427 results about "Individualized treatment" patented technology

The invention relates to the technical field of data processing, and discloses a preoperative risk comprehensive assessment method and system for breast cancer and a storage medium. The method comprises the following steps: collecting clinical information and carrying out digital processing; segmenting the multi-modal image by using deep learning; analyzing a pathological section based on the image features; carrying out multi-level fusion modeling to obtain a risk heat map; performing digital twinborn simulation in combination with a knowledge graph; and dynamically processing the follow-up data to form a risk adjustment mechanism. According to the method, accurate quantification and individualized treatment decision making of the preoperative risk of the breast cancer patient are realized, and the prediction accuracy is improved.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses a prognosis evaluation method and system for II-III stage colorectal cancer patients, and relates to the technical field of wisdom medicines.The prognosis evaluation method comprises the steps that variables related to survival prognosis of the patients are screened, potential variables influencing RFS and OS of the patients are obtained, and prognosis outcomes of the colorectal cancer patients are evaluated by evaluating relevance between various indexes and prognosis outcomes of the colorectal cancer patients and correcting confounding factors; screening out indexes having important value for the model; the screened indexes and machine learning algorithms are utilized to construct RFS and OS prognosis models of the CRC patients, the RFS and OS prognosis models are used for performing prognosis evaluation on the CRC patients in the II-III stages, and the machine learning algorithms comprise six types of LR, RF, XGB, SVC, MLP and GNB. By integrating clinical data and a machine learning technology, an efficient and accurate tool is provided for prognosis evaluation of the II-III stage colorectal cancer patient, and a scientific basis is provided for making clinical decision support and an individualized treatment scheme.

The invention aims to provide an acne precise treatment and drug delivery system based on multi-modal data and feedback control. The system comprises an image acquisition and identification module, which carries out acne focus identification on an obtained skin image based on a deep learning algorithm, and extracts focus feature data; the sensor acquisition module is used for acquiring skin physiological parameters and environmental parameters related to acnes; the multi-modal fusion module carries out fusion processing on the focus characteristic data, the skin physiological parameters and the environmental parameters to generate current acne state parameters; the digital twinborn prediction module constructs a digital twinborn model of the patient according to the current acne state parameters and predicts the development trend of acnes; the individualized treatment decision module formulates an individualized treatment scheme and a corresponding administration dosage according to a prediction result of the digital twinning prediction module; and a set of system capable of realizing multi-source data acquisition, automatic evaluation, prediction analysis and personalized treatment scheme recommendation of the acne of the patient is constructed.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to the technical field of treatment of chronic diseases of old people, and discloses a metabolic characteristic-based medicine dosage optimization method for chronic diseases of old people. The method comprises the following steps: acquiring drug metabolism parameters (including a plasma concentration peak value, a half-life period curve and the like) and organ function data (including a hepatocyte metabolism rate, a glomerular filtration rate and the like) of a plurality of monitoring nodes, arranging the drug metabolism parameters and the organ function data into a time sequence input vector, and extracting a dynamic feature vector by using a time convolution network and an adaptive filter network; performing feature crossing, pharmacokinetic constraint correction and feature enhancement processing, performing fusion to generate a joint feature vector, inputting the joint feature vector into a dose decision model to obtain an adjustment coefficient, and generating a drug dose interval with a safety threshold in combination with a historical drug use record. According to the method, multi-dimensional data integration and dynamic modeling are realized, the accuracy and safety of chronic disease medication of old people are improved, and the method is suitable for individualized treatment.

The invention discloses a cerebral hemorrhage postoperative gastrointestinal hemorrhage prediction method based on an LGBM model. The method comprises the steps of obtaining a multi-dimensional clinical feature sequence of a target patient, screening out a stable feature subset by adopting a Boruta feature selection algorithm, performing nonlinear relation fitting and integrated decision by utilizing a pre-trained LightGBM machine learning model, and generating an individualized ATH risk probability value; and when the risk probability value exceeds a dynamic risk threshold value, triggering a high-risk early warning signal, and based on a Kaplan-Meier survival analysis model, carrying out association mapping on a prognosis track of poor long-term neural function recovery, and finally generating a comprehensive prediction report. According to the invention, accurate quantitative evaluation of ATH risk is realized, clinical intervention timeliness is improved through a dynamic threshold early warning mechanism, short-term complication risk and long-term function prognosis are organically combined, and a comprehensive and reliable prognosis basis is provided for individualized treatment decision.

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 provides a therapeutic drug monitoring kit and method and system, a magnetic bead extraction method is combined with LC-MS / MS detection to be applied to human blood drug concentration monitoring, a mixed magnetic bead solution containing phospholipid removal magnetic beads and to-be-detected drug adsorption magnetic beads is utilized to perform adsorption and extraction pretreatment on a to-be-detected drug in a sample, automatic treatment equipment is matched, and the concentration of the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug is detected. The pretreatment efficiency is high, the phospholipid content in the supernate is low, and the matrix effect and the chromatographic column washing pressure are greatly reduced. According to the therapeutic drug monitoring method provided by the invention, a unified pretreatment process is used, simultaneous extraction of multiple drugs is realized, and through a magnetic bead automatic extraction method, the pretreatment step of a sample can be rapidly completed, a therapeutic drug monitoring result can be rapidly obtained, the overall detection time is shortened, and the detection efficiency is improved. Therefore, a reliable laboratory examination basis is provided for individualized treatment.

The invention discloses a method, a system and equipment for evaluating the treatment effect of CGRP on epilepsy or brain injury caused by epilepsy. It is found for the first time that CGRP inhibits expression of P2RX7 by inhibiting a JAK-STAT1 pathway so as to play a role in treating epilepsy or brain injury caused by epilepsy. On the basis, the invention provides a method, a system and equipment for evaluating the treatment effect of the CGRP on the epilepsy or the brain injury caused by the epilepsy for the field, so that a doctor is assisted in evaluating the treatment effect of a patient suffering from the epilepsy or the brain injury caused by the epilepsy, and a clinician is guided to formulate an individualized treatment scheme for the patient.

The invention discloses a chronic sinusitis CT (Computed Tomography) image automatic typing and severity assessment method and a chronic sinusitis CT image automatic typing and severity assessment system based on deep learning. The method comprises the following steps: acquiring paranasal sinus CT image data of a chronic sinusitis patient and an associated clinical tag; carrying out standardization processing on the CT image, wherein the standardization processing comprises gray level normalization, denoising and region-of-interest segmentation; a multi-task deep learning model is constructed, an improved 3D Re < s> Ne < t-50 > architecture is adopted by a typing sub-model to output an immune endotype classification result, and a severity evaluation sub-model segments a lesion area based on a U-Ne < t + + > structure and automatically calculates a severity level; integrating model output to generate individualized treatment suggestions; and superposing a labeling result through the thermodynamic diagram and embedding the labeling result into a hospital PACS system. Accurate typing and evaluation of chronic nasosinusitis are achieved, and diagnosis efficiency and clinical decision support capability are improved.

The invention relates to the technical field of biological medicine, in particular to a nano-vesicle for targeted brain glioma metabolic immune remodeling and a preparation method and application thereof. According to the nano-vesicle for targeted brain glioma metabolism immune remodeling, double-target metabolism and cascade responsive cleavage site modified biological vesicles are integrated, and effective penetration of a drug blood brain barrier and precise targeting of brain glioma can be achieved. The ROS responsive nanoparticles II are prepared by selecting BSA as a drug carrier, applying a nanoprecipitation method and utilizing SLC1A5, LDHA antagonists and responsive linkers, so that the metabolic double-target inhibitor only responds to specific ROS of tumor cells, and specific release in the tumor cells is realized. The cascade response polypeptide provides a simple and convenient delivery scheme with low invasiveness and application prospects for treatment of brain glioma, and is expected to realize precise and individualized treatment in the field of treatment of malignant glioma.

The invention discloses a rapid progression type nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and relates to the field of medical informatics crossing. The invention provides a rapid progressive nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and aims to solve the problem that a rapid progressive nasopharyngeal carcinoma patient is difficult to recognize in time by depending on TNM staging and experience judgment in the prior art. According to the method, historical case data collection, missing value filling and standardization preprocessing, core feature determination through feature screening, class imbalance correction, feature coding and feature matrix construction are sequentially carried out, an artificial neural network model is trained and optimized under a cross validation framework, and performance and threshold values are determined on a validation set. During clinical application, patient features are input, and the model outputs a rapid progress risk probability and a risk level. Compared with a conventional staging or linear model, the method can improve the prediction accuracy, and achieves the early recognition and individualized treatment of a high-risk patient.

The invention provides a heart failure risk prediction method and system based on multi-modal feature fusion, and the method comprises the steps: firstly carrying out the data feature analysis, carrying out the screening and extraction of data key features, and then building a multi-modal fusion model to provide more comprehensive and accurate medical data analysis. And finally, constructing a patient prognosis evaluation model so as to predict the heart failure risk of the heart failure patient. The heart failure key indexes are automatically screened through data feature analysis, then the multi-modal deep learning model is fused, the heart failure risk of the patient can be accurately predicted in advance, and especially in clinic, early intervention, individualized treatment and resource optimization can be achieved accordingly.

The invention relates to the technical field of medical treatment, and particularly discloses a compound prognosis model for cancer prognosis evaluation, a prognosis evaluation method and a calculator. The compound prognosis model is constructed based on inflammation burden related prognosis indexes ALNCR and TNM by stage combination; the creation method comprises the following steps: step 1, collecting albumin concentration, lymphocyte count, neutrophil count and C-reactive protein concentration data of a cancer patient; 2, calculating an ALNCR index according to a formula; 3, according to a clinical classic TNM staging system, the patients are divided into a TNM staging stage I, a TNM staging stage II, a TNM staging stage III or a TNM staging stage IV; 4, the ALNCR index obtained in the step 2 and the TNM staging result obtained in the step 3 are associated and integrated, and the individualized prognosis score of the ALNCR.TNM composite prognosis model is obtained through calculation. The method is suitable for clinical rapid application; high-risk patients can be accurately distinguished, a quantitative basis is provided for individualized treatment and follow-up visit strategy formulation, and excessive medical treatment or insufficient treatment is reduced.

The invention provides an image-based skin treatment effect evaluation method, medium and system, and belongs to the technical field of skin treatment effect evaluation.A standardized image is collected, a focus area is recognized by applying a skin spectral analysis model, a three-level focus area division model is constructed, and a current boundary and a prediction boundary are calculated; determining a boundary transition index; pixel-level pigment analysis is carried out on the third-level focus area, a pigment distribution matrix is constructed, a pigment concentration representative center is extracted, and pigment distribution skewness is calculated; establishing a time sequence image library for comparison before and after, and calculating a focus area change rate, a boundary transition index change rate and a pigment concentration change rate; combining the indexes to calculate a comprehensive treatment effect score, and generating a thermodynamic diagram to display the treatment progress; a patient individualized evaluation model is established, evaluation precision and an individualized treatment scheme are optimized, and the technical problems that in the prior art, evaluation objectivity of the skin lesion treatment effect is insufficient, and accurate quantification is difficult are solved.

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 relates to the field of biomedical treatment, and particularly discloses application of CD146 in diagnosis and treatment of rhabdomyosarcoma, and the CD146 is specifically and highly expressed in tumor tissues of the rhabdomyosarcoma. The invention provides application of a biomarker for diagnosing rhabdomyosarcoma or / and a detection reagent thereof in preparation of a product for diagnosing rhabdomyosarcoma. Meanwhile, experiments prove that the CD146-targeted chimeric antigen receptor T cell has a relatively strong tumor cell killing effect and can effectively remove CD146 positive tumor cells. The CD146 biomarker provided by the invention provides a new target and theoretical basis for early diagnosis and individualized treatment of rhabdomyosarcoma, and has important clinical application value.

The invention relates to the technical field of biomedicine, in particular to a liver cancer molecular subtype typing and prognosis model construction method based on glycometabolism and lactic acid metabolism related genes and application thereof. According to the invention, the typing and prognosis model of liver cancer subtypes is constructed for the first time on the basis of a coordinated regulation network of a glycometabolism gene and a lactic acid metabolism gene, the constructed liver cancer subtype subtypes comprise Cluster1 and Cluster2, and the total survival rates of different subtypes have significant difference; the clinical characteristics T.stage and TNM.stage of different subtypes are obviously different from each other; 12 kinds of immune cells have significant differences among different subtypes; iPS immune scores of different subtypes are significantly different; the TIDE scores of different subtypes have significant score differences; the genes with the highest mutation frequency in different subtypes comprise TP53, CTNNB1 and TTN. CLM scores of the prognosis model constructed on the basis of the collaborative regulation network of the glycometabolism genes and the lactic acid metabolism genes have no significant difference in different patient states; the 17 immune cells have significant differences between high and low risk groups. And a new way is provided for individualized treatment strategy formulation and survival outcome improvement of HCC.

The invention discloses a topological mapping monitoring and correcting method for abnormal scapular brachial rhythm after breast surgery, and belongs to the field of medical rehabilitation and computer vision. The method comprises the following steps: acquiring a trunk dynamic three-dimensional point cloud and infrared thermal imaging data; constructing a multi-layer collaborative dynamic topology network fusing a skeleton structure, soft tissue deformation and physiological activeness; extracting an intrinsic motion field and comparing the intrinsic motion field with the ideal model to generate deviation measurement; and calculating a dynamic instability index, comparing the dynamic instability index with a threshold value, and triggering optimization energy correction path calculation and instruction output. According to the method, the multi-dimensional topological mapping and dynamics prediction technology is adopted, accurate recognition and feed-forward early warning of motion abnormity under the influence of deep tissue adhesion after the breast surgery can be achieved, rehabilitation of a patient is guided through an energy consumption minimization path, and the accuracy of shoulder joint function remodeling and the individualized treatment effect are improved.

A human chronic myeloid leukemia cell line and the use thereof. The human chronic myeloid leukemia cell line is the first cell line internationally established from chronic-phase leukemia cells of chronic myeloid leukemia, and was named human chronic myeloid leukemia cell YYXY-M6, which was deposited at the China Center for Type Culture Collection (Wuhan University, Wuhan, China) on July 24, 2023, under the deposit number of CCTCC NO: C2023219. The leukemia cell line exhibits primitive cell morphology and has three karyotypes, i.e. t(6:11)(q25:q23), del(11)(q23) and normal karyotype (46, XX); is BCR-ABL gene-negative; has good in-vitro proliferation ability; can be used as cellular material for the study of the mechanism of occurrence and development of the chronic phase of chronic myeloid leukemia, from the chronic phase thereof to the blastic phase thereof, and of BCR-ABL gene-negative chronic myeloid leukemia, and for the in-vitro study of individualized treatment; and can also be used for both in-vitro and in-vivo studies of drug screening and evaluation for the chronic phase of chronic myeloid leukemia, from the chronic phase thereof to the blastic phase thereof, and BCR-ABL gene-negative chronic myeloid leukemia, providing guidance for clinical medication.

The invention relates to the technical field of medical information, and discloses a CRRT treatment intelligent safety early warning system based on multi-modal fusion, and the system comprises a data collection and fusion module which is used for collecting multi-modal data from a plurality of heterogeneous data sources in real time, carrying out the standardization and fusion processing of the multi-modal data, and generating fusion data with a unified time sequence; the knowledge model library is used for storing medical knowledge, clinical guidelines and historical data related to CRRT treatment and trained early warning and decision models; according to the system, through multi-modal data fusion and machine learning, the crossing from passive alarm to active early warning is realized, and the safety of a patient is remarkably improved. The intelligent decision support can output an individualized treatment scheme, promote precise medical treatment and optimize resource utilization, the system integration and grading alarm mechanism effectively reduces the cognitive load of medical care, and the self-learning ability of the system ensures that an early warning model can be continuously evolved and promotes the continuous improvement of clinical treatment quality.

The invention relates to the technical field of biological medicine, in particular to an ulcerative colitis disease clearance evaluation molecular marker and application thereof. By detecting the expression level of the HMGCS2 gene in a sample, the ulcerative colitis disease clearance state and disease activity period can be evaluated, and the method is used for curative effect monitoring, recurrence prediction and individualized treatment strategy formulation of ulcerative colitis and has important clinical application value and market prospect.

The invention relates to a molecular typing model construction and identification method of BRCA mutation breast cancer. The method comprises the following steps: performing unsupervised clustering analysis on a gene with most significant change in a common transcriptome expression profile of BRCA1 / 2 mutant breast cancer to obtain an optimal clustering number, and dividing the optimal clustering number into subtypes with different clinical prognosis and molecular characteristics of the optimal clustering number; based on the clustering result, a random forest algorithm is utilized to construct a molecular typing model, the model comprises a plurality of key classification genes, and the molecular typing model is verified through a verification set and an external data set so as to ensure the accuracy, the stability and the clinical applicability of the molecular typing model. Molecular typing can be rapidly and accurately carried out according to common transcriptome sequencing data of a BRCA1 / 2 mutant breast cancer patient tumor sample, and a scientific basis is provided for selection of an individualized treatment scheme.

The invention provides a brain disease drug curative effect analysis and prediction system and method, and relates to the technical field of drug analysis. Multi-source heterogeneous data such as genetic variation spectrum, neural image topological characteristics, metabolic trajectory vectors and microbiome dynamic distribution matrix are integrated, and cross-modal dynamic fusion is realized through decomposition. The system constructs a drug prediction coupling network based on dynamic causal inference, analyzes overlapped potential intervention nodes of multiple disease mechanisms, and generates a disease-drug-phenotype multi-dimensional mapping library. A short-term drug response toxicity threshold is predicted through a metabolic entropy change model, a long-term neurological function degeneration trajectory is evaluated in combination with epigenetic drift, and an anti-fact strategy gradient is utilized to dynamically optimize a drug administration scheme. According to the invention, accurate fusion and dynamic analysis of multi-modal data are realized, a balance mechanism of curative effect and risk is established, and accurate drug combination sequence and administration strategy support can be provided for individualized treatment of complex brain diseases.

The invention belongs to the technical field of medical data processing, and particularly relates to a bladder cancer vessel tumor embolus assessment method and system based on multi-modal feature fusion, and the optimization method comprises the steps: obtaining and preprocessing the pathological information and image data of a bladder cancer patient, extracting a CT image corresponding to a tumor, and determining a region of interest; image omics features are extracted from the region of interest of the CT image, and deep learning features are extracted from the maximum tumor cross section; carrying out feature selection processing on the obtained radiomics features and deep learning features, and inputting the processed features into a fusion model; and outputting a probability value of'lymphatic vessel invasion 'by the fusion model to obtain an evaluation result. A multi-modal machine learning model is constructed by fusing radiomics features and deep learning features extracted from a CT image, and the objective of the invention is to noninvasively and accurately evaluate the lymphatic vessel infringement state of bladder cancer before an operation, so that a key basis is provided for clinical individualized treatment decision-making.

The invention belongs to the technical field of tumor molecular diagnosis and biological information analysis, and relates to a high-throughput sequencing method and system for monitoring acute lymphocytic leukemia (MRD). Through targeted sequencing with a unique molecular identifier and / or a double-chain tag, error modeling based on a background noise spectrum and statistics / machine learning pseudo variation filtering, ultra-deep accurate detection of IG / TCR cloning and related gene low-frequency variation is realized. And an artificial intelligence recurrence risk prediction model is established by combining a time sequence MRD index, cloning diversity and clinical information, and a structured clinical report is output and docked with LIS / HIS. According to the method, the sensitivity and the specificity of ALL minimal residual disease detection can be remarkably improved, dynamic evaluation on leukemia cloning evolution and recurrence risks is realized, and a reliable basis is provided for individualized treatment decision and long-term follow-up visit.

The invention provides a preparation method of pH / ROS double-response drug-loaded microspheres, a microneedle system and a method for optimizing the microneedle system, and belongs to the technical field of crossing of biomedical engineering and intelligent materials. The preparation method of the pH / ROS double-response drug-loading microspheres comprises the following steps: mixing chitosan, 4-hydroxyphenylboronic acid pinacol ester, polyethylene glycol diacrylate and dialdehyde polyethylene glycol, emulsifying, and carrying out chemical crosslinking, so as to obtain the pH / ROS double-response drug-loading microspheres. According to the pH / ROS dual-response drug-loading microsphere provided by the invention, targeted precise drug release of an inflammation site can be realized; the core-shell structure layered microneedle system has a shell layer-transition layer-core layer three-dimensional structure, so that sequential controlled release can be realized; according to the method for optimizing the microneedle system, reverse optimization of the preparation process can be achieved, and an individualized treatment scheme can be predicted.

The invention provides a dental implant data monitoring analysis method based on deep learning, and the method comprises the steps: collecting the multi-modal structural data of a patient, such as preoperative images, clinical examination and surgical parameters, carrying out the standardized preprocessing, and inputting the data into a deep neural network model with a double-branch structure, thereby achieving the extraction of the implant failure risk score and local causal association strength; on the basis of model output, a dynamic individualized causal map is constructed, key clinical intervention variables serve as regulation and control nodes, anti-fact disturbance simulation is carried out, variable intervention sensitivity is quantified, and a key causal path is analyzed; according to the method, a priority intervention suggestion list and an auxiliary decision report are generated, the causal atlas can be adaptively optimized according to actual follow-up feedback, long-term closed-loop evolution is realized, the accuracy of implant failure risk prediction and the pertinence and operability of an intervention scheme are improved, and clinical individualized treatment decision support is enhanced.