 10 < 9 > / L; the curative effect of OH2 single-drug treatment on tumor patients with the neutrophil number level below a critical value is better than that of tumor patients with the neutrophil number level above the critical value.

The invention discloses a female tumor comprehensive management system and method based on a female tumor large model, and relates to the technical field of female tumor management, and the system comprises a model training module and a personalized management suggestion generation module. The model training module is used for training a model with a double-engine architecture based on multiple groups of multi-modal data associated with the female tumor to obtain a large female tumor model; and the personalized management suggestion generation module is used for generating personalized management suggestions of the target user by using the female tumor large model when the female tumor related data of the target user is received. According to the female tumor large model, individual differences can be fully considered, so that the generated personalized management suggestions are more targeted. Moreover, the whole system architecture has good expandability and self-learning ability, can continuously optimize the female tumor large model along with continuous input of new data and new knowledge, and provides more accurate personalized management suggestions for female tumor patients.

The present invention discloses a method and system for managing the side effects of medication for tumor patients based on big data analysis, the method comprising the following steps: collecting and preprocessing patient data and drug data, extracting patient characteristics and drug characteristics, wherein the patient data includes genomic data provided by the patient; constructing a prediction model, including a time series analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model, for using drug characteristics and patient characteristics as inputs of the prediction model to predict the risk score of different types of side effects in the patient; inputting the basic information, medication information and genomic data of the current patient into the trained prediction model to obtain a risk assessment of various side effects in the current patient; according to the risk assessment results, selecting a personalized adjustment suggestion suitable for the patient from a preset intervention program library and providing it to the current patient. The present invention can effectively improve the scientificity and accuracy of the management of medication side effects during the treatment stage.

The invention discloses a tumor radiotherapy prognosis prediction simulation method and system based on big data and a medium, and relates to the technical field of tumor radiotherapy prognosis, and the method comprises the following steps: collecting basic physiological information of a tumor patient, and collecting a color image and a temperature image of skin of a radiotherapy part of the tumor patient; skin color feature extraction and skin temperature feature extraction are carried out to obtain skin color feature data and skin temperature feature data; a radiodermatitis prediction model is constructed; performing prediction simulation on the skin of the radiotherapy part of the tumor patient based on the radiodermatitis prediction model, and performing prediction simulation correction to obtain a radiodermatitis prediction result of the tumor patient; the method is used for solving the problem that when an existing tumor radiotherapy prognosis technology is used for predicting and simulating disease development, the risk and time of radiodermatitis of a tumor patient cannot be objectively and accurately predicted and simulated according to physiological characteristics of skin of the tumor patient after radiotherapy.

The invention discloses a malignant tumor acute kidney injury prediction method based on machine learning, and the method comprises the steps: taking a single hospitalization record of a malignant tumor patient as an independent sample, collecting and structurally integrating electronic medical record data, and constructing a standardized sample set through the verification of time integrity and numerical value consistency; under correlation constraints, multi-algorithm joint screening is adopted to determine a key feature set, multiple prediction models are constructed based on the feature set, and a target prediction model is selected through a unified verification process; further constructing an individual risk mapping structure and introducing a feature contribution degree interpretation mechanism to realize interpretability analysis of a prediction result; and performing risk assessment on the patient according to a prediction result, and outputting a corresponding monitoring and intervention strategy, thereby realizing early warning and auxiliary decision support for the malignant tumor related acute kidney injury.

The invention belongs to the field of tumor medicine and the technical field of molecular biology and gene detection, and relates to application of rs17771861 in preparation of a nasopharyngeal carcinoma prognosis prediction marker, and the sequence of the SNP site rs17771861 is as shown in SEQ ID NO.1. The invention also relates to application of the SNP site rs17771861 in preparation of a nasopharyngeal carcinoma prognosis prediction marker. The invention provides an SNP (Single Nucleotide Polymorphism) marker rs17771861 related to nasopharyngeal carcinoma prognosis, application of the SNP marker rs17771861 and a detection kit capable of being used for nasopharyngeal carcinoma prognosis prediction, and the SNP marker rs17771861 is used for assisting in guiding individualized treatment and improving prognosis of tumor patients. After the kit disclosed by the invention is applied to clinical detection, prognosis of a patient can be evaluated before treatment, and a more positive and effective treatment scheme is formulated for the patient with poor prognosis, so that individualized treatment of the patient is realized, and the survival rate is increased. In addition, the kit only needs to detect peripheral blood, and has the characteristics of convenience in sampling, simplicity in operation, high timeliness and the like.

The invention discloses a tumor patient portrait construction method and system based on multi-source heterogeneous data. The method comprises the following steps: collecting heterogeneous data related to a tumor patient; preprocessing the heterogeneous data, including format standardization, missing value completion, time sequence alignment and privacy desensitization processing, to obtain a data set which can be used for unified modeling; respectively extracting disease features, psychological features, behavior features, social features and regional features, and generating corresponding feature vectors for various features; mapping the feature vectors to a unified feature space by using a heterogeneous graph neural network based on an attention mechanism, dynamically calculating contribution degrees of different features to patient portraits, and establishing a multi-dimensional feature association graph of the tumor patients; and generating a structured tumor patient portrait including a disease dimension, a psychological dimension, a behavior dimension, a social dimension and a regional dimension. According to the method, high-precision, multi-dimensional and explainable patient feature description is realized, and a data basis is provided for precise service.

A treatment method, wherein a drug containing a DNA alkylating agent prodrug compound and a salt, ester, solvate, and isotopic isomer thereof and a drug upregulating p53 protein expression or activating p53 function are used in combination for treating cancer and tumor patients. Use of the drug containing the DNA alkylating agent prodrug compound and a salt, ester, solvate, and isotopic isomer thereof in combination with the drug upregulating p53 protein expression or activating p53 function in the preparation of a drug for treating cancers and tumors. A method using the DNA alkylating agent prodrug compound and a salt, ester, solvate, and isotopic isomer thereof in combination with the drug upregulating p53 protein expression or activating p53 function in order to inhibit the growth of cells which are separated from organisms. A pharmaceutical composition containing the DNA alkylating agent prodrug compound and a salt, ester, solvate, and isotopic isomer thereof and the drug upregulating p53 protein expression or activating p53 function; the pharmaceutical composition is configured such that the drug containing the DNA alkylating agent prodrug compound and a salt, ester, solvate, and isotopic isomer thereof and a drug containing a p53-MDM2 inhibitor are used in combination for treating cancer and tumor patients.

The invention relates to an N-acetyl-D-glucosamine composition with an anti-tumor effect and application of the N-acetyl-D-glucosamine composition, and relates to the technical field of medicines and health care products, the N-acetyl-D-glucosamine composition with the anti-tumor effect comprises the following raw materials by weight: 17-29 parts of N-acetyl-D-glucosamine and 5-10 parts of an extract of Tongshan. According to the invention, the N-acetyl-D-glucosamine and the extract in a proper proportion are matched for use for the first time, so that the synergistic interaction of the N-acetyl-D-glucosamine and the extract is realized. In-vitro animal anti-tumor experiments show that the N-acetyl-D-glucosamine composition provided by the invention can significantly reduce the tumor volume in a rat model, provides a new way for clinical treatment of tumor patients, and has a wide application prospect.

The invention provides a tumor patient group psychotherapy method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting demographic and disease course information, psychological scale scores, physiological signals and voice-text-face multi-modal data of a patient; emotional features are jointly recognized through a deep model, psychological needs are evaluated, clustering is carried out in combination with disease course stages, and patients are automatically distributed to homogeneous treatment groups. In the implementation process, the speaking balance degree, the topic dominant rate and the intra-group cohesion are calculated in real time, and if indexes cross the boundary, a guide instruction is generated to adjust discussion. And the system adaptively selects and dynamically adjusts a cognitive behavior therapy according to the emotion and interaction state, accepts a commitment therapy or a positive pressure reduction script until the emotion returns to a safety interval, carries out closed-loop summarization on intervention effect data, and outputs a report containing an emotion trend, interaction quality and an intervention effect. According to the method, real-time accurate evaluation, immediate intervention adjustment and continuous optimization can be realized.

The invention relates to the technical field of tumor vaccines, and provides a novel mRNA (messenger Ribonucleic Acid) tumor vaccine for treating mouse liver cancer, which is prepared by the following steps: S1, specific neoantigen prediction: carrying out bioinformatics analysis on a next-generation sequencing database of tumor patients in the hospital, and selecting tumor specific neoantigens through the following steps; s11, low-quality readings are removed through a filtering step; s12, detecting single nucleotide variation by using a Mutect method and a VarScan method; s13, annotating missense mutation by using ANNOVAR software; in-vivo and in-vitro experiments prove that the vaccine has a remarkable anti-tumor effect, is designed and synthesized aiming at a newly screened new antigen mutation sequence and has high specificity and pertinence, and experimental results show that the vaccine can remarkably improve immune response of specific CTLs cells, induce T cell response of new antigen specificity, and has a good anti-tumor effect in a tumor-bearing mouse model. The vaccine has a remarkable inhibiting effect on tumor growth, and the problems in the prior art are solved through the technical scheme.

The invention relates to the technical field of medical health informatics, and discloses a tumor patient meridian energy dynamic monitoring system and method. The system comprises a midnight-noon ebb-flow time sequence energy mapping module, a multi-modal tumor characteristic signal acquisition module, a tumor focus-meridian correlation map construction module, a chemotherapy toxic reaction prediction module and a moxibustion intervention decision module. Dynamic and accurate monitoring of the meridian energy state of a tumor patient is achieved, potential risks can be early warned 12-24 hours in advance by establishing a chemotherapy toxicity risk scoring algorithm, the life quality of the patient is remarkably improved by generating a personalized moxibustion intervention scheme, and clinical decision support is provided for traditional Chinese and western medicine combined tumor auxiliary diagnosis and treatment.

The invention provides a tumor treatment prognosis evaluation method and system based on machine learning. The method comprises the following steps: S1, analyzing the influence of each immune cell subset of peripheral blood of a patient on tumor treatment responsiveness through a single-factor logistic regression model; s2, based on the total lifetime of the patient, constructing a multi-factor COX risk scale model by using the immune cell subpopulation, and calculating a peripheral blood immune pattern risk score of the patient; and S3, dividing the patients into a high-risk group and a low-risk group according to the median value of the risk score. According to the tumor treatment prognosis evaluation method and system based on machine learning, the peripheral blood immune pattern of the advanced tumor patient at different time points can be evaluated while noninvasive and dynamic material taking is achieved, then the influence of small-dose rhythm chemotherapy on prognosis of the advanced tumor patient is accurately and dynamically predicted, and the method and system are suitable for clinical transformation and popularization and have good application prospects. And a reference value is provided for treatment scheme selection of advanced tumor patients.

The invention relates to the technical field of intelligent medical prediction, in particular to an intelligent early warning method and system for adverse reactions of chemotherapy medication of tumor patients, and the method comprises the steps: inputting gene sequencing data and time sequence physiological index data of a current patient into a network model, and outputting an injury stage by the network model; the training data set of the network model comprises gene sequencing data, time sequence physiological index data and injury stages; the acquisition process of the injury stage comprises the following steps: acquiring the relative metabolic rate of the gene sequencing data to chemotherapeutics, and further determining the relative speed of in-vivo toxicity accumulation of the gene sequencing data; after time sequence physiological index data of meaningless physiological fluctuation of a historical patient is smoothed, a nerve injury change curve is obtained according to the time sequence physiological index data, whether a potential subclinical signal segment exists in the nerve injury change curve or not is recognized, and if yes, the injury stage is judged according to the potential subclinical signal segment. According to the method, individualized and accurate risk prediction can be carried out before irreversible nerve injury caused by chemotherapy occurs.

The invention relates to the technical field of medical monitoring, in particular to an all-dimensional monitoring system and method for tumor patients in the emergency department. Through a multi-modal data acquisition module, equipment such as a flexible patch sensor, a bedside rapid detector, portable ultrasonic equipment and a micro-fluidic chip are utilized; multi-dimensional data such as vital signs, laboratory indexes, image data and blood concentration of a patient are collected in real time. And through noise reduction processing and tumor boundary extraction technologies, high-quality data such as a vital sign time sequence and a tumor volume space matrix are generated. And the multi-modal data fusion module maps the data to different dimensions, constructs a space-time association tensor, performs weighted fusion through an attention mechanism, extracts a space-time feature vector in combination with a convolutional long-short term memory network, realizes comprehensive fusion of information and accurate extraction of key features, and provides high-quality data support for subsequent risk assessment.

The invention relates to a PD-L1 targeted biphenyl radioactive tracer and application thereof, and belongs to the technical field of PET imaging. The invention provides a series of PD-L1 targeted biphenyl radioactive tracers which comprise [18F] LG-2, [18F] LG-3, [18F] LG-13, [18F] LG-14, [18F] LG-15 and the like, the series of biphenyl radioactive tracers have good in-vitro stability, can be specifically combined with PD-L1, and are relatively good in targeting specificity, and therefore, the series of biphenyl radioactive tracers can be applied to the field of radioactive tracers. The series of biphenyl radioactive tracers can noninvasively, accurately, dynamically and systemically monitor the expression level and change of PD-L1 in tumors from the molecular level in real time through PET imaging, and then the curative effect of PD-1 / PD-L1 immunotherapy on tumor patients is effectively evaluated.

The invention discloses a method for promoting renal clear cell carcinoma metastasis by VSIG4 through epithelial-mesenchymal transition, which comprises the following steps: S1, clinical data analysis of ccRCC patients: S1.1, data collection: collecting clinical data of ccRCC patients, including two groups of primary metastatic-free tumor patients and metastatic patients, and observing whether there is statistical difference in expression of VSIG4 on renal carcinoma pathological tissues of the two groups of patients; s1.2, immunohistochemical staining and scoring: performing VSIG4 immunohistochemical staining and scoring on pathological section tissues of the ccRCC patients, grouping according to whether the VSIG4 is highly expressed, and observing whether the patients have disease imaging progression and whether the total lifetime is different; and S1.3, expression difference research: determining the effect of the VSIG4 in invasion and metastasis of the kidney cancer according to the expression difference of the VSIG4 among the kidney cancer, the para-carcinoma and the cancerous pseudoenvelop. According to the application disclosed by the invention, the VSIG4 promotes the metastasis of the renal clear cell carcinoma through epithelial-mesenchymal transition, and a potential means is provided for treating metastatic renal carcinoma by taking the VSIG4 as a target.

The present invention relates to the field of biomedical technology and discloses an artificial intelligence-based anti-tumor drug resistance detection method and system, comprising: collecting genomic data of tumor cells and analyzing genetic changes in tumor cells; extracting tumor molecular markers from tumor cells and constructing a knowledge graph of anti-tumor drug resistance in tumor patients; extracting gene-protein features of tumor cells and identifying the pharmacochemical properties of anti-tumor drugs; fusing gene-protein features and pharmacochemical properties to perform dimensionality reduction processing to construct a drug resistance analysis model for tumor cells; analyzing drug-target interaction relationships in tumor patients and extracting gene-resistance association features of tumor cells to identify the model reliability of the drug resistance analysis model; defining the result interpretation of the drug resistance analysis model, and combining the model reliability and result interpretation to output the anti-tumor drug resistance detection results of tumor patients. The present invention can improve the accuracy of anti-tumor drug resistance detection.

The invention discloses boletus brownii xylan palmitate as well as a preparation method and application thereof. The boletus brownii xylan palmitate is xylan palmitate which is formed by alpha-(1-> 4)-xylose as a main chain, alpha-(1->)-glucose as a branch chain and palmitic acid molecules through ester bonds. The invention further discloses a preparation method of the boletus brownii xylan palmitate and application of the boletus brownii xylan palmitate in the aspect of immunoregulation. The boletus brownii xylan palmitate can be used as a dietary supplement or a food additive for immunodeficiency patients and tumor patients.