23224 results about "Pharmacy medicine" patented technology

A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches. It scales across distributed computing environments, enabling complex collaborative tasks like materials discovery, product engineering and manufacturing process design, biomedical research, and drug development. The system supports multi-party economic rewards through systematic contribution effort, cost and importance tracking, while standardized interfaces manage security, privacy, and policy constraints across heterogeneous agents.

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The present disclosure provides novel compounds useful as inhibitors of KRAS, in particular KRAS G12D and / or other KRAS G12 mutants, as well as pharmaceutical compositions comprising these compounds and methods of treatment by administration of these compounds or the pharmaceutical compositions.

The invention relates to a drug-disease relationship prediction method and system based on a dual-channel fusion knowledge graph. The method comprises the following steps: S1, constructing a biomedical fusion knowledge graph; s2, constructing a drug-disease sub-graph based on the biomedicine fusion knowledge graph; s3, constructing a dual-channel adaptive fusion feature module, and embedding a drug-disease sub-graph; s4, performing enhanced splicing on the drug-disease sub-graphs in the dual-channel fusion knowledge graph pre-embedded network, importing an edge-node iterative updating learning mechanism, training a spliced sub-graph relation perception learning network, and obtaining enhanced sub-graph feature embedding; and S5, utilizing the trained enhanced subgraph features to embed, calculate and output the prediction probability of the drug-disease relationship. According to the method, the semantic representation capability of the knowledge graph and the topological modeling advantages of the graph neural network are integrated, and efficient and accurate prediction of the drug-disease relationship is realized through multi-modal feature interaction, sub-graph enhancement and a dynamic sub-graph learning mechanism.

The invention relates to an intelligent diagnosis and treatment method and system based on a knowledge graph and a large language model. The method comprises the steps of constructing a basic framework of a medical field knowledge graph based on collected medical field data, constructing multi-level knowledge graph expression, obtaining symptom description of a patient through multiple rounds of dialogues, diagnosing the patient and generating a diagnosis report, rechecking and adjusting the diagnosis report by a professional physician, and finally outputting the diagnosis report. The intelligent diagnosis and treatment system comprises a knowledge graph construction module, an intelligent question and answer system, an entity matching module, an intelligent diagnosis module, a doctor review and diagnosis report adjustment module, a diagnosis report output module and the like. And main diseases are screened, so that the coverage is wider. And three levels of knowledge maps are constructed, so that text matching with finer granularity can be carried out. The non-standardized expression of the patient is firstly converted into standardized medical terms through a large model, and drug allergy and the like are taken into consideration, so that the diagnosis accuracy is improved, and the diagnosis and treatment risk is reduced.

The invention discloses a method for in vitro slow release performance evaluation of a slow and controlled release preparation based on the overflow principle. A container with an upper outlet and a lower outlet is adopted to serve as a release tank, a to-be-detected medicine and a dissolution medium are placed in the release tank, a stirring device is adopted in the release tank to perform stirring, and the dissolution medium is added from the lower outlet of the release tank through a peristaltic pump; the dissolution medium dissolved with the medicine is taken away through the overflow from the upper outlet so as to offset medicine absorption or metabolism, and the slow and controlled release speed of the medicine, the total medicine overflow quantity and the total release amount are calculated by measuring the concentration of the medicine contained in the dissolution medium flowing out, the volume of the release tank and the overflow quantity. According to the method, the releasing degree of the slow and controlled release medicine can be evaluated accurately, the effective medicine release time of the slow and controlled release preparation is evaluated, and slow release performances of the slow and controlled release preparation are evaluated comprehensively.

Set forth are vascular valves and servovalves and methods of using the same that may be positioned in the circulatory system without interrupting blood flow to facilitate solid organ transplantation so as to reduce graft organ anoxia and degradation before and after harvesting and storage, thereby minimizing late-term cardiac allograft vasculopathy as a major cause of graft failure. Along the urinary tract, bypassing bladder-generated urge sensation by diverting urine directly from the ureters to a neobladder or collection bag alleviates intractable incontinence, nocturia, urethral syndrome, overactive bladder, dysuria, or frequency. Where the lower tract is missing, a prosthesis can thwart disease and preclude the degenerative metaplastic transition to carcinoma risked when gut is used to construct a conduit for urine, a neobladder, and / or high-maintenance stoma. Druglines to valves and servovalves can directly pipeline-target medication to nidi, anastomoses, or any other trouble spots.

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

The invention belongs to the technical field of medical resource intelligent management, and relates to a hospital comprehensive monitoring analysis method and platform based on artificial intelligence, and the method comprises the following steps: generating a medicine management basic data set; generating a primary prediction signal; adjusting a prediction weight according to the conflict level and outputting a reliable demand prediction instruction; a quantitative evaluation value reflecting the medicine turnover efficiency, the immediate pressure and the cost loss is calculated, and when the quantitative evaluation value exceeds a preset safety alert threshold value, an inventory abnormity alarm signal is generated; outputting an allocation strategy instruction containing the medicine flow direction, the allocation quantity and the time efficiency; and comparing a deviation value between the reliable demand prediction instruction and the allocation strategy instruction, and when the deviation value continuously exceeds a preset deviation threshold value, triggering an update prompt of the drug safety use limit rule base. The problem that demand prediction and clinical rule verification are separated in a traditional mode is solved.

The invention discloses a liver disease diagnosis and treatment strategy recommendation method based on a knowledge graph, a medium and equipment, and the method comprises the steps: collecting the basic information of a user, constructing a space-time correlated individual liver disease knowledge graph according to the basic information of the user, and enabling the knowledge graph to comprise a disease cause feature node, a pathology grading node, a complication early warning node and a treatment response node which are correlated with each other; inputting the individual hepatopathy knowledge graph into a hierarchical reinforcement learning model for joint reasoning, and outputting multiple groups of strategy options including an antiviral treatment scheme, a liver protection drug combination and a metabolic intervention measure; analyzing a topological propagation path of a complication early warning node through a complication prediction sub-model, and dynamically adjusting an initial candidate diagnosis and treatment strategy set; and updating the individual hepatopathy knowledge graph when obtaining the basic information of the new user. According to the method, multi-dimensional data fusion and dynamic strategy optimization of liver disease diagnosis and treatment are realized, and the accuracy and timeliness of a diagnosis and treatment scheme are remarkably improved.

A recombinant humanized collagen and a preparation method therefor. The recombinant humanized type III collagen can be efficiently and stably expressed in three expression systems of yeast, Escherichia coli, and CHO cells, and has the biological activity superior to that of a commercially available collagen product. The recombinant humanized type III collagen has structural characteristics and functional activity similar to those of a natural collagen, and thus has wide application prospects in biomedical materials, tissue engineering products, cosmetics, foods, health care products or drugs, medical instruments, medical cosmetology and the like.

A computer-implemented system for personalized health optimization constructs a confidence-weighted personal health knowledge graph (PHKG) from heterogeneous data, including wearable sensors, medical devices, lab results, medication logs, and conversational inputs. A multi-stage causal-inference stack identifies modifiable drivers of outcomes using layered methods (e.g., MI, GAM, Neural Granger, DAG-GNN), and simulates candidate interventions. A recommendation engine ranks lifestyle or pharmacologic actions using a benefit-to-friction score, selecting a personalized intervention aligned with user readiness and clinical safety constraints. Interventions may include a minimum effective dose (MED), optimal level, adaptive low-dose, or behavioral challenge. Optional modules include reinforcement learning for timing adaptation and privacy-preserving on-device inference. The system operates across domains including metabolic, cardiovascular, renal, sleep, stress, and medication response, enabling cross-condition synergy evaluation. The architecture is modular, supports runtime plug-in targets, and adapts in real time with or without continuous clinical oversight, depending on deployment.

The invention discloses a drug combination risk prediction method and device based on multi-source feature fusion and comparative learning, equipment and a medium, and relates to the technical field of drug combination prediction. The drug combination risk prediction method comprises the following steps: acquiring a drug data set; and obtaining a feature map according to the drug data set. And according to the feature map, carrying out adaptive feature extraction on the molecular map structure to obtain molecular map features of the medicine. And defining molecular map features as structural features. And inputting the molecular graph features into a multi-layer cascaded graph convolutional network, extracting high-order topological features in a drug network, and obtaining relation features. According to the drug data set, similarity features are calculated, and interactive embedding features are extracted; according to the structural features and the similarity features, potential correlation and differences between the features are mined through a comparative learning mechanism, and structural comparative learning features and similarity comparative learning features are obtained; and performing feature fusion and feature alignment on the features to obtain fused features. And predicting the drug combination risk according to the fusion features.

A database structure obtained by means of information retrieval, and a reasoning system, which structure and system specifically relate to a gut microbe knowledge graph system, comprising: a gut microbe knowledge graph consisting of a gut microbe knowledge base, a gut microbe and small-molecule drug therapy association knowledge base, and a clinical medicine database; and a multimodal uncertainty reasoning system, using the gut microbe knowledge graph. The gut microbe knowledge graph system predicts potential diseases, drugs, genes, etc., which are associated with gut microbes.

The invention relates to the field of medical informatics, in particular to an anesthetic dosage optimization method based on artificial intelligence. Comprising the following steps: collecting real-time physiological data and static data of a patient, converting the real-time physiological data and static data of the patient into standardized feature vectors through standardization processing, and predicting the initial infusion rate of an intraoperative anesthetic by using a long-short-term memory network; collecting and standardizing the intraoperative data of the patient to obtain the standardized intraoperative data; a personalized anesthetic infusion rate is generated through a dynamic metabolism risk and nerve sensitivity joint optimization algorithm; the maximum allowable infusion rate and the minimum effective infusion rate are determined based on a clinical guide, the remaining time of an operation is obtained, safety verification is carried out, and the final infusion rate is generated in combination with the remaining time of the operation. The technical problem that the prediction of the initial infusion rate of the preoperative anesthetic is inaccurate due to the fact that the initial infusion rate is determined by depending on the experience of an anesthetist in the prior art is solved.

A drug supply apparatus that packages a drug with packaging paper that is conveyed from an upstream side to a downstream side, the packaging paper being folded in half with an opening on an upper side, the drug supply apparatus including: a nozzle that dispenses the drug into the packaging paper, the packaging paper including a sealed part, in which the nozzle includes: an insertion guidance piece that protrudes downward and positions a folded part of a lower end of the packaging paper in a state of being inserted in the packaging paper from the opening on the upper side, and a posture adjustment part that guides to inside of the packaging paper the drug, and causes the drug to stand while shifting the drug toward the sealed part side in the packaging paper, the posture adjustment part being provided on the downstream side of the insertion guidance piece.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

An auto-injector for administering a dose of a liquid medicament includes an elongate housing arranged to contain a syringe with a hollow needle and a stopper for sealing the syringe and displacing the medicament, the housing having a distal end and a proximal end with an orifice intended to be applied against an injection site. The syringe is slidably arranged with respect to the housing. A spring capable of, upon activation: pushing the needle from a covered position inside the housing into an advanced position through the orifice and past the proximal end (P), operating the syringe to supply the dose of medicament (M), and retracting the syringe with the needle into the covered position. After delivering the medicament, an activator arranged to lock the spring in a pressurized state prior to manual operation and capable of, upon manual operation, releasing the spring for injection.

The invention relates to a layered drug-loading microneedle patch as well as a preparation method and application thereof. The preparation method of the layered drug-loading microneedle patch comprises the following steps: S1, dissolving a drug and a soluble high-molecular polymer in a solvent according to a mass ratio of (2: 1)-(1: 12) to form a needle tip solution, then filling a needle tip cavity of a microneedle mold with the needle tip solution, drying, and removing redundant drug residues on the surface to form a drug-loading needle tip layer; and S2, filling a mold containing the drug-loading needle tip layer with a photocurable polymer, performing ultraviolet curing for 10-30 seconds under the wavelength of 365-405 nm, and then performing demolding to form a substrate layer, thereby obtaining the layered drug-loading microneedle patch. According to the method, one-time filling of the needle tip part is successfully achieved by combining needle tip auxiliary material proportion optimization, meanwhile, the curing process is rapid, the medicine utilization rate and the process stability are remarkably improved, the medicine stability is good, and the method is suitable for industrial production.

A modular disease management system may provide medication management for a patient. The system may include a base configured to at least partially couple to a tissue site of a patient. The system may further include a medication bladder coupled to the base and configured to maintain medication. The system may further include a medication pump coupled to the medication bladder, comprising a conduit and muscle wire-actuated plungers, wherein the medication pump is configured to cause the medication to flow from the medication bladder through the conduit. The system may further include a cannula insertion device coupled to the medication pump via the conduit, wherein the cannula insertion device comprises a cannula, a spring, and a triggering component, wherein the cannula insertion device is configured to insert the cannula into the tissue site of the patient by actuation of the spring released by the triggering component.

The invention relates to the technical field of medicine supply chain management, in particular to a medicine supply and marketing evaluation management device and system, and the device comprises a data collection module, an intelligent evaluation module, a dynamic adjustment module and a supervision integration module. The intelligent evaluation module is used for generating a medicine supply and demand balance evaluation result and a risk early warning index by adopting a supply and demand prediction model based on machine learning and combining historical supply and marketing data and real-time market demand fluctuation characteristics; and the dynamic adjustment module is used for starting an intelligent scheduling algorithm and generating a dynamic adjustment strategy when the evaluation result shows that the supply and demand imbalance risk value exceeds the preset threshold value. According to the invention, by constructing a full-link dynamic closed-loop management mechanism, the emergency drug prediction period is automatically shortened to realize rapid allocation when a public health accident occurs suddenly, and the problems of information asymmetry and slow emergency response of a traditional supply chain are solved.

A medication container includes: a container body, with an accommodation chamber defined therein for receiving medication, wherein a slot is formed at a rear end of the accommodation chamber; and a lid, including a cover plate and an elastic soft rubber insert, wherein the elastic soft rubber insert is inserted into the slot, and a top end of the elastic soft rubber insert protrudes from the slot and is connected to a rear end of the cover plate, such that the cover plate is pivotally connected to the container body only via the elastic soft rubber insert; wherein the cover plate is engaged with an opening of the accommodation chamber by virtue of bending of the elastic soft rubber insert, and the elastic insert is deformed in response to being pulled by the rear end of the cover plate.

An auto-injector for administering a dose of a liquid medicament includes an elongate housing arranged to contain a syringe with a hollow needle and a stopper for sealing the syringe and displacing the medicament, the housing having a distal end and a proximal end with an orifice intended to be applied against an injection site. The syringe is slidably arranged with respect to the housing. A spring capable of, upon activation: pushing the needle from a covered position inside the housing into an advanced position through the orifice and past the proximal end (P), operating the syringe to supply the dose of medicament (M), and retracting the syringe with the needle into the covered position. After delivering the medicament, an activator arranged to lock the spring in a pressurized state prior to manual operation and capable of, upon manual operation, releasing the spring for injection.

The invention discloses a construction method of a traditional Chinese medicine alert question-answering system based on a large language model. The method comprises the following steps: establishing a traditional Chinese medicine knowledge base; constructing a retrieval enhancement generation model PV-RAG oriented to drug alert, and training and optimizing the PV-RAG model by taking the traditional Chinese medicine knowledge base as a training set; the PV-RAG model comprises a retrieval module, a text extraction module, a query rewriting module and a generation module; and constructing an intelligent question and answer application end, and generating a traditional Chinese medicine alert answer according to a natural language question input by a user. According to the traditional Chinese medicine alert question-answering system based on the large language model and the retrieval enhancement generation technology, deep semantic understanding of traditional Chinese medicine literatures is achieved through the text embedding model, multi-source data such as traditional Chinese medicine specifications, traditional Chinese medicine classics and clinical research literatures are effectively integrated, and the traditional Chinese medicine alert question-answering system is established. And rich knowledge support is provided for traditional Chinese medicine alert questions and answers.

An auto-injector for administering a dose of a liquid medicament includes an elongate housing arranged to contain a syringe with a hollow needle and a stopper for sealing the syringe and displacing the medicament, the housing having a distal end and a proximal end with an orifice intended to be applied against an injection site. The syringe is slidably arranged with respect to the housing. A spring capable of, upon activation: pushing the needle from a covered position inside the housing into an advanced position through the orifice and past the proximal end (P), operating the syringe to supply the dose of medicament (M), and retracting the syringe with the needle into the covered position. After delivering the medicament, an activator arranged to lock the spring in a pressurized state prior to manual operation and capable of, upon manual operation, releasing the spring for injection.

The invention provides a medicine crushing method and device, and relates to the technical field of medicine processing, and the technical scheme is characterized in that a target crushing state is set, and the target crushing state comprises a target particle size distribution range and a target effective component retention rate; acquiring real-time particle size distribution information and process parameter information in the traditional Chinese medicine crushing process, wherein the process parameter information comprises temperature, humidity and pressure in a crushing cavity; calculating the deviation between the current crushing state and the target crushing state according to the real-time particle size distribution information, the process parameter information and the target crushing state; based on the deviation and a preset traditional Chinese medicine characteristic model, a crushing parameter adjustment scheme is generated, and the crushing parameter adjustment scheme comprises adjustment values of the rotating speed of a crushing main machine, the pressure in a crushing cavity and the feeding speed; and according to the crushing parameter adjustment scheme, working parameters of the crushing equipment are automatically adjusted. The medicine smashing method and device have the advantages that the smashing efficiency is improved, and the smashing quality and the effective component retention rate are guaranteed.

An auto-injector for administering a dose of a liquid medicament includes an elongate housing arranged to contain a syringe with a hollow needle and a stopper for sealing the syringe and displacing the medicament, the housing having a distal end and a proximal end with an orifice intended to be applied against an injection site. The syringe is slidably arranged with respect to the housing. A spring capable of, upon activation: pushing the needle from a covered position inside the housing into an advanced position through the orifice and past the proximal end (P), operating the syringe to supply the dose of medicament (M), and retracting the syringe with the needle into the covered position. After delivering the medicament, an activator arranged to lock the spring in a pressurized state prior to manual operation and capable of, upon manual operation, releasing the spring for injection.

The present application relates to the technical field of device control. Disclosed are a control method for an electrotherapy device with an injection function and the electrotherapy device. The method comprises: controlling, via a drive unit, a needle seat to control distal ends of several needle bodies to penetrate a treatment subject to at least one depth; and controlling the several needle bodies to be electrically connected to an electrical energy output assembly to release electrical energy, and / or connected to a liquid storage unit to perform drug injection.

An auto-injector for administering a dose of a liquid medicament includes an elongate housing arranged to contain a syringe with a hollow needle and a stopper for sealing the syringe and displacing the medicament, the housing having a distal end and a proximal end with an orifice intended to be applied against an injection site. The syringe is slidably arranged with respect to the housing. A spring capable of, upon activation: pushing the needle from a covered position inside the housing into an advanced position through the orifice and past the proximal end (P), operating the syringe to supply the dose of medicament (M), and retracting the syringe with the needle into the covered position. After delivering the medicament, an activator arranged to lock the spring in a pressurized state prior to manual operation and capable of, upon manual operation, releasing the spring for injection.