42 results about "Drug reaction" patented technology

This disclosure relates to a method of treating a neurological disorder, comprising administering a bupropion, or a combination of a bupropion and a dextromethorphan, to a human being in need thereof, monitoring the human being for Brugada pattern / syndrome, drug reaction with eosinophilia and systemic symptoms, or acute generalized exanthematous pustulosis, and reducing or discontinuing use of the bupropion, or the combination of the bupropion and the dextromethorphan, if the human being develops Brugada pattern / syndrome, subcutaneous tissue disorder, drug reaction with eosinophilia and systemic symptoms (DRESS), or acute generalized exanthematous pustulosis.

The invention provides a risk detection method and system for adverse events of a drug clinical test. The method comprises the following steps: constructing a clinical biological model according to a body health state, body parameters and a historical examination report of a subject; recording the trial information accepted by the subject, and generating a trial database; according to the physiological monitoring equipment and the health monitoring system, body function parameters and biological indexes of the subject after drug testing are monitored in real time, and drug testing data are obtained; screening and optimizing the drug test data based on a genetic algorithm to obtain drug test reaction characteristics; determining a risk score according to the reagent database and the reagent reaction characteristics; and dividing different risk levels according to the risk scores, and carrying out risk early warning according to the risk levels. According to the method, through screening of the drug reaction characteristics and calculation of the risk score, the monitoring efficiency of adverse events is improved, the data analysis capability is enhanced, and the scientificity and accuracy of the result are ensured.

The invention discloses an anti-tumor pharmaceutical composition based on NBDHEX and application of the anti-tumor pharmaceutical composition in treatment of immunosuppressive malignant tumors. The pharmaceutical composition comprises NBDHEX (6-(7-nitro-2, 1, 3-benzoxadiazol-4-ylthio) hexanol) and a PD-1 (programmed death-1) inhibitor, and is characterized in that the NBDHEX (6-(7-nitro-2, 1, 3-benzoxadiazol-4-ylthio) hexanol) and the PD-1 inhibitor are used as raw materials. The NBDHEX can induce tumor cells to generate immunogenic death and promote tumor antigen release, DAMPs signal activation and dendritic cell maturation, so that an immunosuppressive tumor microenvironment is remodeled, and CD8 + T cell infiltration and effector functions are enhanced. The pharmaceutical composition can significantly improve the anti-tumor immune response intensity and the treatment response rate. Animal experiments and cell experiments verify the synergistic anti-tumor effect of the composition in a lymphoma model. The immunosuppressive malignant tumors are suitable for treatment of various immunosuppressive malignant tumors including lymphoma, and are especially suitable for patients with poor single-drug reaction or drug resistance to immune checkpoint inhibitors, and a new way is provided for clinical treatment of tumors.

The invention relates to an antibody or antigen binding fragment thereof which is capable of binding to CD1a. The antibody or antigen binding fragment thereof may be chimeric or humanised, and may be used to treat one or more inflammatory skin or mucosal disorder, or disease or one or more associated systemic disease or disorder, or one or more inflammatory drug reaction which manifests systemically, or a CD1a-expressing malignancy.

The invention relates to the technical field of chromatography, in particular to a preparation method and application of primary hepatic stellate cell membrane biological chromatography for screening anti-hepatic fibrosis active ingredients. The method has the advantages that the primary hepatic stellate cell membrane is used for packing the chromatographic column; the characteristic that primary hepatic stellate cells are closer to in-vivo cells is utilized, the advantage that more real drug response can be provided can be provided, and the method is applied to screening and identification of active ingredients of anti-hepatic fibrosis traditional Chinese medicines. The chromatographic column disclosed by the invention integrates the advantages of primary hepatic stellate cells and a comprehensive two-dimensional cell membrane chromatographic analysis system. Establishment of a comprehensive two-dimensional primary hepatic stellate cell membrane chromatography-reversed phase chromatography system is realized for the first time, and the method can be applied to screening of anti-hepatic fibrosis active components.

The present disclosure provides methods of reducing the risk of developing, and / or severity of, an adverse drug reaction such as drug-induced liver injury (DILI). The methods include identifying patients at risk for developing DILI by determining the presence or absence of one or more HLA alleles in the patients.

The invention relates to CD1a-reactive T-cell receptors (TCRs), and their use in treating or preventing an inflammatory skin or mucosal disorder or disease or one or more associated systemic disease or disorder, one or more inflammatory drug reaction which manifests systemically, a CD1a-expressing malignancy or a malignancy wherein CD1a is altered in expression or function.

The present application relates to the field of medical information technology, in particular to a kind of adverse drug reaction prediction system using skin electronic medical record, the system obtains the skin electronic medical record of a number of historical patients, by analyzing skin electronic medical record, determine the basic correlation degree of each feature and its effective coefficient, further determine the correlation degree of each feature, based on the correlation degree, determine the reference weight of a number of decision trees trained using the skin electronic medical record of historical patients;The skin electronic medical record of the patient to be predicted is input into a number of decision trees, and the output result of the decision tree is weighted using the reference weight, so as to obtain the prediction result of the adverse drug reaction of the patient to be predicted.The present application determines the reference weight of the decision tree by self-adaptation, improves the prediction accuracy of the adverse drug reaction of the patient to be predicted.

The utility model discloses an automatic monitoring diaphragm metering dosing device, which relates to the technical field of environmental protection devices and comprises a bottom plate, a sewage barrel for storing sewage to be treated is arranged at the upper end of the bottom plate, a fixing frame is arranged in the middle of the upper end of the bottom plate, and a diaphragm pump for conveying medicaments is mounted at the upper end of the fixing frame. The output end of the diaphragm pump communicates with the interior of the sewage barrel through a first flow guide pipe, a rotating seat reciprocates in the vertical direction and drives a stirring roller to rotate, chemicals in the chemical conveying barrel are fully mixed, the reaction speed of the chemicals is increased, a scraping ring scrapes away the residual chemicals on the surface of the inner wall of the chemical conveying barrel, and the utilization rate of the chemicals is increased; the upper end of the sensing seat is fixedly connected with the output end of the air pressure rod, the lower end of the sensing seat is rotationally connected with the rotating seat, the sensing seat pushes the rotating seat to move, and the sensing seat is rotationally connected with the rotating seat, so that rotating conditions are provided for rotation of the rotating seat in linear motion, the stirring range is enlarged, and the drug reaction is accelerated.

A biological function estimation device includes a processor configured to predict a drug reaction in a patient based on an evaluation value obtained by evaluating a biological function of the patient from a test result before drug administration and data on a drug to be administered to the patient, and configured to correct the evaluation value according to a comparison result between an obtained prediction value and a measurement value obtained by measuring the drug reaction after the drug administration.

The invention discloses a construction method of a gene mutation library detected by nucleic acid mass spectrometry, which comprises the following steps: searching a gene sequence related to tumor radiotherapeutic toxicity, collecting and processing a sample, extracting and purifying nucleic acid, determining a gene segment related to the radiotherapeutic toxicity, amplifying the gene segment related to the radiotherapeutic toxicity, and constructing the gene mutation library by using the nucleic acid mass spectrometry. A mutation site is introduced into a gene segment related to radiotherapeutic toxicity, a long nucleic acid segment is subjected to fragmentation treatment, a carrier is connected, a connection product is converted and amplified, and library quality is controlled. By constructing the gene mutation library for nucleic acid mass spectrometry detection, gene functions can be systematically studied on the gene level, specific effects of genes in normal physiological and pathological processes are determined, gene variation conditions in disease states are simulated, disease progression is predicted, resources are provided for discovery of drug targets, and the method has a wide application prospect. The medicine curative effect and the medicine reaction mechanism are evaluated, precise medical treatment is achieved, and convenience is provided for malignant tumor treatment work.

This invention discloses a rapid phenotypic drug susceptibility detection method for Mycobacterium tuberculosis infected samples. The method utilizes the incorporation of deuterium into biomolecules synthesized by metabolically active Mycobacterium tuberculosis in a heavy water (D2O) environment, thereby generating C-D characteristic peaks in single-cell Raman spectroscopy. The inhibition of metabolic activity by anti-tuberculosis drugs leads to a decrease in the C-D signal. By calculating the metabolic change rate between drug-treated and untreated samples, the bacterial drug response can be quantified, and susceptibility / resistance determination and MIC value can be established. This invention provides a rapid phenotypic drug susceptibility detection method for Mycobacterium tuberculosis based on single-cell Raman spectroscopy and heavy water labeling technology (Raman-DIP). This method detects the metabolic changes of individual bacteria under the action of anti-tuberculosis drugs to determine drug susceptibility and minimum inhibitory concentration (MIC), enabling a rapid detection process from sample to result.

The invention relates to an antibody or antigen binding fragment thereof which is capable of binding to CD1a. The antibody or antigen binding fragment thereof may be chimeric or humanised, and may be used to treat one or more inflammatory skin or mucosal disorder, or disease or one or more associated systemic disease or disorder, or one or more inflammatory drug reaction which manifests systemically, or a CD1a-expressing malignancy.

The invention provides an intelligent intravenous drug regulation and control system and method, and the system comprises a data collection module which is used for collecting basic parameters and life parameters of a patient; the intelligent decision-making module is used for predicting the medication response of the patient and recommending the medication dosage of the patient based on the basic parameters and the life parameters of the patient; and the execution interaction module is used for automatically generating a doctor's advice based on the patient medication response prediction result and the patient medication dosage recommendation, being linked with a hospital information system and sending an operation prompt to a medical staff terminal. And the aim of improving the individual difference coping ability is fulfilled.

A method for assessing the risk of cutaneous adverse drug reactions (CADRs) caused by an epidermal growth factor receptor inhibitor is provided, wherein the CADRs comprises but not limited to: maculopapular eruption (MPE), erythema multiforme (EM), Stevens Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and drug rash with eosinophilia and systemic symptoms (DRESS). Also provided is a detection kit for assessing the risk of developing CADRs in patients, the kit comprising a reagent for determining specific HLA alleles and a use of the detection kit in assessing the risk of developing CADR in a patient.

1. The name of the design product: colorimetric box. 2. The use of the design product: for drug reaction and result comparison. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: design 1 perspective view. 5. Design 1 is designated as the basic design.

The invention provides a model for predicting the risk of allergic reaction of a Wilson disease patient during copper expelling treatment, and belongs to the crossing field of clinical medicine and biological information technology. The risk prediction model comprises: an input module receiving patient parameter indexes composed of a sodium dimercaptopropanesulfonate injection dosage, a severe adverse drug reaction, a drug allergy history, a neutrophil count, a lymphocyte count, a ratio of neutrophil to lymphocyte, a ratio of platelets to lymphocyte, a leukocyte count, a urine copper level and a platelet count; the processing module is connected with the input module and is used for calculating the score and prediction probability of the allergic reaction during copper expelling treatment based on the parameter indexes of the patient; and the output module is connected with the processing module, and outputs the allergic reaction risk probability obtained by the risk prediction model during the copper expelling treatment of the Wilson disease patient. The risk prediction model can be conveniently used for early recognition and risk prediction of anaphylaxis of a Wilson disease patient during copper expelling treatment.

Methods of detecting a human leukocyte antigen (HLA)-A*32:01 allele in a subject as disclosed are useful, for example, to determine whether the subject is at risk for developing or has vancomycin-induced drug reaction with eosinophilia and systemic symptoms (DRESS). Methods of treating bacterial infection in a subject with vancomycin-induced DRESS are also disclosed.

This disclosure relates to a method of treating a neurological disorder, comprising administering a bupropion, or a combination of a bupropion and a dextromethorphan, to a human being in need thereof, monitoring the human being for Brugada pattern / syndrome, drug reaction with eosinophilia and systemic symptoms, or acute generalized exanthematous pustulosis, and reducing or discontinuing use of the bupropion, or the combination of the bupropion and the dextromethorphan, if the human being develops Brugada pattern / syndrome, subcutaneous tissue disorder, drug reaction with eosinophilia and systemic symptoms (DRESS), or acute generalized exanthematous pustulosis.

The invention relates to a DDI prediction method and system based on multiple view angles and a storage medium. The method comprises the steps that four small-amount sample DDI data sets under hot start and cold start settings are constructed according to drug reaction types; extracting SMILES substructure features and molecular graph substructure features of drugs in four small-amount sample DDI data sets; respectively calculating interaction characteristics between the substructure characteristics of the two drugs from the SMILES-SMILES interaction view angle, the SMILES-molecular graph interaction view angle and the molecular graph-molecular graph interaction view angle; and splicing the interaction features of the three interaction view angles and the Morgan molecular fingerprints of the two drugs, and inputting the spliced interaction features and the Morgan molecular fingerprints into a multi-layer perceptron for fusion to obtain a DDI prediction result. The SMILES modality and the molecular graph modality of the medicine are fused through three interaction perspectives, so that the DDI prediction precision of a DDI data set of a small number of samples is optimized.

The invention belongs to the technical field of biological medicine, and particularly relates to a liver organ model and an establishment method and application thereof. The establishment method comprises the following steps: pre-treating an obtained liver tissue sample to obtain a mixture containing liver tissue-derived cell clusters, and then carrying out gradient digestion treatment, filtration, lysis and centrifugation to obtain bile duct organs; performing induced culture on the bile duct organ to obtain a liver organ model; wherein the gradient digestion method comprises the step of treating the mixture containing the liver tissue-derived cell mass for 5-10 minutes by sequentially using IV type collagenase with the mass concentration of 0.1%, 0.2% and 0.3%. According to the establishment method of the liver organoid model, provided by the invention, the organoid yield is relatively high, and the prepared organoid is clear in boundary and uniform in shape. A fatty liver model constructed by using the aptamer is sensitive to drug response, can screen drugs for treating fatty liver at high throughput, and provides a basis for clinical screening of novel anti-inflammatory drugs.

The application relates to the field of biotechnology, and particularly discloses a preparation method of a mouse triple-negative breast cancer double-tumor model, which comprises the following steps: providing two kinds of triple-negative breast cancer cells; transplanting the two kinds of triple-negative breast cancer cells to two mammary pads of a mouse respectively; feeding the mouse for 7-10 days to obtain a mouse triple-negative breast cancer double-tumor model; wherein the two mammary pads are located on the same side of the mouse and are not adjacent. The application constructs a triple-negative breast cancer double-tumor model by transplanting different tumor cells to the same mouse, eliminates the problem that the drug reaction effect is different due to individual differences of tumors, greatly restores the heterogeneity and complexity of real tumors, and has great application prospect in the research on development characteristics of triple-negative breast cancer and novel treatment methods.

The invention discloses a turbid traditional Chinese medicine antibacterial MIC detection method based on a TTC indicator and a detection kit thereof, and belongs to the technical field of microbiological detection. The method comprises the following steps: screening a TTC optimal working concentration culture medium and an adaptive pore plate which have no bacteriostatic action on target bacteria and can make pore bottom bacteria precipitate and develop clearly; selecting an adaptive pore plate, setting a blank control, a drug-TTC reaction control and a positive control, adding a turbid traditional Chinese medicine liquid, a bacterial liquid of a to-be-detected strain and a TTC optimal working concentration culture medium, and constructing an experiment and control system; culturing at constant temperature; and based on a pore plate precipitation focusing effect, observing the color and precipitation state of an experimental pore in combination with a contrast pore result, and judging an MIC value. The method solves the problem that MIC determination is interfered by turbid traditional Chinese medicine color development, is simple and convenient to operate and accurate in result, can quickly realize accurate detection of turbid traditional Chinese medicine antibacterial MIC, and is suitable for evaluating the antibacterial activity of traditional Chinese medicine.

The present application relates to a kind of drug reaction prediction methods based on multi-source heterogeneous network, comprising: extracting the structural features of drug, constructs drug structure similarity matrix, introduces drug physical and chemical property information and constructs drug physical and chemical property similarity matrix, drug structure similarity, physical and chemical property similarity matrix and synergistic drug matrix are fused to obtain drug similarity fusion matrix, constructs three cell line similarity matrix and adopts nuclear fusion strategy and is fused to obtain cell line similarity fusion matrix;Based on drug similarity fusion matrix, cell line similarity fusion matrix and drug-cell line association matrix, construct heterogeneous network, and extract drug embedding features and cell line embedding features from it, reconstruct drug-cell line association matrix, realize prediction.The present application introduces drug physical and chemical property information and drug synergistic information, enriches the features of drug, solves the problem of highly sparse chemical descriptor and molecular fingerprint feature vector.

The present disclosure in one aspect, relates to compounds, compositions, and methods for degrading pregnane X receptor (PXR) protein. The invention further relates to the use of the disclosed compounds in decreasing adverse drug reactions such as, for example, adverse drug reactions associated with administration of an anticancer agent, an antibacterial agent, a non-steroidal anti-inflammatory agent, or an anticonvulsant agent. The invention, in one aspect, further relates to compounds, compositions, and methods for identifying PXR ligands. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present disclosure.

The invention provides a Chinese patent medicine adverse reaction causal inference method and application based on natural language processing and deep learning fusion, and the method comprises the steps: recognizing adverse drug reaction features in a medical record library through a large language model, and obtaining to-be-processed key features; separately extracting time sequence features and static features in the to-be-processed key features to obtain corresponding time sequence features and static features; the time sequence features and the static features are standardized, and time sequence feature standard data and static feature standard data are obtained; encoding the time sequence characteristic standard data by using a transform encoder to obtain a first input; taking the static characteristic standard data as second input; and simultaneously inputting the first input and the second input to output the causal effect of the adverse drug reaction. According to the method and the device, the learning ability of complex time series data is improved, and the accuracy of adverse drug reaction risk assessment is further improved.

The embodiment of the invention provides a bifunctional micro-fluidic chip for guiding cancer medication, a preparation method and a detection method. The chip comprises a substrate, a cell processing layer and a substance injection layer which are sequentially stacked, the substance injection layer is communicated with the cell treatment layer; the cell processing layer comprises a cell mutation detection module and an organoid analysis module, and the substance injection layer comprises a drug injection module; the cell mutation detection module is used for capturing and detecting the type of injected cancer cells; the drug injection module is used for providing a corresponding anti-cancer drug for the organoid analysis module according to the type of injected cancer cells; and the organoid analysis module is used for culturing the injected cancer cells into organoid and monitoring the medication reaction of the organoid in real time. According to the chip, a feasible solution combining cell EGFR mutation detection and organoid-based drug reaction test is realized, and the chip can be used for guiding personalized lung cancer drug treatment.

The utility model relates to the technical field of tap water treatment, in particular to an on-line automatic chlorine demand measuring device which comprises a reaction water tank, the reaction water tank is used for reaction of raw water and drugs, the reaction water tank is connected with a water quality detector, and the water quality detector is used for detecting residual chlorine data. According to the device and the system, the chlorine demand index of the chlorine disinfection process in the tap water industry is defined, the chlorine demand index of a water body can be quantitatively measured through the device and the system, and accurate quantification of initial feeding of a chlorine disinfectant in the tap water production control process is realized.

The present invention relates to a composition, a combination, and a combination therapy for treating type 2 diabetes mellitus. The composition, combination, and combination therapy effectively reduce blood glucose level by administering 10 mg or 25 mg of empagliflozin to a patient with type 2 diabetes mellitus whose blood glucose level is not adequately controlled by a combination therapy of metformin and sitagliptin, and maintains the controlled blood glucose level for a long period of time with symptoms mostly confirmed to be moderate or lower, and most of the adverse drug reactions are known adverse reactions that have been previously reported in previous clinical trials of clinical trial drugs, and therefore, a new three-drug composition, a combination, and a combination therapy with ensured safety may be provided.

The invention relates to the technical field of intensive care medical treatment, and discloses a vasoactive drug intelligent infusion system based on real-time physiological data. A physiological data acquisition unit of the system acquires real-time physiological data and performs preprocessing. The physiological state classification unit classifies the preprocessed data according to a preset physiological state interval, and extracts multiple types of physiological features to generate a state feature set. The abnormal state marking unit monitors the switching frequency of the physiological state interval and marks the abnormal state interval when the frequency exceeds a threshold value. The drug reaction feature decomposition unit decomposes the feature set of the non-abnormal state interval based on the similarity between the historical drug reaction stage and the current physiological feature to generate a drug reaction feature subset. And the physiological parameter coupling correction unit is used for performing parameter correction on the subset by utilizing the coupling relationship among the physiological parameters. And the medicine infusion decision-making unit reversely traverses and positions potential medicine requirements along the medicine effect path, and accordingly, the infusion priority is determined, and an infusion instruction is generated.